A sample opportunity memo.

The thesis, as we understood it

"Claims processing at mid-size European insurers is about to be rebuilt around AI. Adjusters spend most of their time reading documents, not making decisions. I want to own the company that becomes the claims-intelligence layer for insurers too small to build this themselves. I have capital committed and no technical team."

Executive verdict

Buildable, and the timing logic holds — but the company that wins here will not sell "AI claims automation." It will sell shorter settlement cycles with an audit trail a regulator can read. The technical risk is moderate; the two decisive risks are data access before the product exists and the EU regulatory posture of anything that touches a claim decision. Both are addressable, and both shape the architecture below. We would take this build.

1. The system we would build

A claims-intelligence pipeline that sits beside the insurer's core system (Guidewire, Sapiens, or the in-house relic), never inside it in year one:

• Intake & extraction. Documents (FNOL forms, medical reports, invoices, photos) enter through a watched channel; a document-understanding layer extracts structured facts. Extraction runs on a hosted frontier model behind a provider-agnostic gateway, with every field carrying a confidence score and a source span, so a human can see where in the document each fact came from.
• Validation & enrichment. Extracted facts are checked against policy data and claim history. Deterministic rules where rules suffice; the model only where judgment is genuinely required. This split is what keeps the system explainable and the per-claim cost low.
• The human gate. Nothing pays out automatically. The system drafts a settlement recommendation with its evidence attached; an adjuster approves, adjusts, or rejects. Every disagreement between adjuster and system is captured — that disagreement stream is the most valuable training data in the company.
• The eval suite, from day one. A private, adversarial test set of real claim documents (anonymized, partner-provided) that every model change must pass before deploy. This is the moat the demo never shows: after a year, the eval suite and the disagreement data are why a competitor with the same model API cannot catch up.

Deployment: single-tenant per insurer, EU-hosted, VPC or on-prem where required. Boring by design — regulated buyers pay for boring.

2. Team plan and sequencing

The sequence begins with a design partner, real claims-process access, and an evaluation set before the production product. The first build covers one claim type—motor or property, not both—and one adjuster team. A second design partner enters only after the first workflow produces evidence worth repeating. The operating company, not Corteus, must have a full-time commercial owner throughout.

3. Cost to first production deploy

A real plan would be priced only after data access, deployment boundary, compliance ownership, and partner contribution are known. The cost model would separate:

• Technical capacity: validation, product engineering, infrastructure, and the continued ownership required to operate the system.
• Model & infrastructure: measured per claim and per completed workflow, including retries, review, storage, and observability.
• Data & compliance: the underestimated line — anonymization tooling, DPIA work, audit logging, and the eval-set construction. Budget it like a feature, because it is the product's license to exist.
• Commercial validation: customer discovery, design-partner access, procurement, and the time carried before the first production revenue.

4. The three risks most likely to kill it

• Data access before product. Insurers do not hand claims data to a stealth vendor. De-risk: anchor design partner secured before incorporation (an LOI with data-sharing terms is worth more than the first hire), synthetic and public corpora for the pipeline skeleton, single-tenant EU deployment as the opening concession.
• Regulatory classification. A system that decides claims is high-risk under the EU AI Act; a system that assists an adjuster with evidence attached sits in a defensible posture. De-risk: the human gate is not a feature, it is the positioning — and the audit trail is designed for a regulator to read, not just the customer.
• Incumbent distribution. Guidewire and the consultancies own the C-suite relationship. De-risk: wedge through claims operations, not IT; one claim type with measurably shorter cycle time beats a platform pitch; integrations stay read-only for the first year so the core-system vendor has nothing to veto.

What we would need from you

A full-time business operator, committed capital or a credible funding path, direct access to at least one potential design partner, and the willingness to stop if the evidence contradicts the thesis.

End of sample. This is a hypothetical reasoning artifact, not a quote, customer result, investment recommendation, or commitment to build.