Vectorize AI, Inc.

Building an AI support bot is easy. Building one that actually remembers the customer is the hard part. To solve this, Akshat Talwar and I built an agent architecture that deeply integrates with Hindsight memory. We were tired of support bots that treat every interaction like a first date. You can grind on system prompts for hours, but without a robust, isolated memory layer, the LLM is essentially an amnesiac. Customers hate repeating their order numbers every time they open a new chat window. For quite some time, we have been working on IRIS (Intelligent Recall & Issue Support). Instead of just another standalone chatbot wrapper, we built a headless, multi-tenant API service. It sits between an e-commerce brand's existing helpdesk and their order management system to provide automated support with actual long-term context. We just published a technical deep-dive on how the system is built. Here is a look under the hood at how we handle state: - Dumping raw chat logs into a single vector database is a disaster. The agent gets confused and cross-references different users. We use Hindsight (https://lnkd.in/gdCpFNpE) by Vectorize AI, Inc. to strictly segregate state into per-customer banks and global pattern banks. - Global pattern detection is a massive business lever. If 50 people report a "warehouse delay" at the same time, the global memory catches it. The bot stops troubleshooting individual users and immediately acknowledges the known outage, saving hundreds of redundant API calls. - Summarize, do not concatenate. We use intermediate reflection steps to summarize a user's profile before the main LLM call. This drastically improves accuracy and reduces token costs compared to cramming past transcripts into the context window. If you are an engineer dealing with stateful LLM applications or agent memory, we wrote up the exact code behind our memory routers and the lessons we learned. Check out the overall system here: https://lnkd.in/gEYeMKKF Read the full technical write-up on https://lnkd.in/gbMNCrUe

🚀 AI Freelancer Brain 🧠 Over the past few weeks, I’ve been working on a project that solves a very real problem. When you’re handling multiple clients, it’s hard to remember everything — their preferences, feedback style, budget behavior, or even how they like to communicate. That’s where AI Freelancer Brain comes in. It’s a smart system designed to act as your client memory layer — helping you keep track of important insights so you can build better, long-term client relationships. 💡 What We Built ⚡ AI-powered client memory extraction from raw conversations 🧠 Stores insights like preferences, feedback patterns, and scope signals 💬 Clean chat-based interface for easy interaction 📊 Structured client data to improve decision-making over time ⚙️ Tech Stack Next.js (Frontend) Node.js (API & backend logic) AI-based extraction system Database: (Supabase / MongoDB) Deployment: Vercel ⚡ One Challenge We Faced Designing a reliable system to convert unstructured client conversations into meaningful structured data. Unlike traditional apps, this wasn’t just about storing data — it was about understanding it. We focused on extracting key signals like: Communication preferences Feedback patterns Budget sensitivity Scope behavior 🧠 Key Learnings This project gave us a deeper understanding of: Designing practical AI features for real-world use cases Structuring backend systems beyond simple CRUD Building tools that focus on usability + intelligence 🔮 What’s Next Still exploring ways to improve it — thinking of adding: 👀 Smart reminders for follow-ups 📊 Client analytics & insights ⚡ Even better memory + recommendations 👥 Team Behind This Nupur Choksi Nishta Hemdev Netra Vora 👉 Live Demo: https://lnkd.in/dKs-6QpA 📂 GitHub Repo: https://lnkd.in/dKhw7b-y Would love to hear your thoughts or suggestions!

Your fire alarm has lied to you for the last 30 years. It uses thresholding on smoke density and labels it "AI". Detection is a vision problem. We've solved it. Dual YOLOv8 models (fire & smoke, independently trained) are run on CCTV streams. No sprinklers. No new hardware. Just a camera and some inference. Important numbers: → mAP ≥ 0.90 for fire and ≥ 0.85 for smoke → <5 sec processing time: detection → database query → email with screenshot + confidence score → Gemini API acting as another validation step, only real detections will be flagged → Database query returning camera ID, owner, and the closest fire station in one query → Role-based dashboard, owners, admins, and fire stations see different information. The memory problem was a valid concern. Before Hindsight integration, the agent had repeated false positives each session, unable to learn and adapt itself. With Hindsight, the agent was able to adjust its confidence thresholds based on the results of past incidents' resolution. This approach allowed us to incrementally improve performance over time without retraining. Pipeline vs. Agent difference: Classic system: 6 lacs for 5 years, 30-60 sec delay and no image verification AgniShakti: 60K, under 5 seconds, image attached. Wherever there is a camera, there is protection!! A Special thanks to my teammates:- Shudhanshu Kumar, Anurag Anand, Tanisha Priya, Aksha Arulita, Liza Talreja #AgniShakti #AIagents #HindSight #AgentMemory #LLM #Gemini

What if the reason your AI system isn’t improving… is because it forgets everything the moment it finishes a task?... On the next Context Window Podcast, Ed Anuff and Anant Jhingran are joined by Chris Latimer, CEO & Co-Founder of Vectorize AI, Inc., to unpack one of the biggest gaps in AI today: memory. We’ll dig into: 👉 Why most AI agents don’t actually learn - they just start over 👉 The difference between retrieval vs. real memory (and why it matters) 👉 What’s fundamentally broken in how today’s AI systems are built 👉 A new way to think about architecture: retain, recall, and reflect 👉 What becomes possible when systems actually improve over time AI agents are getting more powerful...but they’re still inconsistent, forgetful, and unreliable in real-world use. If we don’t solve memory, we’re not building intelligent systems… we’re just building better workflows. If you’re building AI products (or trying to), this is a conversation you don’t want to miss. 📅 Join us live Tuesday, 4/21

Most AI agents today don’t actually *learn* — they just repeat the same mistakes faster. That’s the real gap. After seeing how agents often fail at retrieval tasks and loop over the same errors, it becomes clear: intelligence isn’t just about answering — it’s about adapting. What stood out to me recently is the shift from static retrieval (RAG) to something more dynamic — **memory-driven systems**. Instead of: → Failing a query → Repeating it blindly We should be building agents that: → Log failures → Learn from them → Adjust their future behavior This idea of *episodic memory for AI agents* is powerful. A smarter agent doesn’t just retrieve better — it **remembers why it failed and improves over time**. Key takeaways: • Static vector similarity isn’t enough anymore • Agents need structured memory, not just context • Learning from execution paths is the real upgrade We’re moving from *“AI that answers”* → *“AI that learns”* And that’s where the real innovation begins 🚀 Curious to hear your thoughts — Are we ready to move beyond traditional RAG systems? GitHub project link - 'https://lnkd.in/g-aGWN26'. Aviral Bhardwaj Thank you for the opportunity . #AI #LLM #AgentMemory #Innovation #ProductThinking #WebDevelopment #FutureOfAI

Agents don’t fail because they lack models. They fail because they don’t remember. Most fraud systems I’ve seen keep relearning the same mistakes. We tried fixing that by adding memory via Hindsight. A few practical takeaways: • Before: static thresholds → same false positive, every time After: Hindsight recall → 94% match suppresses repeat mistakes • Store transactions as vectors, not logs. Retrieval > rules. • Use a high similarity cutoff (~0.92). Lower = noisy memory. • Let human feedback write memory instantly, not in retraining cycles. • Small weight mutations > full retrains for fast adaptation The shift is subtle: from predicting risk → remembering outcomes. Hindsight made the agent behave less like code, more like experience. Repo link - [ https://lnkd.in/g_TRebBx ] Article link - [ https://lnkd.in/gdhBGN2e ] #AIAgents #AI #Hindsight #AgentMemory #LLM