Most LLM products plateau once conversations span days or departments. Context windows burst, graph stores drift, and costs spike. Worse, they never learn from their mistakes—each retrieval is static, each query answered the same way it was yesterday. This project is one I did in my free time to explore this space.
What I Set Out to Build
- Persistent memory that remembers decisions, sentiment, and commitments without drowning the model in tokens.
- Strategic adaptation that learns which retrieval patterns succeed, which queries clarify intent, and which contexts drive better responses.
- Trustworthy retrieval so every answer comes with an evidence trail your compliance team can audit.
- Lean operations that a startup can run on day one and scale with customers without rewriting the core.
Architecture Pillars
Hybrid retrieval. I paired lexical GIN indexes with ANN vector search and structured predicate catalogs. That combination delivers business breadth (millions of snippets) without graph latency.
Signal intelligence. I designed LLM-driven extraction pipelines to capture loyalty signals, risk flags, and intent—stored as typed facts with provenance so downstream agents can reason over them.
Context optimisation loop. I architected alternative query generation, cosine re-ranking, and chunk window tuning to keep prompts slim while preserving relevance for GPT-series models. The system tracks which query reformulations yield higher user engagement, then weights successful patterns more heavily in future retrievals.
Strategic memory layer. Beyond storing facts, I built a reasoning pattern repository that captures which approaches solved similar problems before. When the assistant encounters a familiar task type, it retrieves not just data but proven strategies—reducing trial-and-error and improving consistency across sessions.
Delivery in Practice
- Specification-first. Every API, job, and handler we shipped came with type hints, formatting constraints, and pytest coverage so the team can scale safely.
- 90-day go-to-market plan. I kept architecture, product milestones, and fundraising targets in a single roadmap that aligned founders, design partners, and investors.
- Competitive validation. I benchmarked Supermemory, Mem0, and Zep to back my choices with research—not hype.
Evolution at Runtime
The system doesn't just recall—it learns. I designed feedback loops that track retrieval effectiveness: when users accept suggestions, refine queries, or switch topics, those signals update retrieval weights and strategy rankings. Over weeks, the assistant discovers which memory types matter most for each user, which query expansions clarify ambiguity, and which reasoning chains close tasks faster. This test-time evolution means the product improves without retraining base models or waiting for the next deployment.
Result
LLM Memory isn't just a feature; it's the backbone that lets AI assistants stay coherent and get smarter over time. By combining rigorous retrieval science with adaptive strategy learning and pragmatic engineering, I architected a memory fabric that evolves with usage—something enterprises can trust and startups can actually ship.