AI by industry
AI for insurance that reads the file before you do
For carriers, MGAs, brokers and TPAs, we build AI that prepares the work: claims summarized at first notice, broker submissions turned into structured risk data, and policy questions answered from the wording. Adjusters and underwriters make the calls, with the evidence in view.
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Where AI fits in claims, underwriting and service
- OpenAI
- Claude
- LangGraph
- Python
- FastAPI
- scikit-learn
- OpenCV
- Qdrant
- PostgreSQL
- Apache Airflow
- Next.js
An insurance operation runs on documents: loss notices, photos, repair estimates, medical bills, broker emails, loss runs, schedules of values and policy wordings. Skilled people spend much of their day finding facts in that stream and keying them into core systems. Our systems take over the finding and keying, so adjusters and underwriters open each file with the facts laid out and the gaps already flagged.
Insurers are accountable for decisions made with AI, not only by it. Regulators increasingly expect a written AI program with governance, testing for unfair discrimination and oversight of models and data bought from vendors. We design each system to fit that program: recommendations carry their sources, adverse actions stay with licensed staff, and every model, prompt and output is versioned and logged.
We build for carriers modernizing claims and underwriting, for MGAs and brokers handling heavy submission volumes, and for insurtech teams launching new products. The AI connects to your existing policy administration, claims and document management systems rather than replacing any of them.
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AI across the policy life cycle
From submission to settlement, these are the points where reading and matching slow a team down most, and where AI can prepare the work for a person to finish.
First notice of loss intake
Capture claims from email, web forms, call transcripts and photos, extract the loss details into your claims system, check basic coverage facts against the policy, and flag missing information before assignment.
Claims triage and assignment
Suggest complexity, severity and the right handler for each new claim from your routing rules and past handling patterns, so simple claims move quickly and complex ones reach experienced adjusters sooner.
Underwriting submission review
Read broker submissions, applications, loss runs and schedules of values, turn them into structured risk data for your rating tools, and highlight appetite concerns and missing documents for the underwriter.
Fraud and SIU referral support
Surface inconsistencies across claim documents, prior claims and third-party data, and prepare a referral summary for your special investigations unit. Investigators decide what to pursue, and the AI never denies a claim.
Policy wording answers
Let adjusters, agents and service staff ask coverage questions in plain language and receive answers cited to the clause, endorsement and policy version that applies, with a person making the coverage call.
Bill and estimate review
Extract line items from medical bills and repair estimates, compare them with fee schedules and your guidelines, and mark items that need a closer look. The adjuster decides what is paid.
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What arrives with the system
Integrated application
The AI service and reviewer screens, connected to your policy administration, claims and document management systems and deployed in your cloud.
Model inventory entries
A record for each model and prompt covering purpose, data sources, owner, version history and known limitations, ready for your AI program inventory.
Accuracy and bias testing
Results on held-out claims or submissions, including testing for unfair discrimination wherever outputs influence pricing, eligibility or claim handling.
Decision audit trail
A log linking each recommendation to its sources, the model version that produced it and the adjuster or underwriter who acted on it.
Monitoring and rollback plan
Monitoring for drift, quality and cost, thresholds for escalation, and the steps to roll back a model or prompt change safely.
Guides for adjusters and underwriters
Training material that shows staff how to read the evidence behind each suggestion, when to override it and how to flag a poor recommendation.
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How an insurance AI engagement works
Each stage is built to sit inside the AI governance program your regulators expect, with claims and underwriting leaders involved throughout.
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Choose one workflow and its guardrails
We start with a single line of business and workflow, such as first notice of loss for personal auto or submission intake for a commercial program, and agree which outputs are advisory and which actions stay with licensed staff.
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Design for your AI program
Controls follow the themes of the NAIC model bulletin: documented governance, risk assessment, testing, and oversight of third-party data and models. Data access, retention and logging are settled before any build work starts.
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Replay closed files
We run historical claims or submissions with known outcomes through the system, measure where it agrees, differs or misses information, and test results across groups where fairness rules apply, documenting each step for your auditors.
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Launch with review, then widen
The first release routes every output through a person. As accuracy is proven and your governance committee agrees, low-risk steps can move to lighter review, while monitoring keeps checking quality and cost.
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Fairness, governance and regulation
Insurance regulators have moved quickly on AI. The NAIC model bulletin on insurers' use of AI systems, now adopted in a growing number of states, expects a written program covering governance, risk controls, testing for unfair discrimination and oversight of third-party models and data. Colorado has gone further for some lines, with rules on how insurers govern and test their use of external consumer data and algorithms. In Europe, the EU AI Act classes risk assessment and pricing in life and health insurance as high risk, and GDPR applies to policyholder and claimant data.
Your compliance and legal teams decide how these rules apply to your lines and states. What we build makes their conclusions easy to evidence: claim denials and adverse underwriting actions stay with licensed people, each recommendation records the data and reasons behind it, protected characteristics are kept out of models and used only in controlled testing, and model versions stay traceable for market conduct exams. Assistants explain policy wording; they do not make coverage determinations.
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Related services
Insurance software
We build policy, claims, underwriting and broker platforms that turn insurance from forms-and-fax into a Stripe-grade digital experience.
Build AI agent for business
You want an agent that takes real actions — schedules, drafts, runs reports — not just answers questions.
Build RAG chatbot
You've seen the demos but your data is messy, multi-source, and updates daily.
Automate business workflows
Repeatable processes (onboarding, billing reminders, fulfilment) eat engineering and ops time every week.
AI for healthcare
Clinical admin, triage support and document intelligence.
AI for fintech and banking
KYC documents, fraud signals and service in regulated finance.
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Frequently asked questions
How is AI used in insurance claims?
Mainly to prepare work for adjusters: capturing first notice of loss details, reading documents and photos, checking coverage basics, suggesting a handler, summarizing long files and flagging possible fraud for investigators. The adjuster still decides coverage and payment. Done well, it shortens the time a claim waits for attention and gives every adjuster the same complete picture.
Can AI deny insurance claims?
It should not, and we do not build it to. Denials carry duties to investigate fairly and explain the reason, and some states now restrict AI-driven denials in health coverage. Our systems can recommend and explain, but a licensed adjuster makes and owns any adverse decision, with the AI's reasoning recorded alongside theirs.
What does the NAIC AI model bulletin mean for our projects?
In states that have adopted it, insurers need a written program for AI systems covering governance, risk management, internal controls and oversight of third-party models and data. Each project should fit inside that program. We supply the documentation it calls for, such as model purpose, data sources, test results and version history, so your compliance team can assess and record the system.
Can AI help underwriters with broker submissions?
Yes, and submission intake is one of the most practical places to start. The AI reads applications, loss runs, schedules of values and broker emails, fills the structured fields your rating tools and workbench need, and highlights appetite concerns and missing documents. Underwriters review the extracted data and make every pricing and acceptance decision.
How do you test insurance AI for unfair discrimination?
We agree the approach with your actuarial and compliance teams: which outcomes to test, across which groups, and with which statistical measures. Where the law allows, protected characteristics are estimated only inside a controlled testing environment and never used by the model. We compare outcomes on held-out data, investigate any gap, and repeat the tests after each model or data change.
Will the AI work with our policy and claims systems?
Usually, yes. Modern policy administration and claims platforms, including Guidewire and Duck Creek, expose APIs, and older systems can often be reached through database views or file exchanges. We confirm access, vendor terms and data ownership during discovery, before the design is fixed.
Ready to build AI that actually works in production?
Tell us about your AI project and get a free technical consultation within 24 hours. We'll map your use case, assess your data, and give you an honest feasibility assessment — no sales pitch.