Trusted GenAI — for regulated environments.

We build AI systems banks, insurers, and fintechs can actually deploy: governance-first, auditable, and tuned for risk, fraud, and customer ops.

From KYC/AML copilots to relationship-manager assistants to fraud triage — architected for model risk management, explainability, and the regulators in your room.

Trusted by teams at

Shopify
Qonto
Leafworks

The mandate

Deploy GenAI inside a regulated bank — without risking your license.

The barrier in BFSI is rarely the model; it's MRM, data residency, explainability, SOD, and sign-off. We design to those constraints from day one and move through them methodically, with your risk and compliance teams in the build loop.

What you get

  • Model Risk Management (MRM) aligned to SR 11-7 / SS1/23 / local guidance.
  • Data residency and private-tenancy deployments (Bedrock, Azure OpenAI, on-prem).
  • Explainability layers: traces, feature attributions, decision rationales.
  • Fraud, AML, KYC copilots with clear SOD and second-line review.
  • RM / advisor assistants grounded on your product and compliance docs.
Map a regulated use-case

Why it works

Why this approach wins.

01 · Principle

Risk is designed in, not retrofitted

MRM artifacts (model inventory, intended use, limitations, monitoring plan) are deliverables — not a last-minute scramble before a go-live review.

02 · Principle

Explainable by default

Every AI-assisted decision carries its rationale and retrieval trace. Your second line can re-review without reverse-engineering prompts.

03 · Principle

Your data never leaves your perimeter

We deploy to your VPC / tenant with PII redaction and data classification baked in. No shadow data flows to public model providers.

Outcomes

The outcomes we commit to.

100%

explainable decisions

−45%

L1 review time

fraud triage speed

0

data leaves tenant

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

  • Regulator asked about your AI governance.
  • Fraud losses ticking up faster than analyst headcount.
  • A new product launch needs faster AML review.
  • RM productivity targets slipping as product complexity grows.

How it feels

  • Cautious — one bad AI decision ends careers here.
  • Frustrated that every AI project gets stuck in second line.
  • Envious of neobanks shipping copilots your bank can't.
  • Protective of customer trust above all else.

Where it hurts

  • Endless MRM cycles before anything ships.
  • Public model APIs blocked by InfoSec.
  • No clean audit trail from model output to action.
  • Vendor claims that evaporate under regulator scrutiny.
  • Silos between data science, risk, and compliance.

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

Trusted GenAI pilot

One regulated use-case shipped to prod — with MRM artifacts and sign-off.
  • Use-case scoping
  • MRM pack
  • Private deployment
  • Go-live review
Sign-off

02

Risk & fraud copilots

Analyst-assist for KYC/AML/fraud queues — grounded, logged, reviewable.
  • Queue integration
  • Retrieval layer
  • Decision log
  • Second-line view
2× triage

03

Governance & audit

Policies, inventories, monitoring, and audit exports for all GenAI in production.
  • AI policy
  • Model inventory
  • Monitoring dashboard
  • Audit export
Regulator-ready

04

Explainability layer

Make every AI-assisted decision traceable, reviewable, and reproducible.
  • Trace store
  • Rationale UX
  • Reproducibility kit
  • Review workflow
100% explainable

As seen in

Forbes
The Recursive
SVT
Breakit
Tech EU

After-state

What changes on the other side.

GenAI is deployed across risk, fraud, and customer ops — inside your tenant, with MRM artifacts, explainability, and audit trails that pass regulator review. Innovation ships quarterly, not annually.

How it feels

CalmRegulator-confidentInnovating inside the linesRespected by second line

What becomes possible

  • 01Stand up an enterprise-wide AI governance layer.
  • 02Reduce L1 risk-analyst load so senior capacity moves to complex reviews.
  • 03Shorten product-launch AML review from weeks to days.

Concerns, answered

The usual concerns — handled.

Concern 01

Our regulator hasn't approved GenAI in customer workflows.

We start where regulators are comfortable — internal analyst copilots — with MRM artifacts ready. Customer-facing scope expands as evidence accumulates.

Concern 02

Public LLMs are blocked by InfoSec.

We deploy to your VPC / private tenant (Bedrock, Azure OpenAI, open-weights). No customer data leaves your perimeter. Ever.

Concern 03

MRM will take 12 months.

Not if MRM artifacts are part of the build. We co-design the model card, intended-use doc, monitoring plan, and limitations in sprint one — not in month eleven.

Concern 04

We already have a vendor for “AI.”

Fine — we'll assess what they're actually delivering and where the gaps are in governance, explainability, and domain grounding. We layer, we don't thrash.

Alternatives

Why us and not…

Enterprise LLM platforms

Horizontal tooling; weak on BFSI-specific MRM and sector grounding.

Big-4 GenAI practices

Deck-rich, deploy-poor. We hand you production systems, not roadmaps.

Neobank-style in-house

Fast but lean on governance. We bring the regulated-environment muscle.

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

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