Cash application automation that saves analyst hours.
Match incoming payments and remittance data to open invoices, post high-confidence allocations, and route only ambiguous deductions or short pays.
Nothing is finalized until a human approves it.
Where the time and money actually go.
Remittance arrives through email, portals, EDI, and bank narratives; analysts search across customers and invoices while unapplied cash obscures working capital.
Cash application analysts, AR, collections, customer service
Trigger to act: Unapplied cash is material, lockbox volume is rising, acquisitions created inconsistent references, or collections teams cannot trust customer balances.
The result you can model before you sign.
Illustrative only: 25,000 payment lines/month × 3 minutes removed × $34 loaded hourly cost ÷ 60 = $42,500 monthly capacity. Working-capital benefit depends on posting delay and should be measured separately.
The outcome, plainly: Match incoming payments and remittance data to open invoices, post high-confidence allocations, and route only ambiguous deductions or short pays.
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.
- Bank transaction
- Remittance advice
- Open invoices
- Customer master
- Historical payment patterns
- Deductions and credit memos
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.
AR analysts approve ambiguous allocations, write-offs, credit use, deductions, and intercompany or high-value transactions.
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 ERP 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. AR analysts approve ambiguous allocations, write-offs, credit use, deductions, and intercompany or high-value transactions. The system drafts and recommends; a human approves every consequential action. Explicitly excluded: no autonomous write-off, refund, or credit reallocation; no forced match when payer identity is uncertain; no override of accounting policy.
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 $800 to $5,000 per month (10,000 to 40,000 document pages per month; OCR/form extraction, model verification, storage and workflow compute), plus a $2,600 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 HighRadius?
Tools like HighRadius, BlackLine, and Billtrust 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 the auto-match rate 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