The self-assembling knowledge graph
Spyndel turns documents into a living knowledge layer for AI, automatically organizing information, connecting related knowledge, and adapting as that knowledge is used.
No LLM makes the connections. Spyndel's own compact models find and check every one, on your hardware, so there's no token bill and nothing to pay as your library grows.
One loop, running on its own
- IngestDocuments become individual statements
- OrganizeStatements find their topics and categories
- ConnectRelated ideas are linked across documents, by Spyndel's own models: no LLM, no tokens
- RetrieveAI answers draw on statements and their connections
- AdaptWhat answers use shapes the next answer
Spyndel builds itself
Give it documents. Spyndel finds the statements in them, discovers the subjects they cover, and connects related ideas across documents. You never design a schema, tag anything or write extraction rules.
Self-constructing
Statements, topics, categories and the connections between them are built for you, from the documents themselves.
Verified connections
Links are made only when two statements are actually about the same thing, not just worded alike, and they are checked by Spyndel's own models, not a large language model. Every link carries a calibrated confidence score.
Statement-level precision
Search and links work on individual statements, and every statement keeps its document, page and section, with the text around it.
Connections that cost nothing to make
Other graph builders send every page of every document to a large language model to work out what's connected, and charge you for every token. Spyndel doesn't use an LLM to make connections at all.
in LLM tokens to find and check connections
less AI spend than the LLM-extraction graph tools we tested
to check 13,393 candidate connections on one GPU
Measured on 100 public NRC documents, October 2026, with Claude Sonnet 5 at AWS list price for every system that used an AI model. Spyndel's whole build spent $0.68, all of it on optional topic names and document summaries; none on connections.
Spyndel grows as knowledge changes
Add a document and it connects into everything already there, not onto the end of a list. As the collection grows, Spyndel reorganizes it.
- Connected on arrival. A new document's statements link to related statements across the whole library, and older statements gain connections to it.
- Reorganized as it grows. Rebuilding finds the topics again across everything, so new subjects get topics of their own.
- Cross-document insight. See where documents agree and overlap, and which ones cover the same requirement.
for a 400-page document to join the graph on one GPU
of statements in a single graph, tested on large engineering and regulatory libraries
Spyndel learns from use
When AI answers rely on a connection, that connection ranks higher next time. What proves useful rises; what never helps fades.
+4.2 points more of the right guidance found after 240 answered questions, measured on new questions worded differently. Nothing got worse. See the measurement
Answers with sources
Ask questions in plain language. Every answer cites the exact statements it relies on, and every citation is checked before you see it.
Plugs into your AI
A built-in Model Context Protocol (MCP) endpoint lets Claude, ChatGPT and other assistants search, walk the graph and pull full source attribution.
Sharper with every answer
Each answered question records which statements and connections it used, so retrieval improves the more your team relies on it.
Start building your graph today
The Community Edition is free to download and runs on your own machine. Need scale, support or a custom deployment? Talk to us about Enterprise.