AI that moves money — safely, explainably, in production.

We embed AI-native engineers into banks and fintechs to ship copilots for fraud, AML, KYC, lending, and relationship managers — the kind your risk, compliance, and InfoSec teams will actually sign off on.

From instant onboarding and real-time fraud to predictive lending, M&A data intelligence, and embedded-finance rails — architected for zero-trust, MRM, and audit from day one.

Trusted by teams at

Shopify
Qonto
Leafworks

The mandate

Turn regulated banking AI from a PowerPoint into a release lane.

The hard part isn't the model — it's shipping it through MRM, InfoSec, second line, and legacy cores without losing a quarter. We design inside those constraints, wire into your private tenant, and make explainability a first-class deliverable, not a retrofit.

What you get

  • Real-time fraud & AML triage copilots — grounded, logged, reviewable.
  • KYC / onboarding acceleration with document AI and policy-grounded reasoning.
  • Credit & lending copilots: predictive underwriting, covenant tracking, portfolio Q&A.
  • RM / advisor assistants on your product, compliance, and CRM context.
  • Core modernization: agents over legacy mainframes, cores, and data warehouses.
Scope a regulated use-case

Why it works

Why this approach wins.

01 · Principle

Regulator-ready by construction

MRM artifacts — model card, intended-use, monitoring plan, limitations, change log — ship with the feature. Sign-off is a review, not a rebuild.

02 · Principle

Fraud and AML that think in seconds

Agents triage alerts, dedupe cases, draft SARs, and surface rationale. Analysts spend their day on judgment calls, not on copy-paste.

03 · Principle

Your data never leaves the perimeter

We deploy inside your VPC / tenant (Bedrock, Azure OpenAI, on-prem open-weights). Zero-trust, data residency, and PII redaction are table stakes.

Outcomes

The outcomes we commit to.

−60%

onboarding TAT

fraud triage speed

100%

explainable decisions

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

  • Your regulator just asked about your AI governance.
  • Fraud losses are growing faster than analyst headcount.
  • Onboarding TAT is bleeding new-account conversion.
  • A challenger bank shipped an advisor copilot your stack can't match.
  • Lending teams are drowning in unstructured covenant & credit memo work.

How it feels

  • Cautious — one bad AI decision ends careers here.
  • Frustrated that every AI pilot dies in second line.
  • Envious of neobanks shipping things you're still scoping.
  • Protective of customer trust above all else.
  • Tired of vendors whose claims evaporate under regulator scrutiny.

Where it hurts

  • MRM cycles that take 9–12 months per use-case.
  • Public LLM APIs blocked by InfoSec — no clear private path.
  • No clean audit trail from model output to customer action.
  • Silos between data science, risk, compliance, and the line of business.
  • Legacy cores and data warehouses that AI tools can't reach.

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

Fraud & AML copilot

Analyst-assist over fraud / AML queues: triage, dedupe, rationale, SAR drafting.
  • Queue integration
  • Retrieval + rules layer
  • Decision log
  • Second-line review UX
2× triage speed

02

KYC & onboarding AI

Document AI + policy reasoning for instant account, merchant, and corporate KYC.
  • Doc extraction
  • Policy-grounded checks
  • Exception workflow
  • Audit trail
−60% TAT

03

Lending & credit copilots

Predictive underwriting, covenant monitoring, and portfolio Q&A over your CRM + docs.
  • Credit memo agent
  • Covenant monitor
  • Portfolio Q&A
  • Risk dashboards
3× analyst output

04

RM / advisor assistant

Grounded assistant on product, compliance, and CRM context — with supervision hooks.
  • Grounded RAG
  • Compliance guardrails
  • CRM + call-prep actions
  • Supervisor views
+20% RM capacity

05

Core & data modernization

Agents and APIs over legacy cores, mainframes, and warehouses — without ripping them out.
  • Integration layer
  • Agent tools
  • Data contracts
  • Migration runway
Legacy → AI-ready

As seen in

Forbes
The Recursive
SVT
Breakit
Tech EU

After-state

What changes on the other side.

AI ships quarterly across fraud, AML, KYC, lending, and advisor workflows — inside your tenant, with full MRM artifacts, audit trails, and explainability. Analysts work on judgment; agents carry the load. The regulator reads your dashboards, not your slides.

How it feels

CalmRegulator-confidentInnovating inside the linesRespected by second lineTrusted by customers

What becomes possible

  • 01Turn banking AI from an annual program into a quarterly release lane.
  • 02Bring fraud and AML response to real-time without growing headcount linearly.
  • 03Unlock legacy core and data assets as first-class fuel for agentic products.

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 packs 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, Vertex, or open-weights on your hardware. No customer data ever leaves your perimeter.

Concern 03

Our core is 30 years old — nothing will integrate.

We've wired agents over mainframes, legacy cores, and decades-old warehouses. We bring integration patterns, not rip-and-replace plans.

Concern 04

We already have a "GenAI platform" vendor.

Good. We assess what they actually deliver against your MRM, grounding, and domain needs, and we layer — not thrash — on top of it.

Alternatives

Why us and not…

Big-4 GenAI practices

Deck-rich, deploy-poor. You pay for slides; we hand you production systems with MRM packs attached.

Horizontal LLM platforms

Strong tooling, weak banking grounding. We bring the BFSI muscle: fraud, AML, KYC, lending, MRM.

Neobank-style in-house squads

Fast but lean on governance. We bring the regulated-environment discipline without killing velocity.

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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possibilities.

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