By industry
AI & Automation for Financial Services
Automation that survives an RBI inspection
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
With human review retained on exceptions
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.
What an auditor actually asks for
Not "is the model accurate." Four questions, in roughly this order.
What did it read, and when. Per-case input records, retained for your policy period. This is the one most projects retrofit badly, because logging inputs after the fact means logging a reconstruction rather than the actual input.
How was accuracy established. A held-out evaluation set of your own cases, labelled by your own people, with the methodology written down and the result reproducible. "The vendor says 95%" satisfies nobody.
Who could override it, and did they. Named authority, and evidence the oversight was real rather than nominal. A reviewer who approves four hundred cases an hour is not oversight, and the logs will show it.
What happens when it degrades. Monitoring thresholds, who is paged, and the documented fallback to the manual process. See drift — accuracy measured at go-live is not a permanent property.
Where the work overlaps other sectors
Most of what we build here is document processing and back-office automation with a heavier evidential burden. The engineering is not exotic; the difference is that every design decision has to survive being explained to someone whose job is to find the weakness in it.
That constraint is worth treating as a feature. Systems built to be inspected tend to be systems that can be debugged, handed over and safely changed two years later by someone who was not there when they were built.
FAQ
Questions we get asked
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.
What does a first engagement cost and how long does it take?
Two weeks for a readiness audit that sequences the backlog against your actual volumes, then four weeks to put one process into production. Reconciliation for a single settlement file, or KYC extraction for one document type, are the usual starting points. We scope the first one narrow on purpose — a working narrow system surfaces the data problems that a longer planning exercise would have missed.
We already have a reconciliation tool. Why would this be different?
It may well not be, and if your existing tool matches cleanly on structured files, keep it. The difference shows up on the breaks it cannot classify — the ones that end up in a spreadsheet for someone to work through each morning. Automating the classification and evidence-gathering on that residue is usually where the remaining hours are, not in the matching itself.
Our internal audit function will want to test this. What do they get?
A documented decision path, per-case input and output records, the evaluation set with measured accuracy, the human oversight design with named authority, and logs showing the oversight happened. They can re-run the evaluation set themselves. We build for the assumption that someone hostile and competent will inspect it, because eventually someone will.
How much does the residency constraint actually cost us in capability?
Measurably, and we quantify it on your task rather than in the abstract. On document extraction and reconciliation classification the gap between a frontier API and a good self-hosted open-weight model is usually small enough not to matter. On complex multi-step reasoning it is not. We benchmark both against the same evaluation set and give you the number before you commit to the operational cost of self-hosting.
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.
Or email [email protected] · we reply within 1 business day