Operations analytics
Ask policy, servicing, and operational data questions in plain English through validated queries.
Turn policy, call, and service work into faster, reviewable operations—with source context and human control.
System liveProduction path
What entersPolicy, call, or service case
AI workUnderstand, assist, and route
Control planeEvalsAccessLogs
What leavesA review-ready actionOwned by your team
What we build
Policy, servicing, call, and analytics systems.
Production AI systems for insurers, brokers, and insurtech teams across servicing, operations, analytics, calls, workflow automation, and market research.
Voice and call analysis
Structure claims and support calls into summaries, commitments, coaching moments, and review flags.
Policyholder and agent support
Ground answers in approved policy, product, and procedural information.
Workflow automation
Automate repeated lookups, routing, status updates, and evidence assembly with clear handoffs.
Competitor research
Track product, distribution, pricing, and market signals across changing public sources.
Evaluation systems
Measure answer quality, workflow reliability, and regressions against real insurance examples.
Case studies
Production systems, with measurable outcomes.
Natural-language analytics, real-time voice guidance, and self-healing collection across changing sources.
Engineering scope
Model capability is only one part of the system.
01
Understand
Bring scattered operational context together.
Combine policy, customer, call, workflow, and approved knowledge without removing existing access controls.
02
Assist
Give teams grounded answers and next steps.
Surface the source, confidence, and context needed for a person to act.
03
Automate
Move repetitive work through controlled workflows.
Agents handle bounded tasks and route exceptions to the right owner.
04
Improve
Use production feedback as an evaluation loop.
Reviewer feedback becomes evaluation data for subsequent releases.
Delivery model
Make the production decision early.
Define the workflow
Map the users, systems, data, controls, and measurable operating result.
OutputA focused production plan
Validate with real examples
Test representative inputs against quality, latency, cost, privacy, and review requirements.
OutputMeasured scope and baseline
Engineer the system
Build integrations, evaluations, interfaces, permissions, monitoring, and recovery paths.
OutputProduction-ready software
Launch and hand over
Launch with real users, improve from production feedback, and hand over code, runbooks, and baselines.
OutputAn operated system your team owns
Production controls
Built to evolve as models change.
Models can change without changing the operating contract. Permissions, evaluations, observability, and ownership stay explicit.
01
Policyholder and commercial data stays within the agreed boundary
02
Role-aware access and existing system permissions remain authoritative
03
Every important answer or action retains source and execution context
04
Human review remains explicit for consequential decisions
05
Quality is measured on real servicing and operations examples
06
Your engineering team owns the delivered software and runbooks
Buyer questions
Before you start.
Which insurance workflow should we start with?+
Choose a repeated workflow with accessible examples, visible handling time, and a clear owner—such as call review, operations reporting, support knowledge, or status and routing work.
Can the system respect policy and customer-data permissions?+
Yes. Retrieval, tools, and interfaces are designed around the identity, role, tenant, and data-access rules already used by your organization.
How do you choose the right model and architecture?+
We test the workflow against your quality, latency, cost, privacy, and operational constraints. Model choice follows the evidence; the architecture stays replaceable where practical.
Can you work inside our existing cloud and engineering stack?+
Yes. We design around your APIs, identity model, data boundary, observability, release process, and ownership requirements rather than forcing a separate platform.
Who owns the finished system?+
Your team does. We deliver the code, evaluation baselines, monitoring, runbooks, and transfer needed for your engineers to operate and extend it.
What is a sensible first engagement?+
One valuable workflow with real samples, an accountable owner, and measurable success criteria. We prove the path before expanding the scope.
Start with one system
Bring the workflow, constraints, and real examples.
We will help determine the smallest production slice worth proving, what must be measured, and where human control belongs.