−27% TTR
Production NOC agents reduced repeatable incident resolution time by 27% while recording zero unauthorized execution in UAT and staging.
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
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
Why it works
01 · Principle
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
Agents triage alerts, dedupe cases, draft SARs, and surface rationale. Analysts spend their day on judgment calls, not on copy-paste.
03 · Principle
We deploy inside your VPC / tenant (Bedrock, Azure OpenAI, on-prem open-weights). Zero-trust, data residency, and PII redaction are table stakes.
Outcomes
−60%
onboarding TAT
2×
fraud triage speed
100%
explainable decisions
0
data leaves tenant
Awards
Pain points
What's happening
How it feels
Where it hurts
What we ship
Every engagement decomposes into clear workstreams you can ship and measure. Here's the playbook for this segment.
01
Fraud & AML copilot
02
KYC & onboarding AI
03
Lending & credit copilots
04
RM / advisor assistant
05
Core & data modernization
As seen in
After-state
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
Concerns, answered
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
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
100+
people trained
20+
companies transformed
9.4/10
avg. workshop rating
96%
AI adoption in 7 days
Talk to the founder
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.

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