Why

Semantic governance without centralisation.

SEAM doesn't force your data into a new silo. It acts as the intelligent proxy that translates natural language intent into governed execution — wherever your data already lives.

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No replatforming

SEAM works whether you have a warehouse, are building one, have one that needs rescuing, or have decided you don't need one. It governs whatever you have, wherever it lives.

Existing investment protected

If you've already invested in a warehouse and a semantic layer, none of that is wasted. SEAM governs it alongside everything else — making that investment pay off in an agentic world.

Definitions as infrastructure

Version-controlled, reviewed, tested and owned. The same rigour you apply to code, applied to the meaning of your data. Governance that improves over time, not documentation that rots.

SEAM governs structured and unstructured sources — but honestly about the difference. For databases and warehouses, governance is deterministic: every metric resolves to a validated query, every answer traces to a governed definition. For unstructured sources — Slack channels, documents, emails, meeting transcripts — SEAM applies source hierarchy and context weighting, ensuring agents know which sources to trust and how to prioritise conflicting information. Both are governed. The mechanisms are different. Having both in one layer is a capability no warehouse has ever offered.

What

More than a glossary. The full reasoning context.

SEAM encodes everything an AI agent needs to give correct, consistent, auditable answers across your organisation.

Metric definitions

What "revenue", "active customer" and "churn rate" actually mean here. In this business. With these edge cases. Context-specific variants for different teams.

Source hierarchy

When two systems disagree, which one wins and why. When the warehouse has a modelled figure and a live system has a different one, which takes precedence for which question.

Entity resolution

How a record in one system maps to a record in another. The connective tissue between systems that were never designed to talk to each other.

Business logic

Fiscal year boundaries, attribution models, segmentation rules. The stuff that lives in someone's head until they leave the company.

Temporal context

When definitions changed, which version applies to which period. Because "active customer" might have meant something different before the pricing restructure.

Audit trails

Every governed answer carries a trace: which definitions were applied, which sources were queried, which resolution path was taken. Full explainability.

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Tool call proxy

Sits between the agent and its data sources. Every request is checked and validated before it reaches your systems.

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YAML-based logic

Define your metrics once in human-readable YAML. Every agent uses the same definition, regardless of the model behind it.

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Complete audit trails

Complete lineage of every agent interaction. Know exactly which agent called which tool, with what parameters, and the result returned.

How

The SEAM engine architecture

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Definition files (YAML)

The source of truth. Version-controlled, human-readable schemas that define tools, metrics, and access policies.

metrics:
  mrr:
    sql: "sum(revenue)"
    filters: ["status='active'"]
schema

Compiler / validator

Validates every definition against its schema before deployment, catching errors early.

Context resolver

Intelligently maps what the agent is asking for to the right governed definition.

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MCP proxy

SEAM is a Model Context Protocol proxy. It sits between your AI agents and every data source — intercepting, governing, and auditing every tool call before it reaches your systems. Not just another MCP server. The mediation layer that makes every other tool call trustworthy.

TRACE_LIVE

Audit logger

Full execution trace. Every prompt, every call, every result recorded for governance.

Engineered query flow

Input stage

User question

"What was the total ARR for enterprise customers in Q3?"

Agent intent parsing

LLM generates tool requirements based on natural language prompt.

mediation
Mediation layer

SEAM resolution & logic binding

Metric: ARR
Resolved to: sum(rev) WHERE type='ENT'
Auth: Validated
Scope: Finance_Reader_v2

Proxied execution

The validated, governed query is executed against the secure endpoint.

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Output stage

Governed answer

Agent receives high-integrity data and provides an accurate, fully audited response.

Standards

Built on open standards. Extended where they stop.

SEAM's definition format is informed by the Open Semantic Interchange (OSI) specification — the emerging vendor-neutral standard backed by Snowflake, dbt Labs, Salesforce, and Databricks — and extended to cover source hierarchies and unstructured sources that OSI doesn't yet address. Your definitions are portable where the standard applies, and purpose-built where it doesn't.

Capability OSI SEAM
Metric definitions (SQL) ✅ Core focus ✅ Supported
Entity relationships ✅ Join paths ✅ Cross-system resolution
Source hierarchy ❌ Warehouse-only ✅ Any source, ranked
Unstructured sources ❌ Not addressed ✅ Context-weighted
Agent mediation ❌ Not in scope ✅ Core function
Audit trails ❌ Not in scope ✅ Full lineage
Temporal versioning ❌ Not addressed ✅ Definition history
Governance enforcement ❌ Definitions only ✅ Observe / Advise / Enforce
Why not just...

Existing semantic layers weren't built for this.

Tools like dbt Semantic Layer and Cube govern structured data within the warehouse query path. SEAM governs wherever data lives, through the agent.

"Why not dbt's MCP server?"

dbt exposes an MCP server — it lets agents query your semantic layer. SEAM is an MCP proxy — it sits between agents and every tool call, governing what gets through. dbt gives agents access to warehouse metrics. SEAM governs the access itself, across all sources, structured and unstructured.

"How is this different from Cube?"

Cube is a powerful semantic layer for SQL-based data. It governs queries to your warehouse. SEAM governs queries to everything — warehouses, APIs, documents, Slack, email. And it does it at the agent level, not the query level. Source hierarchy, context weighting, and audit trails apply whether the data is in BigQuery or a Google Doc.

Other tools govern data in the warehouse. SEAM governs the agent's entire view of your organisation.

Partner programme

Use SEAM in your organisation.

SEAM is in active development. We're working with a small group of forward-thinking organisations to shape the product and prove it in real environments.

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Early access

Get hands-on with SEAM before general availability. Deploy it against your own systems with direct support from the team building it.

  • check Priority access to new capabilities
  • check Direct line to the engineering team
  • check Influence the roadmap
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Co-development

Bring your governance challenges and we'll work together to solve them. Your use cases directly shape how SEAM evolves.

  • check Joint definition of governance schemas
  • check Custom connectors for your stack
  • check Shared learnings across the cohort
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Design partner

For organisations ready to go deep. Embed SEAM into your agent infrastructure and help define what governed AI data access looks like.

  • check Dedicated onboarding and integration
  • check Preferential pricing at GA
  • check Named case study collaboration

Join the partner programme

Register your interest and we'll be in touch.