Turn your company's data and files into a structured knowledge graph your agents, copilots, and LLMs can query directly with every answer traceable back to the source.
Building reliable AI on top of company data is harder than it should be.
RAG pipelines hallucinate. Vector search misses relationships. Every new project rebuilds the same ingestion logic. And when something goes wrong, there's no audit trail.
Your data lives across spreadsheets, PDFs, CRM dumps, and system exports never in the clean, queryable format your agents actually need.
Lontiq solves the context layer problem: turning your existing files into a structured, relationship-aware knowledge graph your AI systems can query with confidence.
Upload your files — CSV, Excel, TXT, PDF, JSON, and XML — and Lontiq builds a knowledge graph: a structured web of relationships between your suppliers, projects, clients, products, and teams. Connect your own systems via API to query the graph directly.
Every node is a fact from your data. Every edge is a real relationship. Nothing is hallucinated. When you ask a question, you get an answer you can trace back to the exact cell, row, or page it came from.
Create a Space to organise your team's data and give your AI the right context boundary. Spaces control what your agents can see and query.
CSV, Excel, TXT, PDF, JSON, and XML. Lontiq reads them and builds a structured knowledge graph automatically, extracting entities, properties, and relationships. Direct connector ingestion coming soon.
Ask in plain language or connect via API so every answer links back to the source with a full traceable path from question to answer, and an audit trail of every step the agent took.
A Space is a data boundary. It controls which files, entities, and relationships your agents can access. Assign users and AI agents to Spaces to enforce visibility rules — so every query only returns data the requester is authorised to see.
Use Lontiq to create Context Spaces structured, relationship-aware knowledge layers that your AI solutions can consume directly via API. Connect your AI agents, power your copilots, and plug into your MCP server all backed by context that updates as your data evolves.
AI chatbots generate plausible-sounding answers. Lontiq returns facts from your data with a traceable path back to the source and the logic for generating the answer. If an answer says "Supplier X is 14 days late on PO-0847," you can see the path from question to answer.
Search tools find documents. Lontiq understands how entities in your data relate to each other and can answer questions that span multiple files.
The knowledge graph is built deterministically from your data. There's no generative step making things up. If it's not in your files, it won't appear in an answer.
Most context layers use vector embeddings alone. Lontiq uses a real property graph with typed relationships, traversal, and Cypher queries. That means your agents can answer multi-hop questions that RAG simply cannot.
I kept watching teams lose entire afternoons reconciling Excel files, PDFs, and system exports just to answer questions their own data could already answer.
The problem isn't a lack of data. It's that critical information lives across disconnected files that were never designed to work together. Even when the answer exists, it's buried behind hours of manual lookup.
Lontiq removes that friction. It structures your existing files into a context space that allows you to get answers grounded in your data and fully traceable both to the source and the logic that generated the answer.
If you're building AI on top of messy internal data and running into reliability and traceability problems, I'd genuinely like to hear what you're running into. Let's talk.
— Manuel, building Lontiq from Lisbon
Full REST API reference, MCP integration guide, and code samples to get started.
View DocumentationWe'll walk you through a live demo using files like yours not a canned presentation.