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GraphRAG and AI agents grounded in your data

Vector search finds text that looks similar. A knowledge graph knows how things are connected. GraphRAG uses both, so a language model can answer multi step questions, cite where facts came from, and respect the structure of your business. We design and build these systems end to end, from graph to agent to user interface.

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When this is the right service

  • Your RAG chatbot answers simple questions but fails on anything that needs two or three hops.
  • The model confidently invents relationships that do not exist in your data.
  • An AI agent must only act within permissions, hierarchies and policies that live in structured systems.
  • Stakeholders ask why the AI said what it said, and nobody can show the path.

What we do

  1. 1

    Use case and evaluation design

    A concrete question set with expected answers, so we measure accuracy from day one instead of demoing.

  2. 2

    Graph construction

    Entity and relationship extraction from documents with LLMs, merged with your structured sources, validated against an ontology.

  3. 3

    Retrieval design

    Hybrid retrieval: graph traversal, SPARQL or Cypher generation, vector search over text, and ranking that combines them.

  4. 4

    Agent and tool design

    Agents with tool calling and MCP servers that read and write the graph safely, with guardrails from the schema.

  5. 5

    Production and monitoring

    Latency and cost budgets, evaluation in CI, observability of every answer and its sources.

Tools and standards

  • Claude, OpenAI and open weight models
  • LangGraph, custom agent loops, MCP
  • Neo4j, GraphDB, pgvector, Qdrant
  • Text to SPARQL and text to Cypher
  • Python and TypeScript
  • Evaluation harnesses and tracing

What you get

  • Evaluation set and baseline
  • Knowledge graph built from your sources
  • Retrieval and agent service with API
  • User interface or integration into your tools
  • Monitoring and cost report

Frequently asked questions

Do we need a knowledge graph before starting with AI?+

No. We often start by extracting a first graph from the documents you already have, measure the gain over plain vector RAG, and grow the graph where it pays off.

Which LLM do you use?+

The one that fits your accuracy, privacy and cost needs. We build model agnostic and have shipped with hosted frontier models and self hosted open weight models.

How do you measure success?+

With an evaluation set agreed with you before building: answer accuracy, citation correctness, latency and cost per query. We report on it every iteration.

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Tell us about your data, your product or your AI use case. We reply within one business day with honest first thoughts.

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