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
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.
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
2×
fraud triage speed
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
- 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.
01
Trusted GenAI pilot
- Use-case scoping
- MRM pack
- Private deployment
- Go-live review
02
Risk & fraud copilots
- Queue integration
- Retrieval layer
- Decision log
- Second-line view
03
Governance & audit
- AI policy
- Model inventory
- Monitoring dashboard
- Audit export
04
Explainability layer
- Trace store
- Rationale UX
- Reproducibility kit
- Review workflow
As seen in
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
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.
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
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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”
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CTO at Social Curator
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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
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Steinar Aas
CEO & Co-Founder at Asio AS
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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
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