AI agents that read your policies — and ship claims faster.

We build reliable insurance AI agents that learn your underwriting and claims context, accelerate decisions, and keep a human firmly in the loop — with access controls your auditors trust.

From FNOL intake to underwriting, policy servicing, fraud, and subrogation — grounded on your policy docs and playbooks, wired into your core admin system, and measured on loss-adjusted accuracy, not demo wow.

Trusted by teams at

Shopify
Qonto
Leafworks

The mandate

Turn unstructured insurance work into fast, explainable, reviewable decisions.

Insurance is drowning in PDFs, emails, ACORDs, medicals, and endorsements. We deploy agents that read, classify, extract, reason over policy and playbook, and draft decisions — then route to humans for sign-off with every source and rationale attached.

What you get

  • FNOL + claims intake: multimodal extraction across PDFs, photos, emails, voice.
  • Underwriting copilots grounded on your guidelines, appetite, and rating docs.
  • Policy-servicing agents for endorsements, certificates, and renewals.
  • Fraud / SIU triage with rationale, links, and reviewable evidence.
  • Subrogation + recovery agents surfacing leakage and missed opportunities.
Scope a claims or underwriting agent

Why it works

Why this approach wins.

01 · Principle

Agents that actually read the policy

Not a generic LLM — a grounded agent that cites the clause, the endorsement, the exclusion, and your internal playbook side-by-side.

02 · Principle

Human-in-the-loop is a feature, not a disclaimer

Every decision carries confidence, rationale, sources, and a structured review UX so adjusters and underwriters approve in seconds, not minutes.

03 · Principle

Ships into your core, not around it

We integrate with Guidewire, Duck Creek, Majesco, Sapiens, and homegrown cores — so the agent's output lands in the system your ops already live in.

Outcomes

The outcomes we commit to.

−50%

claims cycle time

+30%

underwriter capacity

FNOL triage speed

100%

cited decisions

Awards

Proud moments.

Top 1% on Clutch Global

Top 1% on Clutch Global

Top AI Strategy Company 2025

Top AI Strategy Company 2025

Clutch Fall Champion 2024

Clutch Fall Champion 2024

Inc. 5000 Fastest Growing

Inc. 5000 Fastest Growing

Breakthrough of the Year 2019

Breakthrough of the Year 2019

Member of Forbes Tech Council

Member of Forbes Tech Council

Voice & Speech Recognition 2024

Voice & Speech Recognition 2024

Top Blockchain Company 2024

Top Blockchain Company 2024

Innovators of the Year 2019

Innovators of the Year 2019

GoodFirms Top Company

GoodFirms Top Company

Pain points

Do you recognize your team?

What's happening

  • Combined ratio is under pressure and ops spend keeps climbing.
  • A CAT event exposed how fragile manual intake really is.
  • Underwriters are the bottleneck for every growth plan.
  • Customers churn because FNOL and servicing feel 20 years old.
  • Regulators are asking how your AI decisions get made.

How it feels

  • Skeptical — you've seen too many "AI" demos that fail on a real claim.
  • Pressured — leadership wants digital transformation without risk.
  • Frustrated — talent can't scale with submission volume.
  • Protective — one mispaid claim is a headline risk.
  • Envious of insurtechs shipping instant quotes and instant claims.

Where it hurts

  • ACORDs, PDFs, and emails that slow every workflow.
  • Underwriters context-switching across 8+ systems per submission.
  • Leakage on subrogation, fraud, and duplicate claims.
  • Core systems that resist modern tooling.
  • No consistent audit trail for AI-assisted decisions.

What we ship

Workstreams, real artifacts, measurable outcomes.

Every engagement decomposes into clear workstreams you can ship and measure. Here's the playbook for this segment.

Workstream

01

FNOL & claims intake

Multimodal intake agent: forms, photos, emails, voice → structured claim file in your core.
  • Multimodal extractor
  • Core integration
  • Confidence + review UX
  • Audit log
3× triage speed

02

Underwriting copilot

Agent grounded on appetite, guidelines, and rating docs — drafts decisions with citations.
  • Grounded RAG
  • Rating + rules hooks
  • Decision rationale
  • Supervisor view
+30% capacity

03

Policy servicing agent

Endorsements, COIs, renewals, and simple changes handled with policy-grounded reasoning.
  • Servicing agent
  • Document generation
  • Exception workflow
  • Customer UX
−50% handle time

04

Fraud & SIU triage

Signals, links, and rationale over claims — prioritizing real cases for investigators.
  • Signal library
  • Link analysis
  • Case rationale
  • Investigator queue
2× SIU yield

05

Subrogation & recovery

Agent reads claim files, spots recoverable cases, drafts demand packages for review.
  • Recovery detector
  • Demand drafts
  • Evidence pack
  • Review workflow
+15% recovery $

As seen in

Forbes
The Recursive
SVT
Breakit
Tech EU

After-state

What changes on the other side.

Claims cycle time cuts in half. Underwriters run 30% more submissions without hiring. Every AI decision lands in Guidewire / Duck Creek / your core with citations and reviewer sign-off. Fraud and subrogation stop leaking quietly.

How it feels

ConfidentFastIn controlProud of the loss ratioRespected by the board

What becomes possible

  • 01Modernize claims without replacing your core.
  • 02Grow premium without linearly growing underwriting headcount.
  • 03Turn loss-adjustment data into a durable competitive advantage.

Concerns, answered

The usual concerns — handled.

Concern 01

We've been burned by RPA and early AI — don't see what's different.

We ship grounded, evaluated agents with a human review UX and full citation trails. Not brittle scripts and not black-box LLMs — engineered for the insurance reality.

Concern 02

Our core (Guidewire / Duck Creek / homegrown) is a fortress.

We've wired agents into all of them. We integrate via sanctioned APIs, event buses, or controlled workflows — so the agent lives where your ops already live.

Concern 03

Regulators will kill anything touching underwriting or claims.

We design for explainability and audit from day one: cited sources, confidence, rationale, and logs. Regulators see evidence, not promises.

Concern 04

Our data is messy and siloed.

That's exactly the problem we're good at. Multimodal extraction, data contracts, and lightweight pipelines — we bring clean signal out of the mess.

Alternatives

Why us and not…

Horizontal insurtech platforms

Opinionated products; hard to fit your line of business. We build into your core and your playbook.

Big-4 insurance consulting

Heavy on roadmap, light on running agents. We ship production systems your ops actually use.

Generic RPA / doc AI vendors

Extract fields but don't reason. We add grounded decisioning with citations and review UX.

Founder & team

Senior humans,
AI-native craft.

100+

people trained

20+

companies transformed

9.4/10

avg. workshop rating

96%

AI adoption in 7 days

Talk to the founder

Mike Doroshenko

Product strategist and AI consultant with 10+ years of digital product strategy and AI transformation. Author of corporate training programs used by leading companies.

Supported by 15+ experts

from McKinsey, Google, and top tech companies.

Book a call with Mike
Mike — Founder of Vahue

Delivery outcomes.

Measurable results from products and AI systems delivered by Vahue.

Enterprise AIVahue case study

−27% TTR

Production NOC agents reduced repeatable incident resolution time by 27% while recording zero unauthorized execution in UAT and staging.

Exaware

Enterprise AI

Enterprise AIVahue case study

>90% automated

More than 90% of RFI responses were automated, moving turnaround from several days to a few hours without removing editing or audit history.

Global B2B Growth Partner

Enterprise AI

Enterprise AIVahue case study

Seconds, not hours

Every repair order could be scored in seconds at 75–80% agreement, while costly billing decisions remained behind human-defined confidence thresholds.

Amerit Fleet Solutions

Enterprise AI

Enterprise AIVahue case study

~10% → ~20%

An unstable scheduling voicebot doubled booking conversion from roughly 10% to 20% while becoming faster, less token-heavy, and easier to monitor.

Docplanner

Enterprise AI

AI-Native EngineersVahue case study

8 months → 6+

Five senior data scientists helped bring an eight-month NLP delivery down to just over six months and cleared inherited technical issues in about one month.

Retail NLP Delivery

AI-Native Engineers

AI-Native EngineersVahue case study

>95% accuracy

A classifier exceeded 95% accuracy, integrated through an API within days, and shipped with documentation for retraining on future data.

Consumer Email Startup

AI-Native Engineers

AI-Native EngineersVahue case study

Value from day one

Embedded specialists onboarded quickly and delivered models, workflow pipelines, deployment support, and ongoing production ownership across several business areas.

Sky

AI-Native Engineers

Team Training & ConsultingVahue case study

>30% adoption

The employee assistant reached more than 90% reported answer accuracy and more than 30% company-wide adoption across permissioned CRM and ERP data.

PioGroup

Team Training & Consulting

Team Training & ConsultingVahue case study

Run in-house

A cross-functional pilot, training, and rollout blueprint left the organization able to run, explain, and extend marketing-mix modeling independently.

Global Food Company

Team Training & Consulting

Team Training & ConsultingVahue case study

6-week roadmap

Several focused sessions converted clinical expertise and vendor distrust into a realistic product design, technical stack, cost range, and scalable roadmap.

Clinical Tools Company

Team Training & Consulting

Contact

We're here to deliver
 

Tell us where you are and what you're trying to ship. We reply within 24 hours with a diagnosis, a shortlist of quick wins, and the smallest next step we'd recommend.

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