Claims intake, underwriting research, fraud triage, and customer comms — each with human-in-the-loop gates and full audit trails. $3,999/mo retainer.
- Analyze
- Automate
- Monitor
monthly retainer
Who this is for
Insurtech ops lead, broker-tech director, or insurance group digital officer where claims intake is slow, underwriting research is still manual, and customer comms eat time that adjusters and underwriters don't have. You've seen AI pilots die on the compliance review. I build insurance AI automation that survives that review.
The pain today
- Claims intake takes days before an assessor can even start
- Underwriters spend hours pulling policy history and case data by hand
- Customer comms — renewals, status updates — pile up because no one has time
- Fraud signals buried in documents go undetected until it's too late
- Previous AI experiments failed compliance or legal review before launch
The outcome you get
- AI automations for insurance ops on $3,999/mo retainer
- Claims intake structured from documents, emails, and recorded calls
- Underwriting research surfaced from policy history and case data
- Fraud triage flags suspicious patterns before human assessment
- Audit trail on every AI input, output, and human override — regulator-ready
Where AI pays back fastest in insurance ops
AI automation for insurtech lands in four places with clear, measurable return.
Claims intake. An LLM reads the submission — forms, emails, PDFs, recorded calls — and extracts structured data for the assessor. The assessor starts with context already assembled instead of spending the first hour doing it themselves. That alone cuts intake processing time sharply.
Underwriting research. Given a risk or a case, the model pulls relevant policy history, precedents, and risk factors from internal knowledge and presents a summary for the underwriter to review. The underwriter still makes the call. They just make it faster and with less chance of missing something buried in 200 pages of prior correspondence.
Fraud triage. Anomaly patterns in claims documents — inconsistent timelines, mismatched identifiers, language patterns that correlate with fraudulent submissions — surface as flags for investigator review. Not decisions. Flags.
Customer communications. Renewal notices, claims status updates, and coverage-change letters drafted in brand voice, ready for agent review before send. The typing work disappears; the human judgment stays.
AI automations for insurance ops on $3,999/mo retainer
Compliance-aware architecture from the start
Insurance AI has a short list of rules that are non-negotiable, and I build to them from day one.
Human-in-the-loop on any decision that affects coverage, pricing, or claim outcome. No AI-autonomous denials. No AI-autonomous approvals on anything material.
Audit trails on every AI action: the input (PII-redacted on shared views), the model output, the model version, the timestamp, and any human override with the reason. For claim-adjacent decisions, that trail is preserved for the retention period required by state or country regulation — typically seven years or more.
Explainability for any AI-informed decision that touches policyholders. If a regulator asks why a flag was raised, the answer has to be in the log, not reconstructed from memory.
Fair-insurance compliance. Protected classes cannot inform underwriting or pricing. AI inputs and outputs are audited for protected-class correlation, including proxies — zip code as a race correlate in certain jurisdictions, for example.
The NAIC model bulletin on AI in insurance and FCA guidance for algorithmic decision-making are the reference points I use for US and UK insurtechs respectively. None of this is optional.
40+: Payment providers integrated.
Fraud triage: the signal most insurtech AI skips
Most AI insurance implementations cover claims intake and skip fraud triage entirely, treating it as a specialist problem. It isn't.
LLMs are good at detecting inconsistency — dates that contradict each other, claimant descriptions that don't match policy records, document formatting that suggests post-event editing. These are signals, not verdicts. The model flags them with a confidence score and a reason. A human investigator reviews and decides.
This matters because fraud losses in insurance run into billions annually, and a significant share comes through the claims intake process. Catching the signal early, before an adjuster invests hours, reduces the cost of investigation and the number of fraudulent claims that slip through.
I treat fraud triage as a separate eval task with its own golden-set test cases, separate from the claims-intake eval. The two tasks have different accuracy tolerances — false positives in fraud triage are annoying; false negatives are expensive.
Typical stack for insurance AI
LLMs: Anthropic Claude handles complex reasoning — claims narrative analysis, underwriting review, fraud signal interpretation. OpenAI handles structured extraction where speed and cost matter more than depth.
Vector DB for retrieval-augmented generation over policy documents, case history, and regulatory guidance. The model does not hallucinate policy terms it has never seen; it retrieves them.
Workflow engines: Temporal or Inngest for multi-step processing with explicit human approval nodes. The workflow graph makes the human-in-the-loop gates visible and auditable, not just a checkbox in the spec.
For insurtechs handling regulated health or financial data that cannot leave in-region infrastructure, self-hosted open-source models — Llama via vLLM — replace the hosted LLMs for sensitive document steps.
Monitoring: custom dashboards for AI decision patterns, accuracy drift, cost per task. The bolttech Payment Service I led at the $1B+ unicorn ran 40+ provider integrations with 99.9 percent uptime and zero post-launch critical bugs. The same discipline — observability baked in before launch, not bolted on after — applies to insurance AI.
What stays fully manual
Some insurance workflows do not change with AI, and being clear about this upfront is part of what makes the automatable parts trustworthy.
Final claim approval or denial. An adjuster or claims manager signs off. Always.
Underwriting decisions on high-value or complex policies. The underwriter makes the call.
Coverage determination in disputed cases. Human, with legal review if needed.
Anything where a regulator could challenge the decision basis and the answer 'the AI said so' would create liability.
I help draw this line clearly in the first month of engagement. Within the line, AI removes the manual labor and speeds up the workflow. Outside it, expert humans do the work as they always have. That line is hard, not fuzzy — treating it as fuzzy is how companies create regulatory exposure that can end them.
Pricing and what the retainer covers
$3,999/mo retainer. This covers AI integration work, compliance-aware architecture, RAG setup over your documents, eval infrastructure, monitoring, and iteration on each AI task.
14-day money-back guarantee. Cancel anytime. 100 percent code ownership under Work Made for Hire. NDA is standard. LLM and infrastructure costs pass through at cost — I don't mark them up.
For insurtechs handling regulated health or financial data, the infrastructure tier runs higher than general AI work — typically an additional $500 to $3,999 per month depending on data volume and self-hosting requirements. I surface that estimate in the first two weeks, not after six months of scope creep.
I run one customer engagements at a time. You work directly with me, not with a project manager passing notes.
Recent proof
A comparable engagement, delivered and documented.
Unified payment orchestration across Asia and Europe
Delivered the payment orchestration platform at bolttech, a $1B+ unicorn, with 40+ integrations across multiple regions.
Read the case studyFrequently asked questions
The questions prospects ask before they book.
Every AI task that could affect a policyholder has a required human review step before the output leaves the system. In a claims intake workflow, the AI assembles structured data from the submission and flags anomalies — but an adjuster confirms the assessment before it progresses. In underwriting research, the model presents a summary and the underwriter makes the decision. The workflow engine records the human action, who did it, and when. That record is the audit trail.
The NAIC model bulletin on AI in insurance is the main US reference — it covers transparency, explainability, and consumer protection requirements for AI-informed decisions. Most states have adopted or are adopting versions of it. California and New York have additional layers. For health-adjacent underwriting, HIPAA applies to how data is processed and retained. I build to the strictest applicable standard from the start rather than patching compliance in after the system is running.
Yes, with the right framing. An LLM identifies inconsistency signals in claims documents — contradictory dates, mismatched policy identifiers, document anomalies that suggest post-event editing. These surface as flags with a confidence score and a plain-language reason, not as automated denial decisions. A human investigator reviews each flag. False positives cost time; false negatives cost claims payouts. The eval threshold is calibrated accordingly, and I test it with a dedicated golden set separate from general claims-intake evals.
Protected classes — race, religion, national origin, sex, age in applicable contexts — are excluded from AI inputs and outputs that affect underwriting or pricing. Proxy variables that correlate with protected classes in specific jurisdictions (zip code as a race correlate is the canonical example) are also excluded or audited. I run regular bias checks on AI-informed decisions and document the methodology. For insurtechs in strict-compliance markets like California or New York, additional auditing layers apply.
PII minimization in prompts — only what the specific task requires, nothing else. LLM providers operate under signed DPAs with no-training clauses on customer data. For highly sensitive data — SSNs, full financial records, health-related underwriting data — I use self-hosted open-source models or dedicated private deployments so data never leaves your infrastructure. Audit logs track PII appearance in prompts with redaction on any shared or exportable views. This is documented in the privacy posture, not kept in someone's head.
Each AI task gets its own golden-set eval: hand-crafted test cases with expected outputs, regression testing on every prompt change, and production sampling with claims or compliance review. For high-stakes outputs — claim assessments, underwriting drafts, fraud flags — 100 percent human review before any production action. The eval infrastructure is documented and queryable, which matters when a regulator asks how you validated the AI before deploying it.
First two weeks: I map your current claims and underwriting workflows, identify the highest-value automation candidates, and draw the hard line between what AI should handle and what stays with humans. I also surface the full infrastructure cost estimate — no surprises after month three. Weeks three and four: first working AI task in staging, eval infrastructure in place, compliance review ready. The first month ends with a system you can show your legal and compliance teams, not just a demo.