Insurance AI Development: From Pilot to Production
We take insurance AI into live underwriting, claims and compliance work for insurers, MGAs and brokers, and build the data, integration and governance it needs to keep running.
Our senior AI and engineering teams work from Sofia, two hours ahead of London and inside the EU. As part of Synechron Group, we work alongside its insurance advisory and strategy teams.
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They trust us
Why new submission tools rarely change how underwriters work
Martin Nikolaev, our Commercial Director for Insurance, UK and Ireland, explains why AI submission tools often sit beside the underwriting process instead of inside it, and what it takes to get them into the workflow. 1 minute.
Why insurance AI pilots stall
Most insurance AI pilots work in a demo and stall before they reach live work. The model usually works; the data, integration and governance around it were never planned for.
01
No workflow to plug into.
Submissions, bordereaux and claims data still move through legacy screens, spreadsheets and email. An AI tool that sits beside that process gets used for a few weeks, then dropped.
02
Data that needs fixing by hand.
When the model reads incomplete or inconsistent data, someone has to check every output, and the time saved disappears.
03
No audit trail a regulator would accept.
Compliance teams can’t sign off decisions they can’t trace, so the pilot never gets approval for live use.
Where AI pays off in insurance
We focus on areas where AI saves measurable time, and where we can show a regulator how each system was built and governed.
Submission intake and triage
Insurance AI reads broker submissions, exposure schedules and loss runs as they arrive, extracts the data and flags what’s missing. Fixed business rules then route each submission, so underwriters start with a structured file. Measured on cycle time and manual touches per submission.
Document extraction
Loss runs, bordereaux, schedules and policy wordings arrive as PDFs, scans and spreadsheets. OCR and intelligent document processing turn them into clean data your systems can use, and send low-confidence fields to a person for review.
Claims triage and orchestration
Our insurance AI sorts first notifications of loss, extracts data from claim documents and routes each claim to the right handler or a straight-through path.
Regulation-to-control mapping
Generative AI reads new regulation and maps it to your existing controls. Your compliance team reviews the proposed changes and doesn’t have to start each one from a blank page. Measured on hours of manual evidence-gathering removed.
Data quality and exception routing
AI checks incoming policy and claims data against your rules, routes exceptions to the right person and keeps a timestamped record of every fix. Measured on review time and errors caught before they reach reporting.
Proof from live insurance work
Our insurance AI and data work runs in live insurance and financial services operations today.
Data quality for a leading insurer
We replaced weekly Excel exports and email chains with a Data Quality Portal that sends every finding and dispute to the right person and timestamps each action. Review time fell by more than half, and data quality reporting went from up to two months to up to three days.
An AI platform for a compliance technology provider
We built the platform with the client’s team: one AI platform that gives compliance teams in regulated financial firms governed access to more than 100 models from four providers. Its AI agents work across live compliance systems and cite the source document behind every answer, so investigations that used to span five or more systems now take minutes.
Document extraction for a private finance firm
A team used to read quarterly fund reports by hand and copy the figures into Excel. Our pipeline now extracts the data from each PDF, checks it against the firm’s business rules and flags what it can’t resolve to a person, with more than 90% accuracy when tested against historical reports.
Underwriting modernisation for a specialty insurer and reinsurer
Some underwriting screens had 1,000 fields. We replaced them with a step-by-step flow that adapts to each answer, and the client then asked us to do the same across all its business lines and its claims system.
AI agents you can audit
This is how we build agentic AI for insurance: AI does the reading and sorting, follows your fixed rules set by your team, and logs every step.
Low-confidence extractions go to a person before the rules route them, and every step lands in the audit trail.
What’s built in:
Evaluation before go-live.
We test each model against your own historical cases and agree an accuracy threshold before it touches live work.
Human review where it matters.
Low-confidence outputs, and decisions above an agreed authority level, go to a person with the evidence attached.
A full audit trail.
Every input, model output, rule applied and human decision is timestamped, so you can show a regulator or auditor how each outcome was reached.
Guardrails and monitoring.
Checks on what goes into and comes out of each model, alerts when accuracy drifts, and a clear way to roll back.
AI governance for regulated insurers
We design every AI system around the rules your regulators already apply, and document it so your risk and compliance teams can sign it off.
EU AI Act
AI used for risk assessment and pricing of individuals in life and health insurance counts as high-risk. For those systems, we build in the documentation, data governance, human oversight and logging the Act requires.
DORA and UK operational resilience
AI models and platforms bought from third parties count as ICT third-party services. We add them to your DORA register of information, or your PRA third-party records, and include them in incident classification and resilience testing.
FCA Consumer Duty
Where AI touches customers, for example in claims or servicing, we test outcomes across customer groups, including customers in vulnerable circumstances, and keep the evidence your annual Consumer Duty board report needs.
Data inside the EU
Models, data and logs can be hosted in EU regions, and sensitive work stays within EU jurisdiction.
Your AI team
Each insurance AI development engagement is delivered by a senior, self-contained team that is accountable for an agreed outcome and can start delivering within weeks.
- Product owner or business analystThe backlog and business outcomes, working with your underwriting, claims or compliance teams
- Solution architectTarget architecture, integration with core and rating systems, security and governance
- AI engineersAgentic workflows, model integration, evaluation and guardrails
- Data engineersIngestion, data quality, the canonical data model and pipelines
- Software engineersThe workbench front end, APIs, workflow and orchestration
- QA engineerTest automation and model evaluations
- DevOps engineerCI/CD, cloud environments, release and run
- UX/UI designerThe underwriter and user experience, so the tool gets used
95%retention
rate
The mix can change based on your needs. A workbench team leans on software and AI engineering, and a compliance team focuses on data engineering. With our 95% retention rate, the team that starts your project will be the one to complete it.
Buy, build or both
We aren’t tied to an AI vendor, so you can build on a tool you’ve already bought, build your own, or combine the two.
Many insurers have already bought AI solutions for submissions or claims. We connect them to your workflow, data layer and core systems, and build custom components only where the bought tool can’t do the job. You own the code, the IP and the evaluation results.
AI tech stack:
- Azure OpenAI
- Amazon Bedrock
- LangGraph
- MLflow
- Vector databases
- OCR and IDP
- Databricks
- Appian, for orchestration and agentic AI
How we take AI into production
Every insurance AI development engagement follows the same three stages, with evaluation and governance built in from the first week.
1
Reimagine
We start from one business outcome, such as quote turnaround or hours spent gathering audit evidence, and redesign the workflow around AI and data. Success measures and compliance guardrails are agreed here, before anything is built.
2
Reinvent
We build what the AI depends on: the underwriting workbench, the operational data layer, integrations with your policy administration and rating systems, and the evaluation tests that show the model is accurate enough for live use.
3
Redeploy
We take it live and run it from Sofia with a stable, senior team. Monitoring, retraining and handover are part of the scope, so your onshore budget and people go to the work only London can do.
Start with a Data and AI Discovery
A fixed-price discovery shows whether your insurance AI use case can reach production, and what it will take to get there.
You leave with a scoped 8 to 12 week first engagement, success measures agreed with your team and a clear view of the risks. You keep all of it whether or not you go ahead.
It covers:
Tell us which AI use case you want in production first.
In a Discovery, we’ll test whether it’s ready for production and scope a first engagement.
