Over the past two weeks, the focus across GraphEngine has been on strengthening the foundations that support long-term, scalable growth. This work centred on improving how the platform communicates with users, reinforcing lifecycle reliability, and ensuring the infrastructure beneath GraphEngine can grow efficiently alongside rising usage.

This DevLog covers progress across transactional lifecycle messaging, cost-efficiency initiatives, database optimisation planning, and intelligence-layer enhancements that prepare GraphEngine for its next phase.

1. Transactional Email: Reliable Lifecycle Communication

As GraphEngine evolves into a production-grade intelligence platform, reliable communication around key lifecycle events becomes essential.

During this cycle, we implemented end-to-end transactional email support, enabling the platform to notify users automatically when important events occur. These include subgraph creation confirmations, approvals, suspensions, and other operational changes that directly affect user workflows.

To ensure reliability and security, email addresses are now collected and retained only after verification. A persistent prompt guides users to complete this step, improving profile completeness and ensuring the platform can reliably reach users when action is required.

This establishes a critical communication layer that supports trust, clarity, and smoother user lifecycle management — and lays the groundwork for richer alerts and notifications in the future.

2. Productionising Email Workflows

Following the initial implementation, recent work focused on production readiness. Email logic, templates, and integration have now been hardened and tested across real lifecycle flows. Domain verification is in its final stages, after which transactional messaging will be fully operational in production environments.

With this in place, GraphEngine gains a dependable outbound communication channel that scales naturally as user activity and subgraph usage grow.

3. Platform Efficiency & Cost Discipline

As real usage increases, responsible infrastructure management becomes increasingly important. Over the last two weeks, we conducted a structured review of platform resource usage with the goal of identifying optimisation opportunities that improve efficiency without compromising reliability or performance. Where safe to do so, immediate improvements were implemented, while larger initiatives were documented for phased execution.

Rather than chasing short-term cuts, this work is about ensuring GraphEngine can scale sustainably — keeping operational overhead aligned with actual usage and allowing more resources to be directed toward product development and ecosystem growth.

4. Database Optimisation Planning

As part of this efficiency work, we completed a detailed analysis of database architecture options to support long-term growth. This included evaluating alternative deployment models, migration paths, operational considerations, and resilience strategies. The outcome is a clear, well-documented plan that balances cost efficiency with reliability, data integrity, and operational ownership.

With analysis complete, this work is now ready to move into implementation planning, ensuring GraphEngine’s core data layer remains robust as query volume and intelligence complexity increase.

5. Improving Intelligence Quality: RAG Prompt Analysis

To continue improving the quality of AI-driven responses, we conducted a comprehensive review of existing RAG prompts. This work involved cataloguing current prompts, assessing functionality, identifying data gaps, and prioritising improvements based on impact. The result is a clear roadmap for enhancing prompt performance and expanding the range of questions GraphEngine can answer accurately and consistently.

This is an important step toward deeper, more reliable AI reasoning across wallets, tokens, and on-chain behaviour.

6. New Wallet Balance API

To support richer intelligence workflows, we introduced a new wallet balance API capable of fetching balances for multiple addresses in a single request.

This capability strengthens several areas of the platform:

• Providing missing context for RAG prompts
• Enabling batch portfolio analysis
• Supporting balance-aware notifications and future alerts

By making balance data easier to integrate into reasoning flows, GraphEngine continues to move from raw data access toward fully contextual intelligence.

Closing Thoughts

These updates may not be flashy, but they are foundational.

Reliable lifecycle communication, disciplined infrastructure efficiency, clearer data strategies, and stronger intelligence inputs are what allow GraphEngine to scale confidently as usage grows. This work ensures that as more users, builders, and applications rely on GraphEngine, the platform remains stable, efficient, and trustworthy.

With these foundations in place, the coming weeks will focus on execution — turning planning into impact and continuing to expand GraphEngine’s intelligence capabilities.

The next DevLog will cover progress across database migration execution, prompt enhancements, and the next wave of platform improvements.

Follow us on X and Telegram to be the first to know.

— Team GraphAI

Over the last two weeks, GraphEngine crossed an important threshold in its evolution. We moved beyond purely live intelligence and into a more complete, production-grade system — one that can safely reason over historical data, enforce subscription lifecycle rules, and align operational priorities with real economic signals.

This DevLog covers the work completed across Token Lens backfilling, subscription-aware subgraph lifecycles, paid prioritisation, and flexible enterprise controls, and explains why these changes matter for users, builders, and the long-term health of the platform.

Token Lens Backfilling: Bringing History Into Focus

Until now, Token Lens focused primarily on live, forward-looking intelligence. That worked well for monitoring activity as it happened, but meaningful analysis often requires context — how a token behaved before today, when key events first occurred, and what patterns emerged early on.

This cycle, we shipped a full historical backfilling workflow for Token Lens, enabling users to query recent on-chain history safely before transitioning into live mode.

How It Works (In Plain Terms)

When creating a Token Lens, users can now select a backfilling window of up to seven days. GraphEngine retrieves all relevant historical events within that window and builds a complete dataset before live ingestion begins.

To ensure accuracy, we introduced a new lifecycle state called backfilling_pending. While a subgraph is in this state, it cannot be queried, preventing users from interacting with partial or inconsistent data. Once backfilling completes and the subgraph is approved, it automatically transitions to live mode, where querying is fully enabled.

The result: history first, intelligence second — no half-built views.

Guardrails That Protect Performance and Cost

Historical data is powerful, but it must be handled responsibly.

To preserve system stability and control costs, we enforced strict internal safeguards:

• Backfilling windows are capped at seven days
• An internal event threshold prevents excessive API usage
• Requests exceeding safe limits are automatically rejected

These guardrails ensure that individual workloads don’t degrade performance for the broader system, allowing GraphEngine to scale predictably as usage grows.

UI & Agent Awareness: Making History Explicit

Historical context is only useful if it’s clearly communicated.

The UI now displays:

• The current subgraph state
• Whether historical (backfilled) data is included
• When the transition to live ingestion occurs

At the same time, the Deep Agent was updated to understand when historical data is present. This allows it to reason correctly across time-bounded datasets, ensuring queries reflect both backfilled and live events with proper context.

Testing for Trust: Proving the Data Is Correct

Because historical analysis underpins trust, we invested heavily in validation.

We verified:

• Token launch dates are populated accurately
• Invalid or arbitrary dates are rejected
• Backfilled events remain isolated from live ingestion pipelines

Key query types — such as first activity, largest events within a range, and last actions before a timestamp — were cross-checked against BaseScan and DexScreener to confirm accuracy.

This ensures users can rely on Token Lens not just visually, but analytically.

Subscription-Aware Subgraph Lifecycles

As GraphEngine transitions into sustained usage, access and ingestion must align with subscription status.

We introduced a full subscription-driven lifecycle for subgraphs:

• When a subscription lapses, subgraphs enter a Grace state
  Querying remains enabled and the UI clearly indicates the status.
• After a one-week grace period, subgraphs move to Suspended
  Querying is disabled and data ingestion stops entirely.

If a user renews or upgrades, the system automatically restores ingestion and access — no manual intervention required.

This balances enforcement with fairness and operational clarity.

Paid Prioritisation: Time as a First-Class Resource

Not all requests have the same urgency.

We introduced paid subgraph request prioritisation, allowing users to fast-track requests with a 1–2 day SLA using $GAI.

Users can apply prioritisation during creation, after submission, or directly from the dashboard. Priority requests are clearly marked with a badge, ensuring visibility for both users and internal operations.

This aligns execution speed with economic signal, without compromising system fairness.

More Flexible Backfilling for Advanced Use Cases

While default limits protect the system, advanced users sometimes need more flexibility.

Two extensions were added:

• An optional toggle to ingest live data in parallel with backfilling
• A “Contact Us” flow for extended backfill requests beyond standard limits

These requests are captured and reviewed through controlled processes, enabling enterprise and high-touch use cases without weakening default safeguards.

Closing Thoughts

These updates represent a meaningful step forward in GraphEngine’s maturity.

With safe historical backfilling, explicit lifecycle states, subscription-aware enforcement, and paid prioritisation, GraphEngine is evolving from an experimental intelligence engine into production-ready infrastructure.

We’re no longer limited to answering what’s happening now — we’re enabling users to understand how activity evolved, why it matters, and how intelligence can be delivered sustainably at scale.

As usage grows and the $GAI economy continues to activate, these foundations will support more advanced analytics, richer applications, and deeper integrations across the GraphAI ecosystem.

The next DevLog will build on this progress, exploring usage trends, application-level patterns, and how builders are beginning to leverage these capabilities in the wild.

Follow us on X and Telegram to be the first to know.

— Team GraphAI

Stablecoins are rapidly becoming one of the most transformative forces in global finance. As they rise, they bring an equally significant challenge: making sense of the trillions of dollars in value moving across programmable, transparent payment rails.

This week, GraphAI officially joined the Circle Alliance Program, marking a major step toward becoming the AI intelligence layer for the next era of digital payments, PayFi, and onchain financial infrastructure. This partnership is not just a milestone for GraphAI; it represents a fundamental shift in how enterprises will interact with blockchain data.

This article explores why this matters, what is changing in the world of payments, and how GraphAI’s technology is positioned to deliver deep value across this burgeoning ecosystem.

The Foundation: Who Is Circle?

To understand the magnitude of this alliance, one must first look at Circle’s role in the financial landscape. As the issuer of USDC — the world’s most trusted and regulated digital dollar — Circle is building the foundation for money that moves at the speed of the internet.

USDC has grown beyond a simple store of value into a multi-chain primitive. It powers a vast array of economic activities, from cross-border payments and onchain settlement to merchant acceptance and complex treasury operations. By providing developer APIs for programmable money movement, Circle has essentially created the rails for a global, composable financial system.

The Rise of PayFi and the Data Challenge

Stablecoins have quietly become one of the largest sources of real economic activity on blockchains. With a circulating supply exceeding $200 billion and daily transaction volumes that rival major card networks, they offer what legacy systems cannot: instant global settlement, 24/7 availability, and absolute transparency.

This shift has birthed a new category of financial infrastructure known as PayFi. PayFi represents the intersection of digital currencies, blockchain-based settlement, and onchain financial automation. In this ecosystem, payments are no longer just isolated transactions; they are data-rich financial events that are fully visible and programmable. However, while blockchain offers transparency, raw data is not the same as intelligence.

The Core Challenge: As payment flows scale, enterprises face a massive hurdle. How do they understand, reconcile, and analyze these flows in real-time?

Traditional payment systems hide data in fragmented silos. Blockchain flips this model by recording every transfer, route, and counter-party publicly. Yet, for payment processors, fintechs, and banks, this raw “firehose” of data is overwhelming. They need actionable intelligence — not just a ledger of transactions — and they need it instantly.

Enter GraphAI: The Intelligence Layer

This is where GraphAI steps in. By transforming blockchain activity into a living, queryable knowledge graph, GraphAI turns chaotic onchain data into structured insights. It enables real-time reporting, automated reconciliation, behavior modeling, and deep wallet profiling.

By joining the Circle Alliance Program, GraphAI is positioned to bring these capabilities into the heart of the PayFi ecosystem. The alliance brings together leading infrastructure providers and innovators building the future of digital payments. For GraphAI, this offers a strategic seat at the table to shape open, programmable money alongside enterprise partners migrating to USDC settlement.

Powering Key Alliance Use Cases

GraphAI’s AI-native graph engine directly supports the critical needs of the Circle ecosystem. Rather than offering generic analytics, GraphAI targets specific, high-value enterprise workflows:

• Onchain Business Reporting & Reconciliation: Enterprises settling in digital dollars require real-time visibility into inflows and outflows. GraphAI automatically categorises and matches high-volume transactional data, turning hours of manual accounting into seconds of automated intelligence.
• Treasury & Yield Optimisation: For businesses managing liquidity across chains and protocols, GraphAI analyses routes and flows to guide optimal treasury strategies.
• Risk & Compliance Intelligence: By monitoring velocity, liquidity corridors, and counterparty exposure, GraphAI provides a macro view of stablecoin health. This allows enterprise AI agents to reason over activity with verifiable context, enabling robust compliance analysis and anomaly detection.

Why AI-Native Intelligence is Critical

As payments move onchain, the volume of transactional data becomes massive. Traditional analytics systems simply cannot cope with the complexity of continuous settlement, multi-chain movement, and autonomous agents.

Blockchain-native AI models, however, thrive in this environment because they operate directly over structured, public ledgers. GraphAI’s architecture is built specifically for this world, utilising live ingestion, multi-chain normalisation, and deep natural-language reasoning to make the blockchain understandable.

The Economic Impact: Activating the $GAI Economy

Joining the Circle Alliance has direct economic implications for the GraphAI network. As enterprises seek reporting and flow analysis, the demand for queries is expected to surge.

This expansion into enterprise analytics activates the $GAI credit economy. Since GraphEngine V1 introduced a credit system for queries and subgraphs, every enterprise use case drives demand for credits, directly strengthening the ecosystem. Over the long term, this fosters powerful network effects; as more businesses rely on AI-native analysis of stablecoin flows, GraphAI becomes an embedded, essential infrastructure layer for the global payment stack.

A New Chapter for Digital Money

GraphAI joining the Circle Alliance Program marks a major step toward a future where dollars settle instantly, payments are intelligent, and financial systems are transparent.

We are moving toward a world where AI agents manage flows autonomously and stablecoin analytics become as essential as the payments themselves. GraphAI is ready to power this transformation with real-time intelligence and enterprise-ready analytics.

This is just the beginning.

Follow us on X and Telegram to be the first to know.

— Team GraphAI

Over the past two weeks, the GraphAI platform has undergone significant evolution across three critical dimensions: AI interaction quality, wallet intelligence reliability, and the infrastructure required for monetisation and public-scale usage. This DevLog breaks down the major updates, explaining why they matter and how they prepare GraphEngine for its next chapter as a full-fledged intelligence platform.

A Smarter, Faster, Clearer Chat Experience

The natural-language interface serves as the primary gateway to GraphAI’s intelligence layer. To handle future enterprise workloads, the team focused heavily on refining the complex, multi-step reasoning process that happens behind every chat reply — where agents make tool calls, fetch blockchain data, and produce insights. The goal was to make this process more transparent and intuitive.

Enhanced Transparency and Performance

The user experience has been upgraded in key areas:

• Tool Call Transparency: Users are no longer presented with a static loading bar. Instead, they can watch the agent’s actions in real-time (e.g., “Calling Wallet Flow Analyzer…” or “Querying Subgraph Engine…”). This transforms the agent from a black box into a visible reasoning system, significantly boosting user trust and aiding in the debugging of complex queries.
• Parallel Tool Calls: Agents can now run multiple data queries simultaneously (e.g., retrieving wallet movements, token balances, and contract interactions at once). This parallelization cuts response times significantly, representing a major performance win for a system that often has to stitch together multiple data streams to generate a single insight.
• UI Polish: The chat interface received polish to expand readability and improve the flow of longer reasoning chains, coupled with small quality-of-life additions like enabling multiline messages via Shift+Enter.

These upgrades ensure the chat interface is faster, clearer, and more usable as GraphEngine begins serving high-stakes enterprise workloads.

Wallet Reliability and Data Reconstruction

As users began testing Wallet Lens and analytics at scale, the team tackled important edge cases to ensure data fidelity and consistency. Wallet Lens is only as good as the data behind it, making reliability paramount for traders, analysts, and institutions performing multi-entity analysis.

Fidelity Fixes for Behavioural Analysis

Two critical areas of improvement were addressed:

• Multi-Wallet Connection: The system was fixed to allow users to reliably connect and switch between multiple wallets, a foundational requirement for complex comparative analysis.
• Reconstructing Missing Swap Events: A key discovery was that certain decentralised exchange (DEX) pool interactions didn’t always surface complete swap events, leading to inconsistencies in buy/sell summaries. The fix involved inferring missing swap events using a combination of transaction context:
• From/to addresses
• Token in/out deltas
• Transaction context

This reconstruction logic dramatically improves the accuracy of wallet behaviour summaries, pushing the system much closer to full behavioral fidelity.

Platform Readiness: Scaling Infrastructure and Monetisation

As GraphEngine V1 transitions from an experimental beta to a full intelligence platform, establishing transparent, scalable monetisation and operational controls was essential.

Foundational Infrastructure for Growth

These upgrades lay the foundations for a full SaaS-like experience:

• Credit Management: Free-tier users now receive a predictable and transparent 100 fresh credits every 30 days. Crucially, a new tier structure (Free, Starter, Plus, Pro, Enterprise) has been established to support sustainable platform growth.
• Operational Control: The system now supports a wider selection of LLMs (including Gemini and Claude Sonnet 2.5) directly in the UI, enabling cost-performance optimisation and specialisation. Furthermore, Cost Analytics instrumentation has begun, critical for pricing models and capacity planning by monitoring feature compute consumption and per-user cost profiles.
• Compliance & UX: Preparing for public visibility required implementing a Playground AI Warning Banner (a crucial safeguard for financial data products), dedicated Terms of Service pages, and final UI polish to meet production-grade platform standards.

Allium Backfill Foundations

To support full-chain token tracking and robust historical analytics, the team conducted an in-depth review of the Allium Explorer APIs. This analysis produced a detailed requirements document mapping out optimal batch requests, unit consumption, and the necessary data models to reconstruct long-term token history. High-quality backfill is necessary because real intelligence requires both history and real-time context.

The Biggest Update: Subscription and Payment Overhaul

The largest engineering push of the cycle transformed GraphAI from a free beta into a fully operational, revenue-ready platform.

The core shift involved moving away from a credits-only system for core functions:

• Subscription Tiers & Quotas: Subgraphs no longer consume credits. Instead, the Plus, Pro, and Enterprise tiers grant fixed quotas for subgraphs alongside specific credit allowances. This simplifies expectations for power users.
• Secure On-Chain Payment Pipeline: A robust, auditable, and fraud-resistant payment flow is now active. The backend verifies the token, amount, sender address, and transaction confirmation to prevent replay/spoofing, automatically activating the user’s plan and refreshing their quotas.
• Flexible Billing & Enterprise Flow: The platform supports monthly, quarterly, and annual billing cycles. A structured enterprise contact flow was also implemented to handle custom allocations, dedicated support, and custom billing plans for high-touch customers.

This update signifies that GraphAI is evolving from a developer tool into enterprise-grade intelligence infrastructure with the monetisation mechanics to match.

Closing Thoughts

These two weeks have accelerated GraphAI across every layer of the stack, resulting in: a smarter chat interface, more reliable wallet intelligence, and the foundations for sustainable monetisation. The pace of development continues to increase as GraphEngine V1 enters the hands of real users and enterprise partners. These improvements lay the groundwork for the next wave: higher query volumes, robust credit consumption, enterprise adoption, and deeper AI-native intelligence across the blockchain.

The next DevLog will cover backfill implementation progress, Wallet Lens enhancements, and early usage analytics from GraphEngine V1’s first month.

Follow us on X and Telegram to be the first to know.

— Team GraphAI

From Experimental Beta to an Actual Economy

During Closed Beta, GraphEngine was intentionally free to use.

We wanted builders to experiment, break things, and push the system hard without thinking about costs. That period was about discovering what mattered most: which Lenses were useful, what kinds of questions people asked, how subgraphs behaved under real load, and what AI needed from on-chain data to answer reliably.

But a free Beta is not the end-state.

If GraphEngine is going to be the AI-native data layer for Web3, it needs two things:

1. A scalable architecture that can support real-world usage across chains.
2. A sustainable economic model where usage, not hype, drives value.

GraphEngine V1 is where that shift happens. It is not just a more polished version of Beta; it is the point where queries, subgraphs, and intelligence become part of a live revenue system, directly tied to the $GAI token.

Credits: The Core Unit of the GraphEngine Revenue System

At the heart of V1 is a simple but powerful idea: every meaningful unit of work inside GraphEngine is paid for in credits.

Credits are the internal “energy” of the system. They are what you spend when you ask GraphEngine to do something computationally valuable.

In V1, credits are required for three main activities:

• Creating subgraphs — turning raw on-chain feeds into structured, AI-ready data streams.
• Maintaining subgraphs — keeping those data streams indexed, fresh, and continuously ingested.
• Querying subgraphs — whether via dashboards, chat, Lenses, or MCP-powered agents.

Crucially, these credits are only acquired using $GAI.

Users, teams, and agents who want to build on GraphEngine will buy $GAI and convert it into credits. That immediately connects platform usage to token demand: as more subgraphs are created and more queries are fired, more $GAI is used.

From there, the revenue loop looks like this:

1. Users buy $GAI to access GraphEngine.
2. $GAI is converted into credits inside the platform.
3. Credits are spent on subgraph creation, maintenance, and queries.
4. The underlying $GAI value tied to those credits is then:

• Shared with subgraph creators/curators as revenue, and
• Partially burned or removed from circulation, tightening supply over time.

This does three things at once:

• Makes GraphEngine economically sustainable, even as usage grows.
• Rewards creators who build valuable subgraphs that attract queries.
• Directly links real on-chain demand to the value of $GAI.

No vague promises, no abstract “ecosystem fund” — just a simple, verifiable flow: usage → credits → value to creators + value to holders.

Why Queries Are the New Blockspace

If we zoom out, it becomes clear why this model is so powerful.

Today, blockspace is valuable because every transaction requires gas. The more useful the chain, the more transactions, the more gas is burned or paid to validators. GraphEngine is building a parallel model — but instead of selling blockspace, it sells intelligence space.

Each query is not just a read of a database. It is:

• Hitting a curated subgraph,
• Running graph queries,
• Pulling together events, transfers, positions, and relationships,
• And giving AI models a clean, structured context to reason over.

As the agentic future unfolds, queries will not just come from humans. They will come from bots, agents, and automated systems that:

• Monitor markets and flows 24/7,
• Trigger positions and hedging strategies,
• Watch liquidity and risk across multiple protocols,
• Generate alerts and actions in response to real on-chain events.

Humans may send tens or hundreds of queries per day. Agents can send thousands, consistently, as long as the answers are profitable.

That is why tying revenue to queries is so important:

• If GraphEngine becomes the default way AI agents understand on-chain state,
• And if every query requires credits,
• Then query volume naturally translates into sustained $GAI demand.

In other words, we are not betting on one-off mints, single events, or speculative cycles. We are building for a world where continuous, machine-driven intelligence becomes the norm — and where each unit of that intelligence runs through a revenue system powered by $GAI.

GraphEngine V1: How the Revenue Loop Actually Works

When V1 launches, the economics are simple and transparent:

Access

• Users acquire $GAI on supported markets.
• Inside GraphEngine, they convert $GAI into platform credits.

Usage

Credits are spent to:

• Create new subgraphs (e.g., Wallet, Token, DEX Lenses, or custom NL-based ones).
• Keep subgraphs running (ongoing ingestion and indexing).
• Run queries via:
• The UI and dashboards,
• Chat and natural language queries,
• MCP integrations into LLMs and agents.

Value Routing

The system logs which subgraphs are being queried, how often, and by whom. A portion of the value tied to those credits is:

• Allocated as revenue share to the subgraph’s creator/curator, and
• Used for burning or locking $GAI, reducing circulating supply over time.

So when you see “query volume increasing,” it does not just mean more traffic on the platform. It means:

• More $GAI being used for credits.
• More value flowing to the builders who create high-quality subgraphs.
• More $GAI being taken out of open circulation.

This is why V1 is a turning point: we move from “free usage” to a direct, measurable connection between intelligence and token value.

GraphPools: The Next Layer of the Revenue Stack

GraphEngine V1 is the moment the revenue engine turns on.
GraphPools will be the moment it starts to compound.

GraphPools are curated collections of subgraphs grouped under a single economic unit. Instead of backing one subgraph at a time, users will be able to support:

• A DeFi insights pool,
• A wallet-intelligence pool,
• A launch-monitoring or narrative-tracking pool,
• Or any other theme a curator designs.

Each GraphPool will:

• Be paired with $GAI in a liquidity pool.
• Represent a bundle of subgraphs that share query revenue.
• Allow users to buy GraphPool exposure using $GAI and receive a share of the underlying revenue performance.

This does a few important things:

• It rewards curators, not just individual technical builders.
• It spreads revenue across a portfolio of intelligence sources, not a single graph.
• It gives users a way to back the intelligence they believe will matter most, similar to backing an index rather than a single stock.

From a token perspective, GraphPools magnify everything V1 starts:

• More subgraphs created and maintained.
• More queries across multiple related graphs.
• More $GAI used to participate in pools and shared revenue.
• More opportunities for burn mechanics and supply tightening.

GraphEngine V1 turns intelligence into a paid service. GraphPools turn that service into an open marketplace of intelligence, with $GAI at the centre.

Closing Thoughts: Usage As the Only Real Signal

There are many ways to design a token economy. Most do not survive contact with reality.

Our goal with GraphEngine V1 and GraphPools is simple:
build an ecosystem where the only thing that really matters is usage.

• If people build subgraphs that nobody queries, they earn nothing.
• If someone creates a Lens configuration or wallet graph that becomes indispensable, they share in meaningful revenue.
• If agents, dashboards, and protocols start relying on GraphEngine for intelligence, $GAI becomes the asset that powers that entire flow.

We’ve gone from free experimental Beta → to paid, credit-based intelligence engine → and soon to curated, revenue-sharing pools of on-chain knowledge.

V1 is the start of that journey — the moment the economic loop closes and $GAI moves from potential utility to active, measurable demand.

The next phase is not just about more features. It is about turning queries into value flows for everyone who helps build, curate, and use the intelligence layer of Web3.

Follow us on X and Telegram to be the first to know.

— Team GraphAI

Over the past two weeks, GraphEngine has advanced through one of its most defining phases yet — the shift from a maturing Beta product into a production-grade AI data engine. These updates are not just technical milestones; they reshape how GraphEngine thinks, scales, and delivers value.

We’ve reached the point where the architecture, intelligence layer, and economic system are aligning into a unified foundation built for real-world usage, real revenue, and real growth.

LangChain Deep Agent — A New Brain for GraphEngine

The biggest leap forward this cycle is the introduction of the LangChain deep agent, which fundamentally changes how GraphEngine interprets user intent and generates answers.

Previously, GraphEngine relied on a single inference step:

You ask a question → the LLM interprets → produces one Cypher query → returns an answer.

That’s the equivalent of asking someone a complex question and expecting them to reply instantly without thinking.

The new deep agent changes everything:

• It breaks the user’s question into multiple reasoning steps
• It reviews subgraph schemas, checks event types, and selects tools
• It executes multiple Cypher queries instead of one
• It analyses intermediate results before deciding on a final conclusion
• It behaves like a mini-researcher rather than a one-shot responder

This leads to dramatically improved accuracy, especially for:

• Complex wallet behaviour questions
• Multi-token or multi-pool analytics
• Context-dependent DeFi insights
• Questions requiring correlation or synthesis across events

For users, this means GraphEngine isn’t just answering questions — it’s reasoning about them.

And as we move into V1, this multi-step reasoning layer becomes the backbone of all natural language interactions, Lens outputs, and future agentic workflows.

Credit System — Foundation of the $GAI Intelligence Economy

The new credit system is the single most important economic update since GraphAI launched. This system is what transforms GraphEngine from a free experimental environment into a full economic protocol where intelligence becomes a monetisable asset. Credits become the universal unit of computational work inside GraphEngine.

What credits control:

• Subgraph creation
• Subgraph maintenance
• Query executions
• Chat interactions
• MCP-powered agent calls

Users purchase credits using $GAI, which creates a direct link between platform usage and token value.

This is the engine for:

Economic sustainability: Every query burns or spends GAI, creating recurring organic demand.

Creator monetisation: Subgraph creators and future GraphPool curators receive revenue flows tied to how often their subgraphs are used.

Intelligence markets: Users back the intelligence they believe in, curators compete to build the best pools, and the best data wins.

Platform growth without paywalls or subscriptions

AI agents pay-per-request using credits, creating seamless, machine-native monetisation. The credit system is the gateway to:

• GraphEngine V1 going revenue-live
• GraphPools becoming a robust marketplace
• Sustainable value being returned to $GAI holders
• Real demand, independent of hype cycles

In short: credits turn GraphEngine into a living economy, not just a data tool.

High-Throughput Kafka Pipeline — Infrastructure Built for Millions of Events

As more subgraphs come online and usage increases, ingestion throughput becomes mission-critical. This cycle introduced major architectural upgrades to ensure GraphEngine can scale gracefully.

We expanded the Kafka consumer pipeline with:

• 6 fully parallel decoded-log consumers
• Larger worker pools for ingestion tasks
• Faster interaction with Neo4j and Allium
• Better handling of real-time stream bursts
• Near-instant propagation of high-volume event flows

Subgraphs — especially those built with Wallet, Token, and DEX Lenses — generate massive bursts of data during periods of market volatility, high-volume launches, whale movement, and intense DeFi activity. To maintain accurate query responses, reliable AI-agent reasoning, real-time dashboards, alerts, and automated workflows, GraphEngine must process this firehose without delay. The upgraded Kafka pipeline is what makes this possible, ensuring true real-time performance at Web3 scale.

Allium Migration, Wallet Lens & Native Transfers — The Wallet Intelligence Breakthrough

These three areas together represent the new intelligence foundation of GraphEngine V1. They’ve been mentioned in earlier DevLogs, but the latest progress completes the puzzle.

Allium Migration — Clean, decoded, structured data

GraphEngine now ingests exclusively through Allium’s decoded logs, removing the need for internal ABI decoding and greatly simplifying the pipeline. This upgrade delivers cleaner, standardised data, much faster ingestion, and immediate readiness for cross-chain expansion. It also enables seamless historical backfilling and removes major infrastructure overhead. Altogether, it forms the core foundation that makes real-time, multi-chain intelligence possible for V1 and beyond.

Wallet Lens — On-chain behaviour in one unified view

Wallet Lens is ready for V1 and provides a complete behavioural profile for any wallet on Base. It unifies ERC20 events, inflows, outflows, and interaction patterns into one structured subgraph for time-based behavioural analysis. Users can detect shifts, trace smart money, monitor risks, identify narratives, or build agentic workflows around wallet activity. As one of the most in-demand analytics layers in Web3, Wallet Lens delivers powerful wallet intelligence from day one.

Native Transfers — Completing the picture

Native token flows were the final missing element in GraphAI’s wallet analytics, and the new Native Transfer Ingestion Service now captures them in real time. With ETH and gas-token transfers synced alongside decoded logs, Wallet Lens now reflects the full financial footprint of any address. The system is optimised for high-volume wallets, improving sync speed and accuracy. Combined with Allium and Wallet Lens, native transfers complete GraphAI’s first holistic wallet-intelligence stack for V1.

Redis Caching, Frontend Enhancements & NL Subgraph Improvements

These enhancements polish the user experience and improve performance across the system.

Redis Caching

• Removes duplicate Allium logs
• Speeds up repeated queries
• Reduces load on external APIs
• Prevents ingestion slowdowns during spikes

A critical upgrade for stability as V1 user traffic increases.

Frontend Enhancements / UX upgrades include:

• Clear credit balances and credit history
• Better error messaging
• Cleaner Lens interfaces
• Improved timestamp formatting
• Better admin toolkit visibility

Small improvements — big impact when onboarding thousands of new users.

Natural Language Subgraph Flow

This flow is now smarter thanks to:

• Wallet address compatibility
• Improved event schema support
• Clarified instructions and inputs

While NL subgraphs are less deterministic than Lenses, they remain a powerful creation tool for rapid prototyping and experimentation.

Closing Thoughts

The development efforts and accomplishments over the last cycle mark a defining moment in GraphEngine’s evolution — the point where we shift from building a Beta to completing a production-grade intelligence engine.

Over these two weeks, we strengthened every pillar of what V1 will stand for: deeper reasoning through the LangChain agent, scalable real-time ingestion powered by Allium and Kafka parallelism, full wallet-level intelligence via Wallet Lens and native transfers, and the introduction of a credit system that activates true $GAI utility.

GraphEngine V1 is more than an upgrade — it’s the moment the network becomes intelligent, scalable, and economically alive. When V1 launches, intelligence accelerates, data expands, and the GraphAI economy begins to move in full force.

The official launch date will be shared very soon. Stay tuned.

Follow us on X and Telegram to be the first to know.

— Team GraphAI

After months of development, iteration, and community-driven refinement, GraphEngine is preparing to transition from its Closed Beta into a fully production-ready intelligence network. This shift marks far more than a version upgrade, it represents a fundamental evolution in architecture, scalability, economics, and opportunity across the entire GraphAI ecosystem.

In this article, we break down what V1 brings, why it matters, and how it opens the door to a revenue-powered, multi-chain future for users, builders, and $GAI holders.

1. From Testing Ground to Production-Grade Intelligence Layer

The Closed Beta served exactly the purpose we designed it for: push GraphEngine to its limits, gather high-quality feedback, and observe real-world usage patterns. With thousands of queries, dozens of subgraphs, and months of active testing, we learned what needed to be streamlined, hardened, improved, and rearchitected.

With V1, GraphEngine transitions from a controlled testing environment into a fully operational, production-level AI data engine. This means:

• Stable, validated ingestion flows
• Optimized query performance
• Predictable uptime and reliability
• Stronger internal automation
• Tighter observability and monitoring
• A system built to handle real user traffic at scale

Everything that was experimental in Beta becomes industrial-strength in V1 — ready not just for devs and testers, but for businesses, builders, agents, dashboards, and the broader Web3 AI ecosystem.

2. A Rebuilt Architecture for Cross-Chain Scale

One of the biggest upgrades in V1 is a complete overhaul of how GraphEngine ingests and processes blockchain data. The beta relied on a mix of internal decoding and RPC-level logs. This was fast enough for testing, but not for scale. V1 shifts to an Allium-powered ingestion pipeline, giving GraphEngine:

• Pre-decoded, structured logs across Base and soon other chains
• Near real-time processing with drastically reduced latency
• Built-in support for historical backfilling
• Clean, consistent event schemas for higher-quality AI outputs
• A multi-chain foundation that can support expanding ecosystems

This new architecture doesn’t just make GraphEngine bigger, it makes it smarter, faster, and fundamentally more flexible. It’s the backbone that will let users create richer subgraphs, run deeper analytics, and soon access intelligence across many chains, not just one.

3. Expanding Lenses and Use Cases

With V1, users will access a more capable suite of Lenses (Token, DEX, Wallet), each powered by the new ingestion layer and upgraded schemas. These Lenses allow anyone, with no coding, to create sophisticated, AI-ready subgraphs that:

• Track liquidity, trading flows, yield and fees
• Map token holder behaviour and distribution
• Monitor wallet activity, inflows/outflows, and interactions
• Provide data pipelines to power bots, dashboards, and agents

This is the beginning of a broader ecosystem of intelligence templates. Over time, GraphEngine will incorporate:

• Cross-chain analytics
• RWA/DeFi lens expansions
• Specialised event tracking
• Extended wallet behaviour analysis
• Agentic “intelligence packs” for real-time reasoning

V1 is where the foundation becomes strong enough to build all of this on top.

4. Monetisation Arrives — and $GAI Enters Its Value Phase

The most important change coming with V1 is the activation of GraphEngine’s revenue model. During Beta, GraphEngine was free. With V1, it becomes a revenue-generating intelligence network. Here’s what this means in simple terms:

• Every subgraph created requires credits.
• Every query executed requires credits.
• Credits are purchased using $GAI.
• A portion of all $GAI accumulated is burnt driving value back to our holders.

This completes the flywheel:
1. Users buy credits using $GAI
2. Subgraphs + queries consume credits
3. Credits → burned or routed into ecosystem pools
4. Demand rises for $GAI
5. Subgraph creators and curators benefit via GraphPools
6. Ecosystem feedback loop strengthens as usage grows

GraphEngine V1 isn’t just a platform upgrade, it’s the moment where the GraphAI ecosystem becomes economically alive. A dedicated post on this will soon follow.

5. GraphPools: The Next Step in Tokenized Intelligence

Shortly after the V1 launch, we’ll introduce GraphPools, enabling creators and curators to tokenize collections of subgraphs and earn from usage. Think of GraphPools as:

• Tokenized intelligence bundles
• Powered by real query revenue
• Paired with $GAI liquidity
• Open to curators, builders, and power users

This turns high-performing subgraphs into productive assets and allows $GAI holders to participate in the growth of the intelligence economy. GraphPools are where monetisation meets community, and where intelligence becomes a liquid market.

6. A Development Roadmap Built for Continuous Expansion

V1 is not the finish line, it’s the launchpad. Over the following weeks, users will see:

• Historical data backfilling
• Cross-chain support
• External data enrichment (pricing, labels, risk signals, off-chain context)
• Advanced agentic reasoning models
• MCP integrations with major AI systems
• More Lenses and more templates
• Faster ingestion and wider coverage

The goal is simple: Make GraphEngine the default intelligence layer for AI agents, DeFi systems, Web3 applications, and autonomous decision-making tools.

7. A Thank You to Our Beta Community

Nothing we’ve built, or are about to release, would exist without the builders, analysts, testers, and early adopters who helped shape GraphEngine throughout the Closed Beta. Your feedback, bug reports, ideas, and experiments directly influenced the architecture, the design, and the direction of V1. You have been instrumental in making GraphEngine what it is becoming: A scalable, high-performance, AI-native intelligence network for all of Web3.

Closing Thoughts

GraphEngine V1 is more than an upgrade. It’s the turning point where GraphAI evolves from a promising prototype into a scalable, revenue-powered, multi-chain intelligence network with real economic impact. The architecture is stronger. The ecosystem is broader. The opportunities are bigger and the value to $GAI holders is about to enter a new phase.

The next era of GraphAI begins now.

Stay tuned for the official launch date — coming next week.

Follow us on X and Telegram to be the first to know.

— Team GraphAI

Over the past two weeks, the GraphAI dev team has been fully focused on one thing: turning GraphEngine into a high-throughput, wallet-aware, multi-chain data engine that is ready for real-world scale.

We completed the migration to our new decoded-data provider, built a native transfer ingestion service, optimised our messaging pipeline to handle hundreds of events per second, and finalised the first version of Wallet-level analytics through a new upcoming Wallet Lens. At the same time, we improved observability and made it easier for users to connect GraphAI to the tools they already use.

This DevLog walks through what changed, why it matters, and how it sets us up for GraphEngine V1.

Allium Decoded Logs — Clean Data In, Smart Insights Out

A core milestone this cycle was completing our integration with our decoded data partner (Allium), and switching all subgraphs over to consume decoded blockchain logs instead of raw events.

Previously, GraphEngine relied on a combination of Alchemy and in-house ABI decoding. This worked well for early experimentation but had some clear trade-offs:

• Every new contract type required custom decoding logic.
• Scaling to multiple chains meant repeating heavy infrastructure work.
• Debugging issues in low-level log parsing could slow down feature development.

With the new decoded-logs integration:

• Subgraphs now consume already decoded events directly, rather than raw hex logs.
• The ingestion architecture is simpler and more reliable — we focus on what happened, not how to parse it.
• Adding support for new chains becomes primarily an API configuration, not a full infra project.

For users, this translates into:

• More consistent and accurate on-chain data.
• Faster rollout of new Lenses and new chains.
• Less “mystery” around why a specific event was or wasn’t captured.

In short, we’ve moved the heavy lifting of decoding down a layer, so GraphEngine can concentrate on structuring intelligence for AI instead of fighting raw log formats.

Native Transfers Ingestion — Seeing The Full Wallet Picture

On top of decoded ERC20 events, we built a Native Transfers Ingestion Service that tracks ETH and other native token movements for all monitored wallets.

Why this matters:

Most real on-chain behaviour is a blend of token transfers and native value flow (e.g. gas, direct sends, protocol interactions). If you only see one side, you never get a complete picture of:

• How value is entering or leaving a wallet.
• Which addresses are true “senders” vs. “receivers”.
• How wallets behave across tokens, protocols, and time.

The new service:

• Ingests native transfer events into SNS (our messaging bus).
• Pipes those events into downstream subgraphs and analytics pipelines.
• Scales automatically as the number of tracked wallets grows.

This is a foundational step for Wallet Lens and future wallet-centric analytics. Instead of just answering “which tokens moved,” GraphEngine will increasingly answer “what is this wallet doing on chain?”

High-Throughput SQS Pipeline — Hundreds Of Events Per Second

Moving to a decoded, multi-source ingestion model only works if the underlying pipeline can keep up.

This cycle we focused heavily on optimising our SQS-based processing system so that GraphEngine can comfortably handle spikes in on-chain activity.

Key upgrades:

• More workers per subgraph — scaled from 1 → 5 workers, allowing multiple messages to be processed in parallel.
• Increased I/O worker pools — more concurrent calls to AWS, Supabase and Neo4j, so the system is not bottlenecked by external services.
• Worker-level Supabase caching — each worker caches repeated lookups, drastically cutting redundant database queries.

In practice, the system is now capable of processing hundreds of messages per second with minimal lag.

For users, this means:

• Subgraphs stay closer to “real time” even during busy network periods.
• Queries and AI responses are based on fresher data.
• The platform is ready for higher-traffic use cases like popular DeFi tokens, launch events, and active wallets.

Chat Integrations — Bringing GraphAI To Where Users Already Are

Not every insight needs a dashboard.

This week we finalised a Chat Integrations page that documents how to connect GraphAI to platforms like Telegram, Discord, and websites.

The goal is simple: let users and communities tap into GraphEngine’s intelligence without having to log into a separate UI each time.

The new page will:

• Show how to wire Graph AI chat backend into Telegram bots, Discord bots, and simple web embeds.
• Lowers the barrier for communities to run “ask me anything” on-chain bots powered by GraphEngine.
• Is the first step toward a broader “GraphAI everywhere” experience.

As more subgraphs come online — especially DeFi and wallet analytics — these chat-based touchpoints will become a natural way for communities to interact with live blockchain intelligence.

Wallet Lens — From Addresses To Behaviour

With decoded logs and native transfers in place, we started building one of the most requested features: Wallet Lens.

The idea behind Wallet Lens is straightforward but powerful:

Give users a unified, structured view of everything a wallet does on chain.

Over the last two weeks, the team:

• Defined the schema and ingestion flow for wallet-level analytics.
• Wired in both decoded ERC20 transfers and native transfer events.
• Built and tested a Wallet Tracking Lens on Base that tracks all ERC20 transfers linked to selected wallets.

On top of this, we’ve now:

• Validated the completeness and correctness of ERC20 data from Allium.
• Tested Token Lens and Wallet Lens against sample datasets to ensure no events are missing or misclassified.

What this unlocks:

• Tracking activity for whales, funds, smart money, or protocol-owned wallets.
• Understanding how wallets move value across tokens and protocols.
• Feeding wallet-level insight into agents, alerts, and dashboards.

Wallet Lens is still early, but the plumbing is now in place. Future iterations will layer on behaviours (e.g. “farmer”, “long-term holder”, “LP”, “airdrop hunter”) and surface these in GraphAI and, eventually, GraphPools.

Logging And Observability — Seeing Inside The Engine

As the ingestion architecture becomes more complex, observability becomes crucial.

This cycle we:

• Enabled centralised AWS CloudWatch logging across all EC2 instances.
• Standardised logging formats so events from different services can be correlated.
• Improved traceability for ingestion, decoding, and data processing workflows.

This may sound like pure infrastructure work, but it has tangible user benefits:

• Faster diagnosis and resolution of data issues.
• Better guarantees that what you see in a Lens matches what actually happened on chain.
• Higher confidence as we scale to more subgraphs, chains, and users.

For a system that aims to be the AI-ready source of truth for blockchain activity, this kind of visibility is non-negotiable.

Summary Of Impact

Across the last two weeks, the GraphAI dev team has:

• Fully migrated to decoded logs, simplifying ingestion and improving data quality.
• Built a native transfer ingestion service to complete the wallet-level picture.
• Optimised the SQS pipeline so GraphEngine can handle hundreds of messages per second.
• Shipped a Chat Integrations page to help users bring GraphAI into Telegram, Discord and web frontends.
• Kicked off and tested Wallet Lens, laying the foundation for deep wallet analytics.
• Improved logging and observability with CloudWatch to keep the system robust as we scale.

These are not flashy front-end features, but they are exactly the kind of foundational upgrades needed before a public V1 launch.

Closing Thoughts

GraphEngine is steadily evolving from “an AI that can query the blockchain” into an AI-native data fabric for Web3 — one that understands tokens, DEXs, wallets, and behaviours in real time, across chains.

With decoded multi-chain ingestion, wallet-level intelligence, high-throughput pipelines, and growing integration points, the platform is nearly ready for its next phase: GraphEngine V1, open to all.

Next Up:

We will finish validating DEX Lens on the new data stack, begin historical backfilling using our new ingestion model, roll out caching and latency optimisations, and move closer to opening Wallet Lens and the upgraded Lenses library to the wider community.

As always, thank you to our Beta users and partners — your feedback and experimentation continue to shape the future of GraphAI.

Follow us on X and Telegram to be the first to know.

— Team GraphAI

The Next Step in AI-Native Monetisation

As GraphAI prepares to bring GraphEngine V1 to the public, one of the most exciting upgrades on the horizon is the planned integration of x402 — an open standard redefining how web services handle payments. Using the long-reserved HTTP 402 “Payment Required” response, x402 allows instant, onchain micropayments in USDC on Base for API calls, queries, and agent-to-agent interactions.

For GraphAI, which powers intelligent access to blockchain data via subgraphs, Lenses, MCP endpoints, and soon GraphPools, this opens a transformative new layer of real-time, pay-per-query monetisation for both users and developers.

Why x402 Fits GraphAI Perfectly

x402 is built for exactly the type of machine-native economy GraphAI is enabling. Rather than relying on traditional API keys or subscription models, x402 enables direct, verified, onchain payments — with settlement times as fast as 200 milliseconds on Base.

This aligns perfectly with GraphAI’s architecture and vision for agentic AI systems that autonomously query blockchain data, trigger reasoning, and return results in real time. Every insight, query, or transaction can now be priced, paid for, and settled instantly, all without the friction of manual billing or authentication.

The integration would empower a decentralized, fairer data economy — one where creators and curators are rewarded directly at the point of value delivery.

1. Per-Query Monetisation for GraphEngine & MCP

x402 can be placed directly in front of GraphEngine’s Query API and all MCP endpoints. If a query or request isn’t pre-paid, the system simply returns an HTTP 402 response, prompting the client to pay and retry automatically.

This creates a native pay-per-query model, ideal for LLMs and AI agents querying real-time subgraphs. Users or agents will no longer need API keys or billing setups — each query is a microtransaction, settled instantly in USDC on Base.

2. Pay-Per-Insight for Lenses & GraphPools

GraphAI’s upcoming GraphPools and existing Lenses can also be monetized with x402, enabling per-query or time-based access to curated intelligence. Each request to a DEX Lens, Token Lens, or curated GraphPool would trigger a microtransaction — revenue that automatically flows into the subgraph’s revenue pool and is shared with curators and token holders.

This model bridges real-time data consumption and tokenized ownership, transforming insights into yield-bearing digital assets and reinforcing $GAI’s role as the fuel for the entire ecosystem.

3. Frictionless Developer Access

With x402, developers and autonomous agents can start using GraphEngine instantly — no account setup, no API keys, no invoicing. They pay per query, with x402-compatible SDKs automatically handling detection, payment, and retry flows.

For hackathons, startups, and agent builders, this means zero onboarding friction and infinite scalability — anyone can start building with GraphAI by paying as they go, in pure machine-to-machine fashion.

How It Would Work

1. Request: A user or agent calls a GraphEngine or MCP endpoint.
2. Response (402): The system returns a 402 response with a “price” for that query.
3. Payment: The client automatically pays in USDC on Base and retries.
4. Settlement: Within milliseconds, the payment clears and the query runs.
5. Revenue Split: Revenue routes to GraphPools, curators, and (optionally) $GAI buybacks or treasury allocations.

This brings true market dynamics to data access — insights cost what they’re worth, paid in real time, and shared transparently.

Implications for the $GAI Token Economy

Integrating x402 would tie the $GAI ecosystem more deeply to real, measurable economic activity. As demand for onchain intelligence grows, so does query volume — and thus payment flow.

Potential token impacts include:

• Increased demand for $GAI as it becomes a medium of exchange and liquidity pairing for GraphPools.
• Enhanced value accrual via automatic routing of a portion of x402 revenues into $GAI buybacks or staking incentives.
• A new economic layer where data, liquidity, and intelligence intersect, driving compounding value to both users and the network.

The Future of Intelligent Payments

By combining GraphAI’s AI-native data layer with x402’s instant micropayment infrastructure, we unlock a new paradigm: real-time intelligence that pays for itself.

It’s the natural evolution of what GraphAI has been building — not just structured blockchain data, but an intelligent, self-sustaining data economy where every query, insight, and action creates measurable value.

With this integration, GraphEngine doesn’t just serve intelligence — it settles it.

Follow us on X and Telegram to be the first to know.

— Team GraphAI

Over the past two weeks, the GraphAI development team has continued shaping GraphEngine into the world’s most connected, scalable, and community-driven Web3 & AI data platform. From deepening our upcoming Allium integration and automating production pipelines, to enabling Telegram and Discord queries and enriching user identity through social and developer profiles — the focus has been on scale, accessibility, and user experience.

These developments mark the final stretch before GraphEngine V1, bringing together infrastructure readiness, real-time data ingestion, and an expanding set of tools that bridge blockchain, AI, and community.

Allium Integration: Powering Real-Time Multi-Chain Data

Our integration with Allium (GraphAI’s chosen decoded data provider) has entered its implementation phase. Allium will provide pre-decoded blockchain logs and wallet transaction APIs, enabling GraphEngine to scale seamlessly across chains without compromising performance.

During testing, the dev team worked closely with Allium engineers to identify and resolve issues, refine the API structure, and finalize the ingestion pipeline. With infrastructure now deployed via EC2 and Lambda services, we’re preparing to parse decoded blockchain logs directly into GraphEngine.

This partnership will be the backbone of GraphAI’s transition into a real-time, multi-chain AI data network, drastically cutting latency, infrastructure costs, and integration overhead.

Auto Push-to-Prod: Continuous Deployment, Zero Downtime

We’ve implemented a new automated CI/CD pipeline, enabling faster and safer deployments from development to production. This system automatically commits code, opens pull requests, merges if conflict-free, and notifies the team instantly via Slack.

The result:

• Rapid iteration with minimal manual intervention.
• Immediate visibility into deployment health.
• A more reliable and responsive release cadence — critical as we prepare for public rollout.

Telegram & Discord Bots: AI Access Anywhere

GraphAI’s ecosystem will very soon extend beyond the browser. Users will be able to query subgraphs directly inside Telegram and Discord, receiving real-time, AI-generated insights without leaving their preferred communication channels.

This integration expands GraphAI’s reach to builders, analysts, and communities — allowing insights to flow seamlessly through the platforms where collaboration already happens.

It’s another step in our mission to make AI-native blockchain intelligence accessible everywhere — from dashboards to chat interfaces.

User Profiles — A New Identity Layer for Builders

The launch of the User Profile Page introduces social identity into GraphAI. Creators can now set handles, bios, and link their Twitter and GitHub accounts, giving every subgraph a visible, verifiable creator.

These profiles appear across the platform — on subgraph pages, leaderboards, and future community dashboards — turning GraphEngine into not just a data tool, but a network of discoverable creators.

• Twitter integration adds follower data and cross-platform visibility.
• GitHub integration highlights developer credentials directly in profiles.

Together, these enrich community credibility and build social trust — essential for the upcoming GraphPools ecosystem, where creators and curators will be the backbone of value creation.

Privacy & Safety: Subgraph Controls and Environment Isolation

The team introduced subgraph visibility settings, allowing users to decide whether their subgraphs are public or private. Private subgraphs remain visible only to the creator, protecting proprietary data and research.

Additionally, environment-based graph isolation now separates development and production subgraphs, preventing test data from leaking into live systems. This ensures that experimentation stays safe — a vital feature as more builders begin deploying production-grade intelligence pipelines.

GraphAI Widget — Bringing Intelligence Everywhere

To make GraphAI’s intelligence portable, the team has built a lightweight, embeddable widget that allows any website or platform to query GraphAI subgraphs through a single line of JavaScript.

This marks a new frontier for GraphAI adoption — enabling communities, analytics dashboards, and dApps to embed live, AI-powered insights directly into their frontends without custom infrastructure.

It’s a simple step with huge implications: bringing the power of GraphEngine into any digital environment.

Summary of Impact

Over the past two weeks, the development team has:

• Strengthened integrations with Allium to prepare for real-time, multi-chain ingestion.
• Automated deployments for faster, more reliable production rollouts.
• Expanded GraphAI’s reach through Telegram, Discord, and website widgets.
• Introduced rich user profiles with Twitter and GitHub integrations.
• Enhanced privacy, safety, and data ownership with visibility and environment controls.

These upgrades solidify the foundation for GraphEngine V1, uniting infrastructure, intelligence, and identity into a single ecosystem.

Closing Thoughts

GraphAI is evolving beyond an AI data platform — it’s becoming an intelligent network where data, people, and AI interact fluidly. With integrations expanding, automation maturing, and community tools coming online, the platform is entering its most powerful stage yet.

Next: we complete the Allium integration, finalise multi-chain ingestion, and begin the GraphEngine V1 rollout — unlocking a truly scalable, AI-native data layer for Web3.

Apply for Closed Beta Here

Follow us on X and Telegram to be the first to know.

— Team GraphAI