Workflow in scope
One existing claims, underwriting, or service workflow that already has real test cases and a named operational owner.
AI reliability for Insurance
Make your insurance AI safe enough to launch.
Claims and underwriting teams lose time moving data between systems, while sensitive customer information raises the cost of mistakes. We test failures, add controls, and give you launch evidence.
Claims accuracy, traceability, and controlled handoff · $4,500 sprint · One defined workflow
Direct answer
Insurance · AI Reliability Guardrails
What does this engagement mean for insurance teams?
Insurance AI is ready to launch only when the team can measure extraction accuracy, retrieval quality, tool behaviour, and escalation under representative claims. A good demo is not enough when a wrong policy clause or claimant match can change an outcome.
Likely system boundaries
- Historical claims and policy examples
- Tracing for retrieval and tool calls
- Release and incident-management workflow
Evidence required
- Evaluate routine, ambiguous, incomplete, and adversarial cases
- Block unsupported policy interpretations
- Test fallback and human review when confidence or system access fails
Important boundary
The sprint produces evidence for one release decision. It does not certify regulatory compliance or guarantee that every future claim will be handled correctly.
Who this is for
For product and engineering leaders who cannot keep shipping on hope.
Best for Seed to Series B Insurance teams—where an AI feature exists, but deployment is frozen over hallucination risk, compliance exposure, or reputation damage.
- The agent hallucinates, loops, or takes unpredictable actions under real data.
- Leadership will not approve a launch because nobody can prove the system is safe.
- Tool calls fail, duplicate work, or leave the workflow stuck with no recovery path.
- You have logs or traces, but no clear evaluation set or release decision.
- Prompt changes create regressions you only notice after users complain.
- You are in fintech, healthtech, insurtech, or legaltech and compliance risk is real.
What changes in the sprint
BeforeAfter
“It seems better after the prompt change.”
Representative eval cases and an explicit go / no-go release decision
Failure shows up as a support ticket
Traces, failure classification, alerts, and defined recovery behaviour
AI takes a high-impact action with weak controls
Approval gates, permission boundaries, and clear escalation
Tool or API errors leave the workflow stranded
Retry, fallback, or human handoff—chosen on purpose
What is included
- One workflow architecture map and failure-mode inventory
- A scoped evaluation plan and representative test set
- Observability or tracing improvements so failures are diagnosable
- Guardrails, approval points, retries, fallbacks, or recovery controls in agreed scope
- Regression checks for the critical paths that matter most
- Handover: implementation notes, operating guidance, known limits, and next priorities
Pricing shape
$4,500
Reliability sprint: map failures, add the controls that matter, and produce release evidence for one defined workflow.
$1,500 / month
Optional retainer for ongoing observability, eval refresh, and controlled tweaks after the sprint. Only when it is useful—not as hidden scope.
Days 1–3 — Inspect the workflow, rank risks, lock definition of done
Days 4–10 — Build agreed guardrails, evals, and recovery behaviour
Days 11–14 — Regression review, release decision, handover
Frequently Asked Questions