Dispute / Deduction Resolution

What changed

A language model reads each dispute and classifies the deduction. The workflow pulls the matching order, invoice, contract terms, pricing and proof of delivery by reference, and the model proposes accept or reject with that evidence attached. That cuts the manual research that lets deductions age into write-offs.

How it's built

LLM workflow (rung 4 of 6). A coded control path: chain, router, parallel fan-out, evaluator-optimizer.

Not an agent: the sequence of steps is fixed in advance, so the whole path can be tested end to end.

A language model handles steps 1, 3 and 4. Rules: step 2 · Human: step 5.

Autonomy: T1 · Draft: Produces a complete output for a human to review and release. Human role: reviews every item and releases it.
How it is governed: The path is testable end to end. Each step is independently evaluable.

What the certification exams call this

Anthropic (Claude Certified Architect – Foundations (CCAR-F))Workflow: models and tools on predefined code paths (chaining, routing, parallelization, evaluator-optimizer). source
OpenAI (OpenAI agent guidance (Agents SDK, Codex, OpenAI Academy))Orchestrating via code, which the Agents SDK calls more deterministic and predictable. source
Google Cloud (Generative AI Leader)Workflow agents (sequential, parallel, loop) that run steps in a fixed order. source
Microsoft (AI-901 (Azure AI Fundamentals) and AB-100 (Agentic AI Business Solutions Architect))Sequential and concurrent orchestration patterns, with steps in a set order. source

How Koko classifies AI · the full ladder is taught in AI & Agentic Delivery Foundations.

Modeled value on KokoAI

Synthetic KokoAI figures — a demo fixture, not audited or a guarantee

Deduction write-offs

28 → 14 $M/yr

lower is better

Recurring impact

$14M/yr

run-rate benefit

Cash freed

$25M

one-time working-capital release

Payback

4 months

≈ 5,000 hrs/yr reclaimed

Evidence-backed triage halves deduction write-offs $28M→$14M/yr (≈$14M/yr recovered) and clears aged deductions faster, freeing ≈$25M of tied-up cash.

Run this in Claude on KokoAI's data

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.

Try it

Illustrative demo

Synthetic data

Worked 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.

Company / entity

Synthetic demo company (a ~$5B high-growth US AI SaaS) — swap in any name to tailor the output.

Period

Focus / scope

What this run should concentrate on.

Run live in Claude

Workflow

1.Generative AIIntake the dispute and classify the deduction type.
2.RulesPull the order, invoice, contract terms, pricing and proof of delivery by reference.
3.Generative AIValidate the claim against that evidence and propose accept or reject.
4.Generative AIDraft the resolution and the customer response.
5.HumanRoute to the analyst for approval; escalate high-value or ambiguous cases.

Prompt 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.

Try in Ask KokoAI

Controls required

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