Explainability the regulator will accept.

• 01

  Use-case risk classification

  Sorted by consumer impact, with pricing and underwriting held to a stricter control set than back-office extraction.

• 02

  Decision-record design

  What gets logged at each step so a decision can be reconstructed months later, including the exact prompt and retrieved context.

• 03

  Fairness and adverse-outcome review

  A repeatable process for testing outcome differences across cohorts, with an owner and a cadence rather than a one-off report.

• 01

  The coverage decision stays with a human

  The system extracts, retrieves the applicable wording and drafts the reasoning. An adjuster confirms or overrides, and their reasoning is recorded — which is what a complaints process and an ombudsman will ask for.

• 02

  Version-aware retrieval or nothing

  Every retrieved clause carries its document version and effective date, and queries are scoped by policy inception. Quoting the wrong revision is a regulatory problem, not a UX one.

• 03

  Extraction accuracy is reported per document type

  A typed invoice and a handwritten claim form are different problems. We publish accuracy per type before you decide what to automate.

1. 1

   Stakeholder interviews

   30–45 minutes with each executive and team lead. We surface the real bottlenecks (not the ones the deck says).

2. 2

   Use-case pipeline

   Score 20–40 candidate AI use cases on impact × feasibility × strategic fit. Pick the top 3 with quantified ROI.

3. 3

   Build vs buy

   Honest answer for each pillar — sometimes the right move is Notion AI or a Zapier flow, not a bespoke build.

4. 4

   Governance framework

   Policy, approval workflow, prompt-injection / PII / IP guardrails. Aligned with your compliance posture.

5. 5

   90-day plan

   Quarter-by-quarter roadmap, hiring plan, budget, vendor selection, and KPIs the CFO will actually accept.

• Guidewire & Duck Creek
• Policy administration systems
• OCR and layout-aware extraction
• Salesforce & Zendesk
• Twilio & WhatsApp Business
• Snowflake
• Document management systems

FAQ

AI governance for insurers: your questions.

• Does the EU AI Act apply to us if we're not in the EU?

  It can, if you serve EU policyholders or your output is used there — and pricing and risk-assessment use cases sit in its higher-risk tiers. We map obligations per jurisdiction against a single control set rather than maintaining parallel frameworks.

• What if we already have model risk management?

  Then most of the framework exists and the gap is usually LLM-specific: prompt versioning, retrieval provenance and eval policy. We extend what you have instead of introducing a second system nobody follows.

• Is this just a deck?

  No. Every strategy engagement ships at least one working artifact — a costed roadmap, a governance template ready for legal, or a vendor negotiation outcome. Decks alone are useless.

• Do you work with non-technical executives?

  Yes — most of our strategy work starts with non-technical leaders. We translate between board-room and engineering-room.

• RAG for insurance policy wordingRetrieval across policy wording, endorsements and claims manuals, scoped by product, territory and effective date — every answer carrying its citation.Read
• AI agents for claims processingAgents that extract from claim documents, chase what's missing, and hand the adjuster a complete file with the coverage reasoning already drafted.Read

• AI governance for financial servicesModel inventories, risk classification, evaluation policy and human-oversight design — written to survive an internal audit rather than a conference talk.Read
• Healthcare AI governance and readinessUse-case triage against clinical risk, a data-handling design that survives a HIPAA or DPDP review, and an oversight model your clinical governance body will actually accept.Read
• AI governance for legal teamsAcceptable-use policy, confidentiality controls, and an approval process for AI tools that lawyers are already using with or without permission.Read