Lontiq makes your company AI-ready
Welcome to Lontiq
Lontiq transforms your existing company data into AI-ready context. Documents, spreadsheets, emails, internal tools, websites, etc. Your data is scattered across multiple tools and platforms. By connecting to the data your company already has, Lontiq turns it into structured knowledge graphs that your AI workflows can consume directly. No rebuilding your workflows. No switching tools. Just plug in your existing data sources and Lontiq handles the rest.
How Lontiq works
- Create your Context Space: Create your space and define its scope: Company-wide, team-specific, or use-case-specific.
- Feed your data: Feed Lontiq your data via API in whatever format it lives in: spreadsheets, PDFs, JSON, XML, markdown, plain text, etc.
- Automatic ontology: It automatically maps out the relationships between information rather than treating each file as a standalone chunk.
- Retrieve your context: When your AI needs context, it queries Lontiq and gets back exactly what it needs.
Why Lontiq matters
Traditional RAG systems retrieve documents based on similarity. They return relevant text fragments but do not understand how entities relate across your data. Lontiq builds a structured context graph and retrieves information through graph traversal not just similarity ranking. This enables relationship-aware, multi-hop retrieval across your operational data. Multi-Hop Graph Retrieval Queries don't stop at directly matching entities. Lontiq traverses relationships across multiple hops to surface connected context — suppliers linked to contracts, contracts linked to invoices, invoices linked to payment terms. This allows: Discovery of indirect dependencies Cross-file reasoning Context expansion beyond isolated snippets Your AI doesn't just retrieve text. It navigates structure. Retrieval Trace Each query includes a transparent retrieval trace: Which entities were selected Which relationships were traversed How many hops were expanded How results were ranked This makes context assembly inspectable and auditable. Source-Linked Responses Every returned fact is linked to its original source — file, record, and location — so answers can be verified and challenged. Core Capabilities Relationship-aware retrieval Returns connected entities, not disconnected fragments. Multi-hop context expansion Discovers dependencies and patterns across structured links. Adaptive company memory Reflects changes in underlying data continuously. Unified access layer The same graph-backed context powers chat, APIs, and internal systems. In Short
Lontiq transforms scattered operational data into a structured, traversable knowledge graph enabling AI systems that are explainable, auditable, and capable of multi-hop reasoning over real company relationships.