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
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.
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
2×
fraud triage speed
100%
explainable decisions
0
data leaves tenant
Awards
Proud moments.
Top 1% on Clutch Global
Top AI Strategy Company 2025
Clutch Fall Champion 2024
Inc. 5000 Fastest Growing
Breakthrough of the Year 2019
Member of Forbes Tech Council
Voice & Speech Recognition 2024
Top Blockchain Company 2024
Innovators of the Year 2019
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.
01
Fraud & AML copilot
- Queue integration
- Retrieval + rules layer
- Decision log
- Second-line review UX
02
KYC & onboarding AI
- Doc extraction
- Policy-grounded checks
- Exception workflow
- Audit trail
03
Lending & credit copilots
- Credit memo agent
- Covenant monitor
- Portfolio Q&A
- Risk dashboards
04
RM / advisor assistant
- Grounded RAG
- Compliance guardrails
- CRM + call-prep actions
- Supervisor views
05
Core & data modernization
- Integration layer
- Agent tools
- Data contracts
- Migration runway
As seen in
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
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.
Case studies
Where ideas become impact.
Behind every system we ship is a team that moved from uncertainty to measurable outcomes. A few recent ones.
Case 01 · Client
Wealth Management Company
Objective
The goal was to integrate AI tools into everyday work across all roles and increase overall productivity.
Results
85%
of employees use AI tools daily in workflows
70%
of routine queries resolved via GPT assistant within the first 2 weeks
5 min
Average response time reduced from 1 hour to 5 minutes
52
ready-to-use prompts created for key scenarios (finance, presale, legal, HR)
12
AI agents deployed for quality, sales, finance, and executive dashboards
100%
prompts reviewed for data security compliance
Stack
ChatGPT Enterprise, n8n, Cursor, RAGDB (vector database), Power BI + Bloomberg GPT, Miro, Whisper / Coqui
Case 02 · Client
E-Commerce Platform
Objective
Automate customer support and optimize product recommendation systems using AI.
Results
60%
reduction in customer support tickets
3x
increase in product recommendation conversion rate
24/7
Automated support coverage with AI chatbot
8
custom AI workflows deployed across departments
40%
faster content generation for marketing campaigns
95%
customer satisfaction score with AI-assisted support
Stack
Anthropic API, LangChain, Pinecone, Next.js, Vercel, PostgreSQL, Redis, NanoClaw
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 30+ experts
from McKinsey, Google, and top tech companies.
Testimonials
Our clients said it best.
Patrik Dvořák
CEO, SECTOR 31 s.r.o.y
“Vahue's responsiveness and accuracy were impressive. We highly recommend them”
Philipp Lenz
Co-Founder, parloo.de
“There are a lot of companies that offer similar services but we've had an end-to-end good experience with them.”
Patrik Dvořák
CEO, SECTOR 31 s.r.o.y
“Vahue's responsiveness and accuracy were impressive. We highly recommend them”
Jacob Berg
CTO at Social Curator
“I appreciated the level of comfort Vahue made us feel. It was like being a part of a family.”
Georg Winkler
CEO, Xpertify
“The different and very profound skillset of the Vahue team was very impressive.”
Prasanna Elvis Eswara
Principal Consultant, Roost Digital
“They were proactive and seemed eager to build a relationship.”
Jacob Berg
CTO at Social Curator
“I appreciated the level of comfort Vahue made us feel. It was like being a part of a family.”
Georg Winkler
CEO, Xpertify
“The different and very profound skillset of the Vahue team was very impressive.”
Prasanna Elvis Eswara
Principal Consultant, Roost Digital
“They were proactive and seemed eager to build a relationship.”
Bartek Czerwinski
CTO, Quik
“Vahue has the ability to dive in and get the work done creatively with a lot of personal input.”
Steinar Aas
CEO & Co-Founder at Asio AS
“Their flexibility and genuine interest in finding the best solution for the product was impressive.”
Georg Winkler
CEO, Xpertify
“The different and very profound skillset of the team was very impressive.”
Bartek Czerwinski
CTO, Quik
“Vahue has the ability to dive in and get the work done creatively with a lot of personal input.”
Steinar Aas
CEO & Co-Founder at Asio AS
“Their flexibility and genuine interest in finding the best solution for the product was impressive.”
Blog
Perspectives that matter.
Deploying LLMs Securely in Enterprise Environments
A practical guide to integrating large language models with sensitive business data while staying compliant and secure.
Evaluating Code Data Sources for Training Large Language Models
A practical comparison of the major code dataset sources — from open-source repos to dedicated coding teams — and how to choose the right one.
The Case for Human-Written Code in LLM Training
Why human-authored code remains essential for building reliable coding assistants — and where synthetic data falls short.
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.
Get more ROI from AI. Get Vahue.