Your agentic AI transformation starts here.

We help banks and financial institutions transform their operations with AI and Agentic workflows that deliver real value.

How we work

A clear path from opportunity to production.

Most banks don't need another strategy deck. They need a partner who can find the right workflow, prove it works, and ship it. Each stage has a fixed scope and a clear deliverable. You decide what comes next.

Book a discovery call
01
Free · 30 min

AI Opportunity Assessment

A free 30-minute call directly with the founders. We talk through where you're at, what you're considering, and where AI could meaningfully move the needle for your business. No pitch, no decks. Just a real conversation about whether we're a fit.

02
2 weeks

Discovery Interviews

Over two weeks, we interview your team across different functions and roles to understand how your operations actually work, where the bottlenecks are, and what would unlock the most value. An AI agent runs alongside us, fact-checking in real time, surfacing patterns, and helping synthesize what we hear so nothing gets lost.

03
Detailed report

Workflow Report & Roadmap

Based on those interviews, we produce a detailed workflow report for your team. It identifies the highest-ROI use cases, lays out the path to automation or agentic workflow for each, and gives you a clear, board-ready view of where to invest first.

04
2 to 3 weeks

Prototype

We pick one use case together and build a working prototype on synthetic data that mirrors your real environment. You get to see, touch, and validate the workflow end-to-end before any real data or production systems are involved.

05
5 to 6 weeks

Production Deployment

Once the prototype is approved, we make it production-ready and deploy it at scale, with monitoring, evals, governance, and full handoff to your team. Subsequent workflows ship faster because the infrastructure compounds.

Use cases

Where agentic AI moves the needle for banks.

A small set of high-impact workflows where we ship faster on each successive engagement, because the underlying infrastructure compounds.

Operations

KYC & AML automation

Reduce alert backlogs, accelerate onboarding, and free analysts from low-value triage. Agents that handle the routine and escalate the complex.

Risk

Fraud operations & investigations

Cut investigation time per case. Agents that pull context across systems, draft narrative reports, and surface patterns analysts would miss.

Lending

Loan origination & underwriting

Compress decision cycles for commercial and consumer lending. Document parsing, exception handling, and policy-aligned decisioning support.

Customer

Servicing & complaint handling

Deflect routine inquiries, resolve complaints faster, and give human agents AI copilots that actually know your products and policies.

Compliance

Policy, regulatory & audit response

Accelerate response to regulatory inquiries and internal audits. Agents that navigate your policy library and assemble evidence in hours.

Internal

Knowledge & analyst copilots

AI copilots for credit analysts, RMs, and ops teams, grounded in your internal documents, controls, and approval workflows.

Workflow examples

Start where the signals are obvious.

The best first workflows are not the flashiest. They are high-volume, policy-bound, and full of manual handoffs. That makes them easy to measure and safer to review.

KYC operations
Signal: backlog + repeat checks

Customer onboarding exception review

  1. 01
    TriggerA new account is blocked because documents, sanctions checks, or beneficial ownership details need review.
  2. 02
    Agent workPulls the case file, highlights missing evidence, drafts the analyst note, and routes only unresolved exceptions.
  3. 03
    Human controlAnalysts approve the final decision. Every source, rule, and edit is logged for audit.
  4. 04
    MeasureQueue size, average handling time, reopened cases, and analyst rework.

Why it matters: easy to benchmark against current queue size, average handling time, and rework rate.

Fraud investigations
Signal: systems touched per case

Fraud case narrative builder

  1. 01
    TriggerA suspicious transaction alert needs a complete investigation summary before escalation or closure.
  2. 02
    Agent workCollects transaction history, customer context, prior alerts, device data, and drafts a concise investigation narrative.
  3. 03
    Human controlInvestigators keep judgment. The agent prepares evidence, cites sources, and flags gaps.
  4. 04
    MeasureCase throughput, missed-signal rate, QA findings, and escalation quality.

Why it matters: the value shows up in case throughput, fewer missed signals, and cleaner QA reviews.

Commercial lending
Signal: document-heavy decisions

Credit memo first draft

  1. 01
    TriggerA relationship manager submits a borrower package for credit review.
  2. 02
    Agent workExtracts financials, summarizes risks, checks policy fit, and prepares a draft memo with cited source passages.
  3. 03
    Human controlCredit officers edit, challenge, and approve. Policy exceptions are never auto-approved.
  4. 04
    MeasureMemo cycle time, missing-document loops, exception frequency, and reviewer edits.

Why it matters: strong fit when teams track memo cycle time, missing-document loops, and exception frequency.

Compliance response
Signal: evidence burden

Audit evidence assembly

  1. 01
    TriggerAn internal audit or regulator asks for controls, policy references, sample cases, and proof of review.
  2. 02
    Agent workSearches the policy library, gathers relevant artifacts, builds an evidence pack, and identifies missing control proof.
  3. 03
    Human controlCompliance owns the response. The agent never sends externally without review.
  4. 04
    MeasureHours spent collecting evidence, missing artifacts, review cycles, and audit response time.

Why it matters: high-value when evidence requests consume scarce senior compliance time.

Why us

What others won't tell you.

Most providers in this space sell strategy decks, hand delivery to people you've never met, or treat security as someone else's problem. We do this differently. Here's the honest version.

The typical approach

A 6-month "AI strategy" engagement that ends in a 60-page deck. Implementation is "phase 2" and somehow always slips.

Our approach

A working prototype on your data within weeks. The strategy comes from things we've actually shipped, not slides.

The typical approach

AI engineers who've never seen a model risk review, an SR 11-7 audit, or how a core banking system actually works.

Our approach

15+ combined years inside CIBC, ABN AMRO, Westpac, Wells Fargo, and Amex, plus production AI work at Microsoft Copilot, Amazon Alexa, and Atlassian.

The typical approach

Treat security and governance as "phase 3" — leaving you to fail your CISO review three months in and start over.

Our approach

Model risk docs, audit logging, and SAFE-MCP-aligned security baked in from day one. Our co-founder co-created the framework.

The typical approach

Senior partners pitch the deal, then hand delivery to junior staff you've never met.

Our approach

Two founders. You work directly with us. Every engagement has a co-founder accountable for the outcome.

The typical approach

Build every project bespoke and start from scratch each time, billing you for that overhead.

Our approach

Each engagement adds to a shared infrastructure layer for RAG, evals, governance, and integrations. Your second workflow ships in a fraction of the time.

Founders

Trusted AI experts.

Two founders. Combined experience inside the largest banks in North America, Europe, and APAC, plus production AI work at Microsoft Copilot, Amazon Alexa, Atlassian, and Expedia. You work directly with us, every engagement.

Vibha

Co-founder

5+ years delivering banking systems at CIBC, ABN AMRO, and Westpac. 10+ years leading AI products used by 500M+ people at Microsoft Copilot, Amazon Alexa, Expedia, and Atlassian.

Banking systems · 5 yrs AI products · 10 yrs Microsoft · Amazon · Atlassian

Bishnu

Co-founder

6+ years building AI/ML systems at Wells Fargo and American Express. Co-creator of SAFE-MCP, the AI agent security framework adopted by the Linux Foundation and OpenID Foundation.

SAFE-MCP · Linux Foundation AI/ML in banking · 6 yrs Wells Fargo · Amex

Let's find the workflow worth shipping.

30 minutes. We'll discuss one workflow you're considering, give you a candid assessment, and share what we've seen work at other banks. No pitch.

hello@exaalabs.com Book on calendar