Validated structured data out of real-world documents, with confidence thresholds, an exception queue for a person, and a full audit record of every decision.
The number that moves
Cycle time per case · straight-through rate · exception rate
Industries · BFSI
Financial Services & BFSI
Banking, insurance, lending and wealth all have enormous document volumes, heavy servicing load and a compliance function that will — correctly — stop anything it cannot audit. The constraint here is almost never whether AI can do the task. It is whether you can prove what it did.
The sector with the most obvious AI use cases and the least tolerance for a system that cannot be governed.
What this sector loses today
The leak is rarely where the budget goes.
Four patterns that recur here. If two are recognisably yours, the answer is usually smaller than the programme you were being sold.
KYC, onboarding and claims documents processed by hand at volume.
A cost base that scales linearly with growth, and a cycle time customers feel.
Servicing queries — statements, status, eligibility — handled by trained staff.
Expensive people answering questions that never needed judgement.
Advisors and relationship managers searching policy documents for answers.
Slow responses, inconsistent answers, and risk when someone guesses.
AI pilots blocked at the risk review because nothing was designed to be audited.
Budget spent, nothing in production, and internal appetite for the next attempt gone.
What changes
How the system behaves, before and after.
These are properties of the architecture, true from the day it goes live. We baseline the numbers underneath them before we build, so the delta is measured against your own starting point rather than against an industry average.
Applications
Where AI actually pays here.
Each is a defined build with a number against it. They can be scoped and bought individually.
Answers grounded strictly in your approved documents with the source cited, and a designed refusal when the answer is not in them. Built for advisors first, customers second.
The number that moves
Answer accuracy against threshold · time to answer
Handles status, statements, eligibility and process questions, authenticates properly, and escalates anything advisory to a licensed person.
The number that moves
Containment rate · cost per servicing interaction
Usage policy, data-handling design, model risk register, human-oversight mapping and audit trail — written for your risk function to review properly rather than to be waved past them.
The number that moves
Time from proposal to risk sign-off
Every candidate scored on value, feasibility, regulatory exposure and adoption, with the explicit list of what we recommend you do not build.
The number that moves
Cost per transaction · hours returned per week
What we would build first
Order matters more than ambition.
Wrong order is the commonest reason AI work here stalls. This is the order we would argue for.
01
Governance first, deliberately
In this sector the review is the schedule. We produce the governance and risk pack alongside the first architecture, not after it, because retrofitting auditability is how BFSI pilots die.
02
One document workflow, end to end
Pick the highest-volume, lowest-ambiguity flow. Prove the accuracy baseline, the exception path and the audit record on something real.
03
Servicing, once the pattern is trusted
Customer-facing comes after internal, because the first thing that speaks to a customer should be built on a pattern your risk function has already signed.
Before your risk function signs
What they are going to ask.
In this sector the review is the schedule. All of it is designed in from the first architecture session.
- Data residency, in-VPC deployment, and open-weight models hosted by you where policy requires it
- PII redaction before any external model call, with full audit logging of inputs and outputs
- Pass thresholds agreed in writing before go-live and measured continuously afterwards
- Human sign-off gates sized to the consequence of an error, with the approval recorded
- Model risk register, versioning, and a documented migration path when a provider deprecates a model
- No advisory or suitability output from an automated system — that boundary is designed in, not assumed
Questions we get in this sector
The ones that decide it.
Can the data stay inside our own environment?
Yes. We design for residency from the first architecture session — regional endpoints, deployment inside your VPC, and open-weight models hosted by you where policy requires it. PII is redacted before any external call, and everything is logged. This is a normal requirement in this sector, not an exception we accommodate reluctantly.
How do we prove to an auditor what the model did?
Every call is traced: the input, the retrieved context, the model and version, the output, the guardrails applied, and any human approval. That record is the deliverable as much as the software is. A system that cannot answer this question should not be in a regulated workflow.
What happens when the model underneath changes?
It will — providers deprecate versions on their own schedule. That is why every build ships with an eval suite: we re-run it against the new model, see exactly what moved, and migrate deliberately. Firms without that suite discover the change from a customer complaint.
Put a number on it
The manual-handling cost, as arithmetic
Four lines, and the blanks are deliberate — they are your numbers, not an average we invented. We fill them in with you on the call.
Cases processed per month____
Minutes of a person per case____
Fully loaded cost per hour₹____
Annual cost of the current process= ____ × ____ ÷ 60 × ₹____ × 12
If that number does not comfortably exceed the build and running cost, we will tell you on the call and you should not do it. Roughly a third of the cases we look at fail this test.
Free working prototype
Send us ten real documents with the PII stripped. We will show you the extraction, the confidence scores and the audit record.
Built to be shown to your risk function, not to a product demo audience. The exception path and the audit trail are in the demo, because in this sector they are the product.
48 hours
Your KYC or claims documents, extracted
48 hours
A retrieval agent over your policy documents
48 hours
The audit record, in full
48 hours
A servicing agent that authenticates first
This is the same free prototype that comes out of the 30-minute call and plan. It arrives faster here because the four scenarios above are already scoped.
What does the work
Stop reading about it. Watch it work.
Tell us the one thing that is costing you most, and we build it on your own material within 48 hours. Free, yours to keep, and the fastest way to find out whether any of this applies to you.