AI can read the dispute, pull the order, invoice, proof-of-delivery and pricing, and propose a valid-or-invalid disposition with the evidence attached — cutting the manual research that lets deductions age into write-offs.
Evidence-backed triage halves deduction write-offs $28M→$14M/yr (≈$14M/yr recovered) and clears aged deductions faster, freeing ≈$25M of tied-up cash.
Opens your own Claude with the prompt and reference data pre-loaded as text — no upload, no setup. Tailor it live to your own numbers.
Synthetic KokoAI figures — a demo fixture, not audited or a guarantee
Try it
Illustrative demoWorked example of "Dispute / Deduction Resolution" on synthetic data — edit the inputs to tailor it. Edit the inputs to tailor the output — it's baked synthetic data, so nothing leaves your browser until you run it live.
Synthetic demo company (a ~$5B high-growth US AI SaaS) — swap in any name to tailor the output.
What this run should concentrate on.
Workflow
- 1.Intake the dispute and classify the deduction type.
- 2.Retrieve the order, invoice, contract terms, pricing and proof-of-delivery.
- 3.Validate the claim against the evidence and propose accept/reject.
- 4.Draft the resolution and the customer response.
- 5.Route to the analyst for approval; escalate high-value or ambiguous cases.
Prompt / agent recipe
For the attached deduction/dispute, gather the relevant order, invoice, contract and delivery evidence, determine whether the claim is valid against the terms, and recommend accept or reject with the supporting documents cited. Escalate anything above [$ threshold] or where evidence conflicts.
- Analyst approves disposition before credit/write-off is posted
- Value thresholds force human review
- Evidence pack retained for audit
- Segregation of duties between dispute resolver and credit poster