Underwriters are reading PDFs by hand
Scans arrive. Someone keys them in.
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Financial services
AI you can explain to a regulator, line by line.
Document-heavy workflows, model governance and in-country infrastructure for lenders, insurers and fintechs.
- In-country residency
- Every decision reconstructable
- Shadow-mode rollout
The starting point
What we hear in the first meeting.
If none of these sound like you, we are probably not the right call yet — and we will say so.
Nobody can explain the decision
An unjustifiable score is a liability.
Residency is not negotiable
Regulated data stays in-country.
Our approach
Explainability is an architecture decision.
If the system has to justify itself later, it has to record the right things now. We design for the audit before we design for the accuracy.
How we work →-
Every decision traceable.
Inputs, evidence, model version, output — logged.
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Data stays in-country.
Your cloud account or an in-region tenancy.
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Humans where it counts.
Automated extraction, human decision.
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Model changes controlled.
Versioned, evaluated, approved before production.
Proof
How we have solved it in Financial Services.
Challenge, solution, outcome. Names withheld, numbers not inflated.
Lender
- Challenge
- Underwriters keyed income documents in by hand, then checked them by hand.
- Solution
- Extraction with confidence scores and a review queue for anything uncertain.
- Outcome
- File prep time fell sharply, and every field kept its source page.
Insurer
- Challenge
- Claims triage depended on who picked up the file.
- Solution
- Automated intake and fraud signals, with evidence logged beside each score.
- Outcome
- Consistent triage, and a decision a reviewer can reconstruct.
Fintech
- Challenge
- A bank partner's audit asked how model changes were approved.
- Solution
- Versioning, evaluation and approval controls with a full audit log.
- Outcome
- The audit question became a document, not a project.
Teams we work with
Who we build for
Different teams, different first project. The platform underneath is the same.
Lenders and NBFCs
Extraction, income verification, early-warning signals.
Insurers
Claims intake, fraud triage, policy search.
Capital markets
Research summarisation and filings extraction.
Fintech products
AI that survives a bank partner's audit.
Scope
What we deliver
Four things, in this order. Each one is useful on its own.
AI Discovery, risk in the room
Two weeks with operations, risk and technology.
Document intelligence
Extraction with confidence scores and human review.
Model governance
Versioning, evaluation, approval and the audit log.
In-country infrastructure
Dedicated compute with residency, backup and DR.
Engagement
How the engagement runs
No surprises about sequence, and no invoice before there is something to look at.
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Discovery with risk
Compliance in workshop one
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Control design
What is logged, who approves
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Pilot on closed cases
Accuracy measured before live
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Controlled rollout
Shadow, partial, then full
Under the hood
What it is built on.
Your existing systems stay. We add the layers that are missing and run them.
Data sources
- Scanned KYC and policy packs
- Core banking and policy admin
- Bureau and market feeds
- Claims and case systems
AI layer
- Extraction with confidence scores
- Retrieval with cited evidence
- Feature-level attribution
- Model registry and evaluation
Infrastructure
- In-country dedicated tenancy
- Immutable audit logging
- Backup and DR
- Role-based access control
Private cloud
In-country, single tenant, auditable.
Regulated workloads run on dedicated capacity in the region you nominate, with the logging, backup and access posture an audit expects to find already in place.
Explore private cloud- In-country residency
- Single-tenant isolation
- Immutable audit logs
- Backup and DR included
What changes
- 100%
- Decisions reconstructable
- In-country
- Residency, verifiable
- Shadow-first
- No live decisions untested
Worked examples
The write-ups behind this work.
Full situation, architecture and results on each one.
Document automation
Forty thousand scanned documents a month, keyed in by hand
11 → 3 People on data entry
Data migration
Migrating a 14-year-old platform with no data dictionary
7 mo Instead of the quoted 18
Cybersecurity
A SOC drowning in alerts that were almost all noise
11k → 40 Reaching an analyst weekly
Free, no call required
The Financial Services AI readiness checklist
The questions we work through before recommending anything — data readiness, hosting constraints, review process and the running cost at year two. Use it with any vendor, including the ones that are not us.
Get the checklist- What has to be reconstructable, field by field
- Where regulated data is allowed to live
- Who approves a model version
- What the audit trail costs to keep
Questions
Asked first, every time
Short answers. Longer ones are a conversation.
Can you prove why the model returned a particular result?
For retrieval-based systems, yes — the evidence the model saw is logged with the answer, so a reviewer can reconstruct the decision. For scoring models we favour approaches carrying feature-level attribution. Where a use case genuinely cannot be explained, we say so and recommend keeping a human decision-maker.
Does data leave the country?
Not unless you choose that. Deployments run in your own cloud account in the region you nominate, on your own hardware, or in a dedicated in-country tenancy.
How accurate is extraction on poor scans?
It varies by document type and scan quality, which is why we benchmark on your actual documents during discovery rather than quoting a headline number. Low-confidence extractions route to a human instead of being guessed.
Will this pass an internal audit?
We build the logging, versioning and approval controls audits ask about, and hand over the documentation. Your audit team makes the call, but they should not find gaps in the trail.
Can you integrate with our core banking system?
Yes — usually via API, message queue or scheduled file exchange, depending on what the vendor supports. Settled during discovery.
Vocabulary
Terms worth knowing before the first call.
The words that come up most in Financial Services conversations, in plain English.
Bring us the workflow your team dreads.
Document-heavy, rules-heavy, audit-heavy. Those are worth automating properly.
- A reply within one working day, from an engineer rather than an account manager.
- An honest read on whether this is worth doing now, or in a year.
- No marketing list. Your details reach the solutions team and stop there.
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