AI in fintech fails on audit, not on accuracy.

Why now

Every retail bank, lender and payments company now has an AI mandate and a risk function that can veto it. The teams getting anything live are the ones who stopped asking the model to be trustworthy and started making its decisions inspectable: grounded answers with citations, a fixed set of actions, and a person on the hook for anything that moves money.

• 01

  Disputes and chargebacks eat the queue

  Tier-1 volume is dominated by the same twelve questions about a transaction, a hold, or a failed KYC step — each one requiring an agent to open three systems to answer.

• 02

  Underwriting is document archaeology

  Analysts read bank statements, GST filings and payslips by hand to fill a form that a structured extraction pipeline could pre-populate with citations back to the source page.

• 03

  Policy lives in PDFs nobody can find

  The answer to a compliance question exists — in a 180-page circular, three revisions ago. Staff guess, and the guess becomes the audit finding.

• 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.

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

• 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
• AI governance for financial servicesModel inventories, risk classification, evaluation policy and human-oversight design — written to survive an internal audit rather than a conference talk.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

1. Step 1 · Validate

   30-day Sprint

   Prove the use case before you commit. Working prototype on real data, eval scores, and an honest signal in 30 days. Fixed scope, fixed fee.

   $4,500 fixed

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2. Most teams land here

   Step 2 · Build

   Prototype → Production

   Turn the validated prototype into a real product. Auth, DB, payments, tests, monitoring, deployed. Sprint fee credits toward this engagement.

   from $6,000

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3. Step 3 · Scale

   Managed Retainer

   Ongoing operation, eval cycles, model iteration, and cost guards. We keep the system improving so your team can focus on growth.

   from $750/mo

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FAQ

AI in fintech: the questions we get.

• Can you work inside our compliance perimeter?

  Yes. We deploy into your AWS, GCP or Azure account rather than ours, with no data leaving your VPC. Where an approved-vendor list rules out the frontier APIs, we build on open-weight models you host — the architecture is the same, the quality bar moves and we'll tell you by how much before you commit.

• How do you prove the system is safe enough to launch?

  With an eval suite built from your own historical tickets or cases, versioned in the repo and run on every release. You get accuracy, refusal rate, and cost per execution as numbers — the same artefact your risk committee needs to sign anything off.

• What happens when the model is wrong in production?

  It gets caught by the guardrail it was designed to hit: low-confidence retrieval refuses rather than answers, out-of-policy actions route to a human, and every execution is logged with its inputs so you can reconstruct what happened. Errors become eval cases in the next release.

• AI for HealthcareHealthcare AI earns trust by being narrow. We build systems that retrieve, draft and route — and leave every clinical judgement with a person who signs it.Read
• AI for Retail & E-commerceA bot that says 'let me check that for you' and can't is worse than no bot. We connect agents to the order, the inventory and the returns policy so they can finish the job.Read
• AI for SaaSThe model is the easy part. Multi-tenancy, per-seat cost, evals and a permissions model that doesn't leak across tenants are what turn a demo into something you can charge for.Read