Overview
Where Hippocampus sits in an AI stack
Models reason, agents act and people decide. Between them and a company's documents sits Hippocampus: its services ingest, curate and retrieve, and its secure OKF wiki keeps every document as encrypted Markdown behind one set of APIs.
Identity and keys
Where knowledge lives
Hippocampus
Who asks
Models it calls
model calls live AI services any language modelWhere it runs
Hippocampus runs on our cloud or on your own hosting. The product is the same in both.
- Hippocampus cloud
- Hosted and run by us in the EU. Get access, and the API key is in the dashboard.
- Own hosting
- Your own cloud, on-premises or any other environment you control. Part of the Enterprise package.
The API and MCP are identical on both. Only the base URL differs.
Own hosting is set up with us as part of Enterprise: talk to us about Enterprise
Architecture
An external mind palace
A new mental function for LLMs: an external, layered memory that AI creates and maintains, shared with the people who use it.
Hippocampus gives AI and humans a shared, well-organised external memory. It is built like Wikipedia, an intranet on steroids: complex data is summarised into short abstracts with deep links to the text they stand for.
The memory is hypertext, which is a core competence of LLMs. LLMs create and maintain it as a modern wiki, and the same pages are the ones people browse and check.
The search starts from titles and links. It follows a link to a summary when a title matters, and opens the full text only for the passage the question needs, without a generative model. The model receives only that passage, so the context stays small however large the corpus grows.
- 1 Titles and linksOne line per document or section, each one a link.
- 2 SummariesShort abstracts with deep links into the text.
- 3 Full textThe document as Markdown, with its pages and byte ranges.
Core properties
- Scalable to huge datasetsThe model gets a small, relevant context: only the passages that matter, found by the search, rather than the corpus. The size of the corpus stops being the limit.
- Built-in quality controlQuality control is part of the memory, not a separate project, and every summary can be rebuilt from the source.
- Open by designAnswers are traceable and accountable: each one leads back to the passage, the page and the original file it came from.
- Human in the loopPeople work in the same wiki the AI builds. They browse it and check it, and cases that need judgement are sent to a person: a core feature, not an afterthought.
- Role-based securityAccess follows roles and permissions, so what an answer is built from is limited to what the person or agent asking may read.
Document tree
Here is the search on one document. Two funnels run over the tree built at import: one walks down from the index summary through the section summaries that match the question, the other searches the leaf sections directly with keywords and embeddings. Their results are fused into one ranking, without a generative model.
- 1 3.1 Encryption
Reached by both funnels: the path through 3 Security measures and the leaf search. - 2 4.1 Sub-processors
Leaf search only: the passage names the processor's hosting provider.
Import
| File | What happens |
|---|---|
.pdf | Text recognition with page markers, so scanned pages work. The original is kept. |
.docx | Converted to Markdown directly, without text recognition. |
.md · .markdown · .txt · .text | Read as-is with page markers added. There is no PDF original for these. |
A file that was already imported is flagged as a duplicate. Progress can stream while the document is read.
Models
- Answer models
- Any language model. You choose which model answers, set for each workspace; on Corporate you can use your own model key. In the web chat a person can pick the model per question. Changing the answer model needs no re-import.
- Embeddings
- Fixed for a workspace: changing the embedding model would invalidate the stored vectors, so it is not offered.
- Model access
- Included in the service. No provider account and no provider key to set up.
Open Knowledge Format
Every document is stored as a folder of Markdown with YAML front matter, following the Open Knowledge Format specification: a summary of the whole document, the full text, the original file, and a folder per section down to the leaf sections. Any document's Markdown can be read through the API.
contracts/
└── data-processing-agreement/
├── index.md summary of the whole document
├── data-processing-agreement.md full text as Markdown
├── data-processing-agreement.pdf the original (none for .md or .txt uploads)
└── 03-security-measures/
├── index.md summary of the section
└── 03-01-encryption.md a leaf sectionAPI reference
Every endpoint, parameter and response is described in a separate API reference.
API reference