The answer is usually already in your data. It is just in five different places.
A single scientific question can touch a compound registry, an assay database, an electronic lab notebook, a folder of reports and a handful of public sources. Each system holds part of the answer. Joining them up is done by hand, again for every question, and the reasoning that connected them is rarely written down. AI agents hit the same wall: they can only be as good as the data they can reach and the relationships they can follow.
What we do
We model how the things in your domain actually relate to one another, connect that model to where your data already lives, and put one graph on top that every person and every tool in your organisation can use.
Map the domain
Working with your scientists, we write down the entities that matter and how they connect: a structure to the assays it was tested in, an assay to what it measures, a measurement to the mechanism it is evidence for.
Connect your sources, without moving them
Each relationship gets a resolver that knows where its data lives and how to fetch it. Your systems stay where they are; the graph reads from them. A new source plugs into an existing relationship.
Add the reasoning
Predictions, similarity, pattern matching and curated public knowledge become further kinds of link in the graph, each one carrying the evidence it was drawn from so it can be checked.
Open it to people and agents
The same graph is available to AI agents over MCP, to your pipelines over an API, and to people in a visual viewer. It runs in your cloud or on your own hardware.
A working example: ChemGraph
ChemGraph is a knowledge graph for chemical safety that we built on this approach. Its public demo is live.
- Built on public data. US EPA ToxCast/Tox21 assay results and AOP-Wiki adverse outcome pathways, linked by structure, assay, target and pathway.
- Shows its working. Enter a compound to see its likely targets. Every prediction opens up to the similar compounds, substructures and measured results behind it.
- Joins the dots. Pick a compound and an adverse outcome, and it traces the route between them through key events, targets and assays.
- Small enough to run anywhere. The whole demo runs on a single small server in under 1 GB of memory, the same stack an on-premises deployment uses.
Have data that should be answering more questions?
Tell us what your team is trying to find out and where the data lives today. We will tell you whether a graph would help.