KYC and onboarding
Turn application packs, KYC files, statements, and payslips into verified structured data.
Move applications and servicing work forward with source-linked evidence, policy context, and clear human decisions.
System liveProduction path
What entersApplication and supporting evidence
AI workExtract, check, and assemble
Control planeEvalsAccessLogs
What leavesA review-ready caseOwned by your team
What we build
Application, credit, servicing, and collections systems.
Production AI for lenders and fintech teams across onboarding, document processing, credit operations, collections, support, and analytics.
Credit operations
Assemble policy references, applicant context, and exceptions for human review.
Statement processing
Extract and normalize transaction and income evidence with source links.
Collections support
Prioritize accounts, summarize context, and draft controlled next actions.
Borrower support
Answer account and process questions from approved systems and knowledge.
Lending analytics
Query approval, disbursal, delinquency, and servicing data in plain English.
Case studies
Production systems, with measurable outcomes.
Source-grounded document intelligence, governed data access, and real-time advisor guidance.
Engineering scope
Model capability is only one part of the system.
01
Ingest
Convert lending evidence into reviewable data.
Multimodal extraction handles documents and statements while preserving source locations.
02
Apply policy
Put approved rules beside the case.
Retrieval and workflow logic assemble relevant policy without pretending the model owns the decision.
03
Route
Send exceptions to the right owner.
Confidence, missing evidence, and policy conditions determine the handoff.
04
Learn
Turn reviewed cases into better tests.
Corrections and edge cases become evaluation examples for future releases.
Delivery model
Make the production decision early.
Define the workflow
Map the users, systems, data, controls, and measurable operating result.
OutputA focused production plan
Validate with real examples
Test representative inputs against quality, latency, cost, privacy, and review requirements.
OutputMeasured scope and baseline
Engineer the system
Build integrations, evaluations, interfaces, permissions, monitoring, and recovery paths.
OutputProduction-ready software
Launch and hand over
Launch with real users, improve from production feedback, and hand over code, runbooks, and baselines.
OutputAn operated system your team owns
Production controls
Built to evolve as models change.
Models can change without changing the operating contract. Permissions, evaluations, observability, and ownership stay explicit.
01
Credit decisions remain governed by approved policy and accountable owners
02
Applicant and borrower data follows explicit access and retention constraints
03
Source evidence stays attached to extracted fields and generated summaries
04
Incomplete cases route to the right person with the available context
05
Evaluation sets cover normal cases, edge cases, and policy-sensitive scenarios
06
The delivered system integrates with and remains owned by your engineering team
Buyer questions
Before you start.
Will the AI approve or reject loans?+
Not by default. We design systems to extract evidence, apply approved workflow logic, assemble policy context, and support accountable decision-makers. Any automated authority must be explicitly scoped, tested, and governed.
Can you process bank statements and KYC documents?+
Yes. We build multimodal extraction and validation pipelines that preserve source evidence, apply checks, and route exceptions for review.
How do you choose the right model and architecture?+
We test the workflow against your quality, latency, cost, privacy, and operational constraints. Model choice follows the evidence; the architecture stays replaceable where practical.
Can you work inside our existing cloud and engineering stack?+
Yes. We design around your APIs, identity model, data boundary, observability, release process, and ownership requirements rather than forcing a separate platform.
Who owns the finished system?+
Your team does. We deliver the code, evaluation baselines, monitoring, runbooks, and transfer needed for your engineers to operate and extend it.
What is a sensible first engagement?+
One valuable workflow with real samples, an accountable owner, and measurable success criteria. We prove the path before expanding the scope.
Start with one system
Bring the workflow, constraints, and real examples.
We will help determine the smallest production slice worth proving, what must be measured, and where human control belongs.