Do we need RAG retrieval too?

They complement each other. RAG retrieves relevant text; a knowledge graph adds the entity relationships that text alone misses. Many life sciences use cases want both.

Where does the graph get its data?

From your governed sources and, ideally, a resolved Single Customer View, so the entities in the graph are the trusted ones the rest of the business uses.

AI-Ready Data

Let agents reason over real relationships, not just similar text.

Build a knowledge graph that captures the entities and relationships in your data, accounts, HCPs, products, sites, studies, so retrieval is entity-rich and an agent can follow real connections instead of guessing from text similarity alone.

Book an initial consultation Start with a Data Audit

Why does our AI miss the obvious connection between an account, its HCPs, and its activity?

Pure text retrieval finds passages that look similar, but it doesn't know that an HCP belongs to an organization, prescribes a product, or sits in a territory. A knowledge graph encodes those entities and relationships explicitly, so retrieval and reasoning follow the real structure of your business, which is exactly what entity-dense life sciences data needs.

What's included

Knowledge graph model

A graph model of the entities and relationships that matter in your domain, designed around the questions agents need to answer.

Knowledge graph build

The build that populates the graph from your sources and keeps it current as the data changes.

Entity-rich retrieval

Retrieval wired to traverse the graph, so an agent can follow relationships, not just match text.

Grounded reasoning

A structure agents can reason over with provenance, the entity backbone behind a trustworthy assistant.

How it works

1
Model the domain

We design the graph model of entities and relationships around the questions that matter.
2
Build the graph

We populate the graph from your sources and set up the refresh that keeps it current.
3
Wire retrieval

We connect graph traversal to retrieval so reasoning follows real connections.

What you walk away with

A knowledge graph model of your domain's entities and relationships
A populated graph kept current from your sources
Entity-rich retrieval that traverses real relationships
A reasoning backbone agents can ground answers in

Frequently asked

Do we need RAG retrieval too?: They complement each other. RAG retrieves relevant text; a knowledge graph adds the entity relationships that text alone misses. Many life sciences use cases want both.
Where does the graph get its data?: From your governed sources and, ideally, a resolved Single Customer View, so the entities in the graph are the trusted ones the rest of the business uses.

Give your AI the real structure of your business

Book a consultation to build the knowledge graph that makes retrieval and reasoning entity-aware.

Book an initial consultation Start with a Data Audit

Where this leads next

RAG Retrieval

Pair the graph with grounded text retrieval for the fullest context.

Explore the project

MCP Data Servers

Expose the graph and retrieval to agents through governed MCP servers.