Where SEAM fits today.

Numbers come from Google Analytics, HubSpot and Salesforce Marketing Cloud. Copilot got rolled out across the team last quarter. Same prompt by three analysts gets three answers. Every session starts cold: re-explaining what "active customer" means, which attribution window applies, what the campaign IDs are.

With SEAM: campaign, customer and conversion definitions are governed at the team level. Copilot resolves through the same map every time - whether answering a question or pausing a campaign. Marketing stops disputing its own numbers, and every answer (and every change) is traceable back to a definition and a source.

Claude or GPT wired to your data via MCP. Some queries work. Many burn tokens discovering tables, then guess wrong. Your eval bench shows wild variance for what should be the same question.

With SEAM: the discovery loop is short-circuited by an entity layer that already knows where things live. Same prompt, same answer - and the same governance for any action the agent takes after. Every call lands in the audit log with its verdict, source and matched definition - your eval bench has a baseline that doesn't drift.

Risk, finance and customer teams point AI agents at the same question and get different numbers. Counterparty exposure, lifetime value, regulatory capital - the definitions sit in spreadsheets and senior analysts' heads. Compliance can't explain to the regulator how any answer was produced.

With SEAM: definitions are versioned, owned and explicit. Every agent resolves through the same map. The audit trail records what was asked, what was answered and which source won - so disputed numbers have a documented path back to a definition, an owner and a moment in time.

Your product surfaces customer data through AI features. Each customer instance has its own definitions, schemas and entitlements. The same prompt across two customers returns answers shaped by different conventions. Enterprise customers are asking how the answers are produced.

With SEAM: a customer-aware governance layer between your AI features and the underlying data. Same prompt, same shape per customer, with permissions, definitions and lineage explicit. Each customer's audit trail traces every answer (and every action your AI takes on their data) back to a governed definition - ready when their internal review asks.

Student records in SITS. The VLE in Moodle or Canvas. Finance in Unit4. CRM somewhere else. Recruitment, registry and student-experience teams ask agents the same question - "how are first-years progressing?" - and get different answers. The OfS wants a story about how AI is being used; nobody can tell it cleanly.

With SEAM: "applicant", "active student", "at risk" defined once, applied everywhere. Every agent resolves through the same map. The audit trail records every answer with its source and definition - ready for any internal review or data-protection enquiry.

Customer service, marketing, finance and merchandising each have an AI agent, hooked into the e-commerce platform, Klaviyo, Google Analytics and the warehouse. The same question - "which products are returning above rate?", "what's the LTV on the new range?" - gets different answers per agent. Returns logic, attribution windows and customer cohorts have drifted.

With SEAM: product, customer, order and channel definitions live once. Every agent resolves through the same map. The Black Friday post-mortem reads identically to finance, merch and your retained agency - and every disputed number has a documented path back to a source.

Pupil progress in Arbor. Wellbeing in Boost Insights. Notes scattered across OneDrive. Teachers were given Copilot last term. "How is John Smith getting on in reading?" - Copilot guesses: wrong term, wrong source, sometimes data the teacher shouldn't see. Most teachers stopped asking.

With SEAM: a governed map of the school's data, permissions respected. The teacher asks; one accurate answer comes back from the right source. Every call logged - so the trust can show, when asked, how AI is being used and what data it's touching.