Can we use a frontier model API at all, given our data rules?

Often yes, with the right architecture — tokenising or redacting identifiers before they leave your environment, keeping the record of decision in-region, and contracting for zero retention. Where that is not acceptable, self-hosted open-weight models are the alternative and we will quantify the capability gap rather than gloss over it.

Will an auditor accept an AI-assisted control?

They accept controls they can inspect. That means a documented decision path, a record of inputs and outputs per case, defined human oversight with named authority, and evidence the oversight actually happened. We build those as part of the system, which is what makes the conversation straightforward.

How do you handle model risk documentation?

Purpose, scope, limitations, evaluation methodology, measured accuracy, known failure modes and monitoring thresholds — written to the standard your model risk policy requires. Covered under AI Governance and delivered alongside the build.

Our core banking system has no useful API. Now what?

Common. We work with database replication, batch file interfaces or a purpose-built integration layer, and we are explicit about which parts become brittle as a result. Sometimes the honest recommendation is to fix the integration surface first.

By industry

AI & Automation for Financial Services

Automation that survives an RBI inspection

Discuss your requirements

We design around

RBI IT and cyber security framework
DPDP Act 2023
PCI DSS
SEBI reporting requirements
ISO 27001

70%
KYC processing time reduction: 94%
Reconciliation breaks caught nightly: 100%
AI decisions traceable to inputs

What we hear

The problems that bring people to us

If several of these describe your week, there is almost certainly something worth automating.

KYC and onboarding backlogs that lose applicants mid-funnel
Reconciliation across core banking, payment rails and ledgers done manually
Dispute and chargeback handling with SLA exposure
Regulatory reporting assembled by hand each cycle
Data residency and audit requirements blocking cloud AI adoption

What we build

Where automation pays off in financial services

KYC and onboarding automation

Document extraction, validation against source registries and risk scoring, with human review on anything below threshold. Cuts onboarding time without weakening the control — and every decision carries a traceable record of what was read and why it passed.

Reconciliation that runs nightly

Matching across core banking, payment gateways, settlement files and the ledger, with only genuine breaks surfaced. Breaks found the next morning are cheap; breaks found at month end become investigations.

Dispute and chargeback triage

Classify, gather the evidence pack from across systems, and draft the response for a human to approve. Preserves SLA compliance when volume spikes without adding headcount.

Reporting with lineage

Regulatory and management reporting generated from the modelled data layer, with every figure traceable to source. Removes the spreadsheet step that is the most common source of restatements.

Residency-aware architecture

For workloads that cannot leave India, we design around in-region deployment and self-hosted models, and quantify the capability trade-off honestly before you commit.

The constraint that shapes everything

In financial services the question is never only "does it work." It is "can you show me how it decided, who reviewed it, and where the data went."

That changes the architecture rather than just the documentation. Audit logging, decision lineage, residency boundaries and human oversight with defined authority all have to be designed in. Bolted on afterwards, they cost several times more and frequently mean rebuilding the retrieval layer.

Where the return is largest

Reconciliation and onboarding, consistently. Both are high volume, rule-bound, and currently absorbing skilled people who could be doing something harder. Both also have a verifiable correct answer, which means accuracy can be measured rather than asserted — a precondition for getting a control approved.

Dispute handling comes next, usually because SLA exposure makes the cost of a backlog immediate and quantifiable.

Residency, practically

For Indian entities under RBI expectations, the practical pattern is: keep the authoritative record and the audit trail in-region, minimise what crosses a boundary by redacting or tokenising identifiers first, and contract for zero retention with any external provider. Where even that is unacceptable, self-hosted models in your own VPC are viable — with a real capability trade-off that we will measure on your task before you commit to the operational cost.

FAQ

Questions we get asked

Talk to someone who has worked in financial services

Bring a process that annoys you. In 30 minutes we will tell you whether AI helps, what it would cost, and where it would fail — even if the answer is don't bother.

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