Brief

Schema Landscape Map — a sample (fictional insurer)

A sample of what the interview produces — every system, owner, figure and name in it is fictional. It shows the shape of the map: the schema landscape, what needs to plug in, the disconnect register, complaints and opportunities, stage placement, and remediation options ranked.

Published

The interview is thirty minutes on a prospect's schema landscape (optional parts can extend it to forty-five). Within two business days it ends in a document of this shape: the systems that hold structure and meaning, what needs to plug in, where two systems disagree and what that is costing, a directional stage placement, and the remediation options ranked, with the parts the prospect can do alone marked as such. The map is the prospect's whether or not they go further.

1. Landscape map

Where structure and meaning are authored today
SystemWhat structure it holdsFormatOwner (role)Read by agents?
Policy admin (core)Policy, coverage, renewal, product codesSQL DDLPolicy Ops leadYes
CRM (Salesforce)Customer, household, contact preferencesJSON SchemaCRM product ownerYes
Warehouse (Snowflake)Claims, premium, actuarial aggregatesSQL DDLData platform leadYes
Content CMS (Contentful)Product copy, disclosure text, FAQsJSON SchemaMarketing opsNo
Partner exchangeBinding, endorsement payloadsXSDIntegration architectYes
Internal wikiAd-hoc field notes, FIBO-derived ontology snippetsWiki pagesClaims Ops and Legal both edit the FIBO-derived snippets; last silent overwrite: March. 3 agent answers used the overwritten FIBO sense in April; tickets INC-4412, INC-4489, INC-4510 (fictional).No

2. What needs to plug in

  1. When a renewal quote is requested, the agent needs to resolve the insured party and active coverages across CRM and policy admin, so that the quote uses one identity and one product code set. Source: policy admin. Consumer: quote agent. Direction: pull, through connectors.
  2. When a claims intake bot classifies a loss, the agent needs validated coverage semantics from the Core Model, so that routing matches the conformance contract. Source: warehouse and policy admin. Consumer: claims agent. Direction: pull, through the MCP endpoint.
  3. When product copy changes in Contentful, downstream agents need a change log of term and disclosure updates, so that answers stay aligned with filed wording. Source: Contentful. Consumer: advisory agent. Direction: push on change.

3. Disconnect register

Where two systems describe the same thing differently
IdThe two systemsThe disagreementStated costOwner
D1Salesforce and policy admin'customer' vs 'policyholder' vs 'insured'; household vs named insuredAbout 120 hours a quarter of reconciliation; 6 priority incidents; wrong insured identity produces 14 wrong-tool logs a week, counted from the platform incident logNo owner
D2Policy admin and partner XSDRenewal date: anniversary vs billed-to vs effectiveA partner launch blocked; 3 weeks of slipIntegration architect
D3Policy admin and SnowflakeProduct code grain: plan vs coverage vs riderAbout 40 hours a month of model reworkData platform lead
D4Wiki ontology and the JSON SchemasFIBO-derived terms not mirrored in live schemasAgents invent synonyms; about 15 failed tool calls a weekNo owner

Costs are as the owners stated them in the interview and were not measured, with one exception: the 14 wrong-tool logs a week in D1 are counted from the platform's own incident log.

4. Complaints and opportunities

Complaint: quote agents fail the identity join on about 18 percent of multi-policy households, adding about 25 minutes of average handle time.

Opportunity: one governed Core Model, with connectors and an MCP endpoint, could reduce cross-system lookups on the quote path from five hops to one.

What would have to be true: a conformance contract, published schemas with a change log, and named owners for D1 to D4 before agent operations scale.

5. Stage placement

Sources and Structure are largely in place (SQL DDL, JSON Schema, XSD), but Semantics and Validation are uneven: the FIBO-derived ontology lives on the wiki and does not bind the live schemas. Retrieval and Agent Operations exist in pilots. A Learning Loop from failed tool calls back into the Core Model and its change log is not yet institutionalized.

This placement is directional, from a structured conversation. The Diagnostic Briefing places each stage on the L0 to L5 scale with the reasoning shown; the Core Model Blueprint measures it.

6. Remediation options

Ranked options, self-serve items marked
OptionFixesEffortSelf-serve?With helpDeliberately does not fix
Publish an identity and product-code Core Model slice; wire connectors for the quote pathD1, D3WeeksNoCore Model Blueprint, ImplementationPartner renewal-date XSD; CMS change log
Stand up the MCP endpoint and a conformance contract for claims classificationD4 (partial)WeeksNoImplementation, Agent Context Cost & Failure AuditFull FIBO alignment; Contentful sync
Self-serve disconnect register and owner RACI, from wiki to schema indexD1 to D4 (visibility)DaysYesDiagnostic BriefingThe semantic merge itself
Self-serve change-log template for Contentful, feeding the agent context packEnables item 3 of section 2DaysYesDiagnostic BriefingPolicy and CRM identity model
Managed operations: scheduled failed-call review feeding Core Model updatesD4A quarterNoManaged OperationsLegacy XSD partner freeze

Ranked by impact over effort: the Core Model slice, then the MCP endpoint and conformance contract, then the disconnect register, then the change-log template, then managed operations. Connector availability is as labelled on coremodels.io/connectors on the day of the interview; the catalogue is the source of truth.

7. Not covered

  • Regulatory filing systems, actuarial pricing engines, and third-party credit and identity bureaus.
  • Runtime authorization, PII redaction policy, and multi-region schema promotion.

8. Reviewer

Reviewed by: name withheld in the sample, Ariesnet. Date: withheld in the sample.

Questions

Answered directly

What does the interview produce, and what does it cost?

Thirty minutes on your schema landscape — optional parts can extend it to forty-five — and within two business days a written map: the systems that hold structure and meaning today, the connectors your agents need, where two systems disagree and what that is costing, and remediation options ranked, with the parts you can do yourself marked as such. The map is yours whether or not you go further, and there is no rung to buy first. A non-disclosure agreement is available on request before the interview.

When two systems disagree on the same metric, how is it resolved and can I reproduce yesterday's answer?

The disagreement is resolved as a mapping in the Core Model, approved by the owner of each schema, and recorded in the change log with who changed what and when. Agents read the model through the MCP endpoint, so an answer traces to the model version it was read from. Version history is a Team and Enterprise feature on coremodels.io; with it in place, yesterday's version is read back, and the answer with it.

Terms used

Definitions

Context supply chain
The end-to-end path that sources, structures, validates, retrieves, and governs enterprise context so agents can act on material that is correct, current, and usable. See also: Maturity level, Retrieval quality
Maturity level (L0 to L5)
A six-point placement applied per stage: L0 Ad hoc, L1 Structured, L2 Semantic, L3 Validated, L4 Operational, L5 Self-improving. Stages are scored independently — a mature retrieval layer sitting on L0 sources is a common and diagnostic pattern. See also: Context supply chain

Related engagement

Want this assessed against your own estate?

The Core Model Blueprint takes one named agent workflow through all seven stages with your systems and your content, and ends in a sequenced Core Model your team could execute without us.