Loan document processing without the re-key chase.
Classify borrower documents, extract verified fields, reconcile inconsistencies, and present a condition-ready file for underwriter judgment.
Nothing is finalized until a human approves it.
Where the time and money actually go.
Income, asset, title, insurance, and entity documents arrive in many formats; processors re-key data and repeatedly chase missing or stale evidence.
Processors, underwriters, closers, quality-control analysts
Trigger to act: Turn time is hurting pull-through, conditions per file are high, seasonal volume creates backlog, or quality-control findings show field inconsistency.
The result you can model before you sign.
Illustrative only: 1,000 files/month × 35 minutes removed × $39 loaded hourly cost ÷ 60 = $22,750 monthly capacity. Approval and pull-through impact require controlled measurement.
The outcome, plainly: Classify borrower documents, extract verified fields, reconcile inconsistencies, and present a condition-ready file for underwriter judgment.
Inputs in. A cited, review-ready result out. Your expert decides.
A Document AI + workflow agent. Every material fact is grounded in an allowed source and returned with its identifier, with no invented data.
Claude Sonnet 4.6 or GPT-5.6 Terra for evidence-aware extraction and reasoning; Gemini 2.5 Flash-Lite or Claude Haiku 4.5 for high-volume classification and normalization. Amazon Textract or Azure AI Document Intelligence; PostgreSQL + pgvector; Pinecone only when scale/latency requires it.
- Pay stubs
- Tax returns
- Bank statements
- IDs
- Appraisals
- Title/insurance
AI-Native, not autonomous. Judgment stays with your people.
The machine does the work; the human’s role narrows to the one thing that matters, judgment. That constraint is what makes it safe to deploy.
Licensed/authorized underwriters make credit and eligibility decisions; processors review extracted values; adverse action is never generated solely by the model.
A scorecard, not a demo. We baseline what breaks in production.
Every deployment ships with an evaluation suite. These are the numbers we baseline before launch and monitor after.
The category is crowded. Most of it isn’t built for your workflow.
Fixed-scope, tuned to your systems and rules, grounded in your data, with the human gate and audit trail built in from day one. A price you own, not a subscription you rent.
It plugs into the stack you already run.
No rip-and-replace. Access is scoped to the minimum data necessary, isolated per tenant, and fully logged.
Transparent by design. The build price buys the workflow and the proof.
A fixed implementation fee plus a monthly bill that scales with volume and governance. No hidden seats.
- ✓ One process / scope
- ✓ Live workflow on your data
- ✓ Baseline evaluation suite
- ✓ Measured vs. current process
- ✓ Full scope & integration
- ✓ Human-review UI & audit trail
- ✓ Write-back to your systems
- ✓ Production evals & monitoring
- ✓ Multi-facility rollout
- ✓ Advanced security & compliance
- ✓ Custom control & escalation
- ✓ Dedicated evaluation program
10,000 to 40,000 document pages per month; OCR/form extraction, model verification, storage and workflow compute.
Planning assumptions, not vendor quotations. Your loan origination system and other platform licenses are separate and owned by you. Figures confirmed during scoping.
“His vast knowledge of technologies and a natural problem-solving mindset consistently lead us through complex challenges with clarity and confidence.”
Questions serious buyers ask.
Does the AI act on its own?
No. Licensed/authorized underwriters make credit and eligibility decisions; processors review extracted values; adverse action is never generated solely by the model. The system drafts and recommends; a human approves every consequential action. Explicitly excluded: no autonomous credit or underwriting decision; no FCRA/ECOA adverse action; no fabricated income; no use of protected characteristics or proxies.
How do you stop it inventing facts?
Every material claim is grounded in an allowed source record and returned with its source identifier. The system separates observed facts, model inference, and missing information, and routes to a human whenever confidence is low, evidence conflicts, or an adverse outcome is possible.
What does it cost to run each month?
A usage bill of roughly $850 to $5,500 per month (10,000 to 40,000 document pages per month; OCR/form extraction, model verification, storage and workflow compute), plus a $2,800 per month managed retainer for evaluation, monitoring and maintenance. Your existing platform licenses are separate and already yours. Exact figures are confirmed during scoping.
Do we need a ChatGPT or Claude subscription?
No consumer ChatGPT or Claude subscription is required for the production workflow. You need an approved API/cloud billing account. Workspace seats are optional for internal prototyping and administrator access.
How is this different from Ocrolus?
Tools like Ocrolus, Blend, and nCino are broad platforms you adapt to. This is a fixed-scope implementation tuned to your systems and rules, grounded in your data, with the human gate and audit trail built in, and a transparent price instead of a seat subscription.
How long until it is live, and how do we prove it works?
We baseline documents per file first, then measure against that baseline. You see the scorecard before expanding scope: the evaluation suite ships with the system, not as an afterthought.
Bring your real numbers. Leave with a fixed-scope plan.
A 30-minute engineering-led working session, no slideware. You leave with a sized opportunity estimate, a fixed-scope pilot plan, and the integration & human-review path mapped.
VP of Growth at ViitorCloud · senior delivery owner confirmed before paid work