Hippocampus
AI works for individuals.
It breaks at organizational scale.
Most enterprise AI efforts stall in the same place: confident but unverifiable answers, quality that collapses with volume, and knowledge locked into a single vendor's stack. Without an organizational memory you actually control, AI stays a promising demo.
Individual
Every person's AI is its own island. Knowledge does not transfer across people or services.
Limited
No model can hold an entire organization's knowledge in context at once.
Doesn't scale
More data means exponentially higher costs, and answers that get worse, not better.
No human in the loop
AI answers are hard to trust, and harder still to know when to verify.
An external memory layer for organizations, built and maintained by AI.
Documents and connections go in. The AI reads them, summarizes them, and links them into a wiki your team can browse, edit, and trust. Any modern model works: Claude, GPT, Gemini, or open weights.
Instead of brute forcing raw files on every query, Hippocampus understands your documents once, then reuses that understanding every time. Answers come back faster, cheaper, more accurate, and verifiable to the source.
Every company brain runs on memory.
"The biggest blocker to AI automation of companies is no longer the models. Now the blocker is the domain knowledge."
The company brain is the idea everyone is chasing: one system that knows what your whole organization knows and reasons with it. The reasoning was never the hard part. Modern models already reason well. The missing piece is a memory the brain can think from, one that is structured, verifiable, and shared across every team.
That memory is Hippocampus. It turns your documents into a knowledge layer any model can read and trust, so a company brain reasons from what your organization has actually learned instead of whatever fits in a context window. We call the system that builds and compounds that memory a knowledge factory.
Ingest. Organize. Access.
Ingest
Any documents go in. Contracts, reports, transcripts, code, emails, policies, etc.
Organize
AI reads them once and builds a layered memory: source → summary → index. Tunes itself over time.
Access
Queried by any AI or human. Every answer links back to its source, fully traceable.
"Room for an incredible new product instead of a hacky collection of scripts."
OpenAI cofounder Andrej Karpathy described the same pattern: an AI building a wiki, another AI reading and updating it, humans rarely editing by hand. Hippocampus brings that idea to enterprise scale.
Three things, on every query.
Scalable information
Knowledge is organized in layers: source, summary, index. The model reads the right depth without exploding costs.
Factual, traceable answers
Every response links back to its source. Auditable, not black box.
Built in quality control
AI agents propose and peer review; humans validate the high stakes calls. The 95/5 split that makes the system reliable in production.
Your keys. Your data. Sealed even from us.
Hippocampus runs on your infrastructure: on premise, in your cloud, or fully offline. Any modern LLM plugs in, cloud or self hosted. Switch models tomorrow without rebuilding your knowledge layer.
Access is controlled at every level: each user, and each AI agent, only sees what they should. Guiding principle: you can't leak what you don't know.
See grounded, traceable answers on a corpus you actually care about.
Bring a representative slice: a department's documents, a regulatory archive, a set of contracts. Add a handful of questions you wish AI could answer reliably. We'll show you what fit looks like within a few conversations.
Common questions about Hippocampus.
Hippocampus is an external memory layer for organizations. AI reads, summarizes, and connects your documents into a knowledge layer your team can browse, edit, and verify. Any modern AI model can query it. Every answer links back to its source.
Built on proprietary technology, not a thin wrapper over an LLM.
A company brain is the goal: one system that knows what your whole organization knows and reasons with it. Hippocampus is the memory that makes it possible.
Models supply the reasoning. Hippocampus supplies the structured, verifiable, shared memory they reason from, built once from your documents and compounding over time. Without that memory layer, a company brain is just a model guessing from whatever fits in its context window.
RAG retrieves chunks of raw text at query time. The model processes them on every request. Costs scale with each query.
Hippocampus reads your documents once, builds a layered memory of source, summary, and index, then queries the right layer at the right depth. The model never needs to re-process the underlying files. Answers are faster, cheaper, and more consistent.
RAG is a retrieval pattern. Hippocampus is a memory architecture.
A vector database stores embeddings. That is a storage layer. You still need to decide what goes in, what gets queried, and how results are used.
Hippocampus is the layer above the storage: ingestion, summarization, multi-level indexing, source traceability, and self-tuning over time. The vector store is one possible substrate Hippocampus sits on top of.
Vector database is infrastructure. Hippocampus is the system.
Yes. Hippocampus runs on your infrastructure: on-premises, in your private cloud, or fully offline. Your keys and your data stay under your control, sealed even from us.
Access is controlled at every level. Each user and each AI agent sees only what they should. Guiding principle: you cannot leak what you do not know.
Any modern AI model. Claude, GPT, Gemini, or open-weight models like Llama and Mistral. Cloud-hosted or self-hosted.
You can switch models without rebuilding your knowledge layer. The memory architecture is decoupled from the model.
Most enterprise AI systems re-process raw documents on every query. The same files get ingested again and again. Tokens compound.
Hippocampus reads each document once, builds a layered memory of source, summary, and index, then queries the right depth for each question. The model gets relevant context, not the entire raw file.
The result is roughly 90% fewer tokens consumed for equivalent answer quality.
Every answer links back to its source. Auditable, not black box.
AI agents draft and peer-review responses. Humans validate the high-stakes calls. The split is roughly 95/5: AI handles the volume, humans handle the consequence.
Answers without verifiable sources do not ship.
