We make your data work with AI.
Same prompt. Same answer. Every time?
SEAM is the intelligence layer between your AI and your data - semantics, context and governance, in one.
AI agents are querying your live systems right now. Increasingly, they're acting on them too. Different teams, different agents, different definitions. The question isn't whether they'll keep doing this - it's whether they're all getting the same answer and taking the same action.
Same question, different systems, conflicting answers.
"How many active customers do we have?"
→ CRM agent returns 3,124
"How many active customers do we have?"
→ Billing agent returns 2,471
"How many active customers do we have?"
→ Warehouse agent returns 2,893
Same question. Three agents. Three answers. No audit trail.
"How many active customers do we have?"
→ 2,471 active customers
resolved · canonical: Billing · audit logged
What SEAM did
SEAM intercepted the question and matched it against the active customer definition contained within your intelligence layer. Billing is the canonical source for that metric, so its answer wins; CRM and Warehouse stay on standby as ranked fallbacks. Every step of that decision - the match, the canonical source, the verdict - is written to your Audit Log automatically.
When agents resolve through SEAM, the difference is measurable.
A head-to-head benchmark against raw vendor MCPs on the same questions, the same data, the same model. SEAM was more accurate, cheaper, faster.
correct answers
vs 44% on raw vendor MCPs across the same question set
cheaper to run
and 1.5× faster than direct-MCP wiring on the same workload
on defined-metric questions
Correct 4× more often than the ungoverned baseline (vs 21%); flagged the relevant caveat 4× more often (vs 21%).
Read the full detailed benchmark report or ask us more in a demo.
Map your intelligence. Operate it. Audit it.
SEAM is not a tool to bolt on. It is the layer your AI reasons through. Three components, one journey.
Map your intelligence.
Canvas in the SEAM Console maps your business onto a single zoomable graph - your metrics (e.g. revenue), your entities (e.g. customer), the data sources behind them and the connections to reach them. One map, edited in the Console or in your IDE, versioned in Git.
Operate it.
Runtime resolves every agent call - read or write - through your map. Same prompt, same answer. Same governance for the action that follows.
Audit it.
Audit Log records every call - read or write - with a verdict: governed, ungoverned or resource-gap. Live in the Console; full history in BigQuery.
Trusted intelligence enables trusted action.
Once the intelligence layer is in place, the same definitions govern every action your agents take. The Customer your agent answers about is the Customer it updates. The Campaign in your dashboard is the Campaign it pauses. Every action inherits the read's governance - same definition, same verdict, same audit log.
Update HubSpot. Post to Slack. File a Jira ticket. Send a Gmail. Run a SQL update. Same intelligence layer either way.
See your intelligence layer.
SEAM Canvas is the visual map your team authors and the runtime resolves through. Built collaboratively in workshops or directly in code. Lives in your Git repository.
The product spin-out of a 12-year consultancy.
Measurelab has been doing the data-governance work behind the scenes for Google, Salesforce, BDO, BFI, S&P Global, AJ Bell, Marqeta, Charles Stanley, EDF Energy, Pret A Manger, Visit London, the University of Exeter and dozens more - work that taught us why agents need an intelligence layer. SEAM is how we work now, productised.
The intelligence layer between your AI and your data.
Centralise governance. Decentralise access. Build trust in your AI agents across every interface.