The paperwork that gets the pilot approved.

• 01

  Model inventory and risk tiering

  Every model, prompt and vendor in one register, tiered by impact, with the control set each tier requires. Usually the first thing an auditor asks for and the last thing anyone has.

• 02

  Human-oversight design

  Which decisions require a person, what they see when they get one, and how their override is recorded — specified per use case rather than as a blanket policy nobody follows.

• 03

  Vendor and model due-diligence

  Data-handling, residency and retention assessed per provider, with the fallback plan for the day a model is deprecated or a term changes mid-contract.

• 01

  Every answer needs a receipt

  We ground responses in your own documents and return the citation with the answer. If the retrieval finds nothing, the system says so instead of improvising — that refusal path is tested like any other feature.

• 02

  Models never move money

  The LLM classifies, drafts and retrieves. Balance changes, limit increases and refunds run through your existing deterministic services behind an approval step, with the full prompt and decision written to an audit log.

• 03

  Data residency is a build constraint, not a setting

  For RBI, PCI-DSS and SOC 2 scopes we deploy inside your VPC or account with zero data egress, and pick models — including open-weights — that can legally sit where your data sits.

1. 1

   Stakeholder interviews

   30–45 minutes with each executive and team lead. We surface the real bottlenecks (not the ones the deck says).

2. 2

   Use-case pipeline

   Score 20–40 candidate AI use cases on impact × feasibility × strategic fit. Pick the top 3 with quantified ROI.

3. 3

   Build vs buy

   Honest answer for each pillar — sometimes the right move is Notion AI or a Zapier flow, not a bespoke build.

4. 4

   Governance framework

   Policy, approval workflow, prompt-injection / PII / IP guardrails. Aligned with your compliance posture.

5. 5

   90-day plan

   Quarter-by-quarter roadmap, hiring plan, budget, vendor selection, and KPIs the CFO will actually accept.

• Stripe
• Razorpay
• Plaid
• Salesforce Financial Services Cloud
• Zendesk & Intercom
• Snowflake / BigQuery
• Postgres with row-level security
• Twilio
• Internal core-banking APIs

FAQ

AI governance for financial services: your questions.

• Is this just a document exercise?

  It ends in documents, but it starts in your codebase and your vendor contracts. We'd rather find that a live prompt is sending PII to an unapproved endpoint than write a policy saying it shouldn't.

• Do you cover the EU AI Act and Indian DPDP together?

  Yes, as one control set with the obligations mapped per jurisdiction. The overlap is large; keeping two parallel frameworks is how organisations end up complying with neither.

• Is this just a deck?

  No. Every strategy engagement ships at least one working artifact — a costed roadmap, a governance template ready for legal, or a vendor negotiation outcome. Decks alone are useless.

• Do you work with non-technical executives?

  Yes — most of our strategy work starts with non-technical leaders. We translate between board-room and engineering-room.

• AI agents for fintechAgents wired into your ledger, KYC provider and helpdesk — resolving the repetitive half of the queue end to end, and escalating the rest with the context already gathered.Read
• RAG for banking and financial servicesRetrieval over circulars, product terms and internal procedure — returning the clause, the revision date, and a link to the page it came from.Read
• Fintech prototype to productionWe take a working fintech prototype and add the things that were never in scope: real auth, migrations, secrets handling, audit logging, and a test suite that survives the next regeneration.Read

• Healthcare AI governance and readinessUse-case triage against clinical risk, a data-handling design that survives a HIPAA or DPDP review, and an oversight model your clinical governance body will actually accept.Read
• AI governance for insurersRisk classification, model documentation and oversight design for insurance use cases — built around what a complaint, an audit or an ombudsman review will ask for.Read
• AI governance for legal teamsAcceptable-use policy, confidentiality controls, and an approval process for AI tools that lawyers are already using with or without permission.Read