GroundingNodes
Your agents aren't failing. Your context is.
The model was never the problem. The knowledge your agents need lives in your people's heads and a thousand documents. It's the way your accounts, decisions, and risks actually connect, and no system has ever written it down. We turn it into a live map your agents can reason on. GraphPulse is how. That's the difference between a demo and a system that survives production.
It worked in the demo. You already know what happened next.
95%
of organizations are getting zero return from GenAI investment, according to MIT research
fortune.com8 in 10
companies cite data limitations as a roadblock to scaling agentic AI, according to McKinsey
mckinsey.comEvery number on this page resolves to its source.
Not a model problem. A context problem. Which means it's fixable.
The insight
Your business already has an ontology. You've just never written it down.
An ontology is the shape of how your company thinks: what you treat as a thing (accounts, deals, decisions, risks) and which of those are allowed to connect to which. Your experienced people run on that shape every day. They know a decision isn't real until someone owns it, and which risks a deal can survive. Your software stores the documents that shape produced. It doesn't store the shape. So we work with the people who hold it, the processes documents that define it, with every connection pointing back to the page it came from.
That's what grounded means. Your agents reason on the shape of your business, not on a pile of text.
Two ways in. One thesis underneath.
Trust-critical answers
For organizations whose agents must be right and provable: public sector, regulated services, anywhere a wrong answer has consequences. Every answer carries its source. The agent doesn't just tell you. It opens the document it answered from.
How engagements workAgentic scale
Grow accounts without growing the team, with agents that actually understand your company. They understand it because we modeled it first.
How engagements workWhat we build
01
Assess
AI opportunity diagnosis on your real workflows, so you know where grounded agents pay back first.
02
Model
We author the shape of your business with the people who hold it, the process documents that define it, then bind it to the systems that already store the data. It's the step everyone skips, and the reason most agents fail.
03
Orchestrate
Multi-agent workflows that reason over your map, not just respond.
04
Ship
From scoped POC to production system in weeks, not quarters.
05
Run
Drift detection, evaluation, and governance, so the map and the agents stay current.
The substrate
The map builds itself.
Three graphs. The semantic graph is the shape of your business — what you treat as a thing, which of those may connect to which, and the system, table and column that record each one. Your data stays where it is; the agent learns how to read it. The process graph is how work actually runs: the steps, the conditions that pick the next one, and who a case escalates to when it reaches the limit of what the process can settle. The activity graph is the running order of what happened — events, decisions, and the corrections that followed.
Two things fall out of that. Because escalation is modelled, an agent that reaches the edge of what it may decide hands off instead of guessing. And because corrections land in the activity graph, the map learns from being wrong. You author the first two once; the third fills itself in from what your business actually does.
This is a representative map. In a working session, we start building yours.
Read about GraphPulseA representative map. Three graphs, from our practice.
Hover or focus a node to trace its connections. Select a graph to see what it holds, where it comes from, and what it unlocks for agents.
Your data. Your map. Yours to keep.
Deliverables, not subscriptions.
Most agent platforms have a quiet catch: the knowledge base lives inside their product. Every document ingested and every workflow learned deepens their moat, not yours. Cancel the subscription, and the understanding of your own business goes dark.
We build the other way around. The ontology, the agents, the pipelines: all of it is a deliverable, not a subscription. We deploy it the way you want it, document it, and hand it over. Change vendors later, change models, change your mind. The map of your business goes with you, because it was always yours.
Work in motion
What this looks like in practice
A public-sector answers platform. Citizens ask which rules and schemes apply to them, and the agent answers from the regulations themselves, then opens the documents it drew from.
A services firm going agentic. Every off-the-shelf agent they tried failed the same way: it didn't understand their business. We model how the firm works, then stand agents on it. Growth without hiring in proportion.
A bank operations desk. The agents reason over current processes, regulations, and internal rules, and they advise the people working tickets, source attached.
The organizations that close the context gap in 2026 will have a compounding advantage their competitors can't buy back in 2027. Every quarter of pilot purgatory is a quarter of decisions your agents didn't learn from.
From the Knowledge Hub
The thinking behind the practice
We publish the frameworks we build with — including the three graphs an agent-first enterprise runs on.
Bring one workflow to a working session.
In the session, we work one of your real workflows with the people who run it. You watch the shape of it come out on the board, and an agent reason on it. Then we scope a POC, where the first deliverable is your semantic and process graphs, authored with your team, inside the first two weeks.
Book a working session