Insurance underwriters and risk engineers spend a significant portion of their time reading. Lengthy evaluation reports, property inspection documents, vehicle fleet assessments — each with different structures, formats, and levels of detail. Extracting the relevant information and producing a standardized summary is manual, repetitive, and error-prone. This is exactly where an AI copilot can make a difference.

In 2024, g-Xperts — a Google Maps & Cloud Premium Partner — asked Hotovo to build an AI copilot that would automate this process. The goal was clear: take complex, unstructured insurance report documents and produce consistent, structured summaries that underwriters can act on immediately.

Why this problem is harder than it looks

Insurance report automation sounds like a straightforward document-processing task. It is not. Three factors make it genuinely difficult:

Format diversity. Reports arrive in PDF, Word, scanned images, and proprietary formats. They range from 5 pages to 200 pages. No two reporting organizations use the same template.

Content ambiguity. A single report may contain both quantitative data (square footage, reconstruction costs, fire protection ratings) and qualitative assessments (“the building appears to be in good condition”). The AI must extract both and know the difference.

Provider agnosticism. g-Xperts required the solution to be AI-provider-agnostic. End customers must be able to host the AI component in their own environment with their chosen LLM provider — or their own models. This is not a nice-to-have; it is a dealbreaker for enterprise insurance clients with strict data residency requirements.

Architecture of the AI copilot for insurance underwriters

We designed a modular pipeline with clean separation between document ingestion, content extraction, summary generation, and quality validation — similar to how we approach AI assistants for complex, domain-specific data environments. Each module can be independently configured and swapped.

The key architectural choices:

1. Structured output schemas that define exactly what fields the AI must extract, reducing hallucination and ensuring consistency across reports.
2. Provider abstraction layer that wraps LLM API calls behind a unified interface. Switching from one AI provider to another requires configuration, not code changes.
3. Prompt versioning and management so that changes to prompts are controlled, testable, and auditable before deployment.
4. Quality validation that compares extracted data against expected formats and flags anomalies for human review.

From first project to AI copilot

Hotovo’s relationship with g-Xperts started with a different project: building a motor fleet risk evaluation module for their BI platform. That project involved ElasticSearch, vector search, intelligent filtering, and data visualization — delivered on time and within scope.

The success of that first project earned us the trust to take on the AI copilot. This is a pattern we see regularly: clients start with one well-scoped engagement, see the quality, and expand into more complex and strategic work — as shown in our long-term collaboration with Protecht.

"AI for some is a black box. For others, it’s magic. But for us, it’s about choosing the right tool for the right job. For Hotovo, it’s a matter of hard-earned expertise with a spark of imagination." — Jozef Soročin, Leader of special projects at g-Xperts

Where this is going

The AI copilot is already live and being used in sales conversations with top-tier insurers. g-Xperts has since expanded our collaboration into another new product: an AI-powered landing page generation platform capable of producing 20,000+ SEO-optimized pages per hour for their local search clients.

Both products share the same foundation: structured AI outputs, provider flexibility, quality validation, and a modular architecture that lets g-Xperts offer enterprise-grade AI products without locking their clients into a single AI vendor.

Key principles for building enterprise AI copilots

If you’re considering building an AI copilot for a domain-specific enterprise use case, here are the principles that guided this project:

1. Provider agnosticism is a feature, not a limitation. Enterprise clients will not adopt AI tools that create vendor lock-in.
2. Structured output schemas are your quality floor. Do not let the LLM generate freeform text when you need structured data.
3. Prompt management is operations, not development. Treat prompts like configuration, not code. Version them, test them, audit them.
4. Start with a bounded project and earn the bigger one. The fastest path to a strategic AI engagement is a well-delivered tactical one.

The quality of output depends heavily on how well the system is structured — something we’ve also seen in AI-assisted migration projects.

Hotovo holds ISO/IEC 42001 certification for AI management systems. If you’re building AI products for regulated industries, we’d welcome the conversation. Reach out at sales@hotovo.com.