Bralak AI

FinTech

Automate the Workflows Around Regulated Processes.

Document intelligence, customer communication and operational automation for financial services — with approval gates and audit trails throughout.

Book an AI Strategy Call

Onboarding a customer, and the exceptions it generates

  1. 01Intake
  2. 02Extract and validate
  3. 03Assemble the case
  4. 04Decide or route
  5. 05Approve
  6. 06Record

Challenges

What slows FinTech teams down

  • Document-heavy onboarding
  • Support volume concentrated at predictable moments
  • Manual reconciliation
  • Audit and evidencing requirements
  • Legacy core systems

The workflow

Onboarding a customer, and the exceptions it generates

The document-heavy path where most of the manual effort and almost all of the evidencing burden actually sit.

StageWhat happensSystemsWho decides
01IntakeApplications and supporting documents arrive by portal, email or API, in whatever formats the customer had to hand.Onboarding portal, shared inbox, document storeSystem
02Extract and validateFields are read from identity documents, statements and corporate records, then cross-checked against each other and against the application.Document store, core banking APISystem; low-confidence extractions flagged
03Assemble the caseScreening and verification results are gathered into one record with the evidence attached, ordered the way a reviewer reads it.Screening provider, CRM, case managementSystem
04Decide or routeClean cases proceed on your rules. A discrepancy, an adverse result or a missing document becomes a routed exception rather than a judgement call.Case managementSystem inside policy; analyst otherwise
05ApproveAnything that opens an account, moves money or alters a financial record waits for a named person.Core banking, ledgerAuthorised approver
06RecordInputs, documents retrieved, the reasoning step, the action, the approver and the timestamp — written at the time, not reconstructed.Audit store, data warehouseSystem

Limits

Where errors cost, and where we would not automate

Both halves of this section are reasons to build less than the maximum. They are here because a supplier who has not thought about them has not built one of these before.

Where an error costs

  • An extraction error in an identity document is not a typo. It propagates into screening, into the account record, and into everything later reconciled against it.
  • An automated financial action that should have been reviewed cannot be undone by improving the model afterwards. Reversibility is why the gate exists — not reviewer capacity.
  • A decision you cannot explain to an auditor is a finding whether or not it was correct.

Where we would not put AI here

  • Final credit, eligibility and risk decisions about an individual. The system assembles and evidences the case; a person decides it.
  • Movement of money without an approval step, at any transaction size. Widening that threshold is a conversation for after six months of measured error rates, not for design time.
  • Regulatory interpretation. We build the workflow your compliance function specifies. We do not tell you what a rule requires, and we make no compliance claim on this site.

Agents

The agents that do this work

Expand any card for the problem it solves, what it does, what it connects to and where a person stays in the loop.

  • Document Agent

    Extracts, validates and processes business documents.

    What it does
    Problem
    Documents arrive in every format and someone retypes them into a system.
    Actions
    • Classifies document type
    • Extracts structured fields
    • Validates against systems of record
    • Flags discrepancies with both sources
    • Files and posts
    • Requests what is missing
    Integrations
    Email · SFTP · S3 · SharePoint · Accounting systems · ERP · Document stores
    Outcome
    Intake stops being manual, and exceptions surface with a diagnosis attached.
    How we build this

  • Customer Support Agent

    Handles repetitive support conversations and escalates the rest with context.

    What it does
    Problem
    A large share of tickets are the same handful of questions, and they sit in the same queue as the cases that genuinely need a specialist.
    Actions
    • Answers from documentation and policy
    • Retrieves account and order history
    • Performs account actions within policy
    • Escalates with a written summary
    • Tags and routes
    • Identifies documentation gaps
    Integrations
    Zendesk · Intercom · Freshdesk · Salesforce Service Cloud · Slack · Email
    Outcome
    Routine cases resolve without a queue; specialists receive pre-researched tickets.
    How we build this

  • Finance Agent

    Handles structured financial workflows and document intelligence.

    What it does
    Problem
    Finance operations run on repetitive matching, chasing and reconciliation work.
    Actions
    • Processes invoices
    • Matches against purchase orders
    • Validates tolerances
    • Flags discrepancies
    • Chases approvals
    • Prepares reconciliation summaries
    Integrations
    Xero · QuickBooks · NetSuite · SAP · Banking APIs · Email
    Outcome
    Finance reviews exceptions rather than performing matching by hand.

    Financial actions default to human approval.

    How we build this

  • Knowledge Agent

    Answers questions from organisational documentation, with citations.

    What it does
    Problem
    The answer exists — in a document, a thread or one person’s memory — and finding it takes longer than the task it unblocks.
    Actions
    • Answers from internal documentation
    • Applies the asker’s permissions
    • Cites sources
    • Says so when the answer is not documented
    • Logs unanswered questions
    Integrations
    SharePoint · Google Drive · Confluence · Notion · Internal wikis · Slack · Teams
    Outcome
    Institutional knowledge becomes reachable, and the gaps become visible.
    How we build this

Integrations

The systems this connects to

  • Core banking APIs
  • CRM
  • Zendesk
  • Document stores
  • Data warehouses

Selected work

Results, once they are checkable

Selected work is being prepared for publication.

We publish results only once they can be independently verified.

FAQ

FinTech questions we are asked

Where is our data processed?

Data flow is mapped and agreed in writing before implementation begins, including which provider sees what and which regions it touches. Where a hosted provider is unacceptable for a given data class, that class is handled by a model deployed in your environment.

Does anything financial happen without a person approving it?

No. Actions are classified by reversibility, and anything that moves money or alters a financial record defaults to an approval gate. Automation is widened later from observed error rates, and only where you decide the evidence supports it.

What can we hand an auditor?

A per-run record: the input, the documents or policy retrieved, the model’s reasoning step, the action taken, the approver and the timestamp. Behaviour is inspected after the fact from that record rather than inferred from the outcome.

Can this integrate with a legacy core?

Usually, and rarely through a modern API. Integration is designed around what the core actually exposes — batch files, message queues, a middleware layer — and the queue, retry and reconciliation behaviour is designed with it rather than added after the first failed run.

How do we explain a decision the model made?

Decisions that carry consequence are structured so the determining inputs are recorded alongside the output, and retrieval-grounded answers carry citations to the source passage. Where a decision cannot be explained to your satisfaction, it should not be automated, and we will say so.

Solutions

How we build it

Your Next Intelligent System Starts Here.

Tell us what you’re trying to improve, automate or build. We’ll help you identify the right AI strategy and engineering path.

Book an AI Strategy CallStart a Project