Stop treating every chat like a first date. Aletheia gives your AI a persistent, evolving brain, a cognitive architecture that learns who your users are one interaction at a time.
Vector databases are giant warehouses of static text. They find words, but they do not understand life. They lose context, ignore the passage of time, and drown in their own noise.
Retrieves data from 2 years ago exactly like data from 2 minutes ago.
Simply matches keywords. It does not know the difference between a wish and a fact.
Stores every uh and um instead of the core truth of the conversation.
Understands the arrow of time. Newer facts naturally supersede obsolete ones.
Distills 1,000 words into 3 verified semantic facts. Efficiency by design.
Connects the dots between conversations to build a coherent world-view.
Raw chat logs are noise. Aletheia acts as a cognitive filter, distilling human rambling into a clean, queryable lattice of facts.
I used to love coffee, but now I only drink tea. Aletheia does not hallucinate your old preferences. It updates your profile in real time.
Our engine automatically discards greetings and filler, keeping only the high-value semantic facts that actually matter for personalization.
Our White Mercedes engine ensures your user's identity is not locked inside a single chat window.
Hey! I just bought a white Mercedes! What should I do first?
GPT-4o detects: User Ownership → Vehicle: Mercedes (White)
Fact Integration
What was that maintenance tip for my car?
Claude 3.5 recalls: "For your white Mercedes, I recommend..."
Engineered for builders who need more than just a place to dump text files.
Memory that follows the user, not the model. Switch from GPT-4 to Claude to Llama and Aletheia keeps the brain intact across every integration.
When life changes, Aletheia knows. If a user moves from NYC to LA, the old fact is marked as superseded so stale context stops leaking into answers.
Built with Rust as a single compiled binary. Deployment is fast and recall latency stays in the sub-100ms range without orchestration drama.
We did not just build a wrapper. We built a high-performance memory kernel from the ground up for safety and scale.
Ultimate memory safety and blazing-fast execution. No garbage collection pauses, just low-level performance where it matters.
No complex Docker chains. One file, zero configuration, instant memory synchronization across your stack.
Human-like recall speeds that keep up with your fastest LLM workflows without turning memory into the bottleneck.
Every layer from ingest semantics to production operations is implemented with one cohesive design system and runtime story.
Hybrid retrieval keeps exact tokens and semantic intent in the same decision path so answers stay grounded.
The engine models memory as something that changes over time, not a static bag of embeddings.
SDK and HTTP entry points are designed to keep local-first workflows aligned with hosted deployments.
The system includes practical controls for measurement, tuning, and safe production rollout.
The product has a clear progression: ingest fidelity, retrieval intelligence, and operational reliability.
Raw events are normalized, deduplicated, and expanded into durable memories with lineage.
Semantic and lexical candidates are fused, reranked, then filtered by temporal policy before response.
Teams deploy one memory engine surface from local bench runs to hosted multi-tenant workloads.
Join the next generation of engineers building agents that actually understand their users. Start your disclosure today and let your AI finally remember.