KAI: Context-Aware Assistant

KAI is a context-aware intelligence system that understands intent, remembers past interactions, and responds only when it matters—bridging software, hardware, and human context.

Comment 6

It's just a prototype and under development

Inspiration

Modern AI assistants are powerful, but they often feel disconnected from real human context. They respond to prompts, yet they do not truly understand why a user is asking something, who they are interacting with, or when it is appropriate to act. We were inspired by the idea of building an assistant that feels less like a chatbot and more like a thoughtful companion—one that understands intent, remembers past interactions, and responds only when it genuinely adds value. KAI was born from this idea: an AI system that prioritizes context, memory, and human-centric intelligence over constant interaction.

What it does

KAI is a context-aware intelligence system that understands user intent, remembers past interactions, and adapts its responses based on situation and history. Unlike traditional assistants that react to every command, KAI is event-driven—it observes selectively, reasons about context, and responds only when necessary. It also do automation, have real-time access to google and duckduckgo!! thnx to free apis, vision analysis, music playing, image generation, documents generation and many more!!

KAI supports:

Intelligent multi-turn conversations with memory Intent-aware responses (understanding why something is asked) Multimodal inputs such as text, voice, and images Real-time search and grounded responses Cross-session personalization using memory and embeddings The result is an assistant that feels more natural, less intrusive, and more human.

How we built it

KAI is built using a modular architecture that separates sensing, reasoning, and action:

Frontend: A responsive web-based interface with a modern, minimal UI

Backend: A Python-based intelligence layer handling reasoning, memory, and orchestration

AI Models: Gemini models for reasoning, multimodal understanding, and grounding; Groq for faster responses

Memory System: Vector embeddings stored in a database to enable long-term, per-user memory

Event-Driven Logic: The system activates intelligence only when meaningful triggers occur, rather than running continuously

This design allows KAI to scale across devices, including future hardware integrations, while keeping the core intelligence centralized and consistent.

Challenges we ran into

One of the biggest challenges was balancing intelligence with practicality. Continuous vision, voice, or automation quickly becomes inefficient, expensive, and intrusive. We had to redesign our approach to be event-driven, ensuring KAI feels intelligent without unnecessary computation.

Another challenge was building reliable long-term memory while keeping responses accurate and relevant. Designing intent understanding that goes beyond keywords required careful prompt engineering and semantic classification.

Accomplishments that we're proud of

Built a fully working, deployed context-aware assistant

Implemented long-term memory that persists across sessions

Designed an intent-aware response system that adapts tone and behavior

Created a clean, professional UI suitable for real users

Successfully integrated multimodal AI capabilities in a responsible way

What we learned

Through this project, we learned that true intelligence is not about doing more—it is about doing the right thing at the right time. We gained hands-on experience with system design, AI-human interaction, memory architectures, and ethical considerations in AI. Most importantly, we learned how software and intelligence can be designed to respect users rather than overwhelm them.

Features it have

🧠 Core AI Engine

Multi-LLM Provider System

  • Primary: Groq (14 API keys for 1.4M+ tokens/day capacity)
  • Fallback: Gemini → Cohere → Instant response
  • Smart Routing: Routes queries to optimal models based on complexity
    • Simple queries → llama-3.1-8b-instant (faster)
    • Complex queries → llama-3.3-70b-versatile (smarter)

Intelligent Query Classification

  • FirstLayerDMM: Decision-making model classifies intents
  • SmartTrigger: Ensemble system combining regex + semantic similarity
  • Automatic routing to: general chat, realtime search, app control, image generation, music, web search

💬 Chat & Conversation

Per-User Personalized Chat

  • User preferences (name, style, language) injected into responses
  • Adaptive personality based on input style (concise/detailed/neutral)
  • Chat history stored per-user in Firebase

Real-Time Search Detection

  • Automatically detects time-sensitive queries (prices, weather, news)
  • Routes to RealtimeSearchEngine with Gemini + Google Search grounding
  • Falls back to DuckDuckGo with retry logic
  • Returns source cards with clickable links

Response Enhancement

  • Code syntax highlighting with Prism.js
  • Markdown rendering with marked
  • Social intelligence layer for human-like responses

🧠 Memory System (Beast Mode)

Persistent Per-User Memory

  • Supabase cloud storage for user memories
  • Semantic search using SentenceTransformer (all-MiniLM-L6-v2)
  • 3-tier memory: Facts, Conversations, Session Context

Contextual Memory Intelligence

  • Auto-extracts important information from conversations
  • Injects relevant memories into LLM context
  • Memory pulse visualization in frontend

🎵 Music & Entertainment

Spotify Integration

  • Play tracks, albums, artists, playlists
  • Search Spotify catalog
  • Returns embedded player cards

🔍 Web & Search

Real-Time Search Engine

  • Gemini with Google Search grounding (5 API keys rotation)
  • DuckDuckGo fallback with HTML scraping
  • 5-minute response caching

Web Scraping Suite

  • ProWebScraper: Professional-grade content extraction
  • EnhancedWebScraper: JS rendering support
  • JarvisWebScraper: Multi-source aggregation

📄 Document & File Processing

Document RAG System

  • PDF support
  • Chunked processing with embeddings
  • Question-answering over documents

Vision Service

  • Image analysis with Gemini Vision API
  • Base64 image processing
  • Multi-image context support

Text Extraction

  • PDF text extraction
  • Document parsing
  • OCR-ready architecture

🤖 Automation & Control - KAI Web - Based Extension Needed

Action Chain System

  • Record and replay user actions
  • Macro execution
  • DOM manipulation

Smart Workflows

  • Multi-step task automation
  • Workflow engine for complex operations

🔗 SaaS Integrations

Productivity Suite

Service Features
Figma Browse design files
Notion Search workspaces
Slack Channels, messaging
Trello Board summaries
Google Calendar Event timeline
GitHub Repo management

Financial & Data

  • Crypto: Bitcoin, Ethereum prices
  • Weather: Live atmospheric data
  • News: AI news, Hacker News
  • NASA APOD: Astronomy pictures

🎤 Voice & Speech

Voice Input

  • WebkitSpeechRecognition support
  • Real-time transcription

Text-to-Speech

  • Edge TTS bypass for natural voices
  • Ultimate Voice system
  • Enhanced speech synthesis

🛡️ Security & Enterprise

Authentication

  • Firebase Auth (Email, Google, GitHub)
  • JWT token management
  • Rate limiting per endpoint

Security Middleware

  • CORS configuration
  • Security headers (X-Frame, X-XSS, HSTS)
  • Request validation

API Key Management

  • Multi-key rotation for all providers
  • Exponential backoff on rate limits
  • Automatic failover

🤖 Agent System

Multi-Agent Architecture

Agent Purpose
PlannerAgent Task decomposition
CoderAgent Code generation
ResearchAgent Information gathering
WriterAgent Content creation
AnalystAgent Data analysis
CriticAgent Quality review
CreativeAgent Creative tasks
WebBrowsingAgent Web navigation
ToolUsingAgent External tool calls
DocumentAnalysisAgent Document processing
MultiModalAgent Vision + text
AutonomousAgent Self-directed tasks

Agent Orchestration

  • SwarmOrchestrator: Parallel agent execution
  • AgentCollaboration: Inter-agent communication
  • AgentOrchestrator: Pipeline management

🎨 Frontend Features

UI Components

  • Cyber/tactical aesthetic design
  • Skeleton loaders for all states
  • Source cards for search results
  • Memory core visualization
  • Chat history sidebar
  • Settings panel (full-page responsive)

Smart Loading States

  • "🔍 Searching the web..." for realtime queries
  • "Neural processing..." for general queries

Rich Media

  • Spotify player embeds
  • PDF preview

📊 Performance Optimizations

Speed

  • Faster 8B model for simple queries
  • Skip memory injection for short queries
  • Connection pooling
  • Reduced retry delays

🚀 Deployment

Cloud-Ready

  • Render deployment configured
  • Netlify frontend support
  • Supabase for storage
  • Firebase for auth & data

📈 Statistics

  • Backend Modules: 97 Python files
  • Agent Types: 17 specialized agents
  • API Endpoints: 360+ routes
  • LLM Keys: 14 Groq + 5 Gemini (rotation)
  • Memory Types: 3 tiers (facts, conversations, session)
  • Integrations: 10+ SaaS services

What's next for KAI: Context-Aware Assistant

Next, we plan to expand KAI into a hybrid software–hardware system, allowing it to operate as a physical context-aware node using edge devices like Raspberry Pi. We also aim to enhance intent reasoning, improve memory visualization, and enable deeper multimodal understanding—all while maintaining KAI’s core philosophy: intelligence with restraint.

Built with ❤️ by Krish Verma

Built With

Share this project:

Updates