AI Pricing Review: Faster, Explainable and Audit-Safe
An evidence-grounded RAG and workflow automation platform designed for Amazon Retail category and vendor managers, reviewing 1P vendor price changes against contract terms—reducing repetitive manual review while preserving rules, accountability and human oversight.
target clause-match accuracy acceptance gate before rollout
~80% less
time per routine review 5–10 min → 1–2 min · ~5× faster
70–90%
estimated bulk review avoided safe items filtered first
2.5K–6.7K
estimated hours saved yearly from stated modeling inputs
~$172K–$468K
estimated annual productivity value capacity returned to the business
Audit-safe
guaranteed by design clauses + timestamps
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Bottom line: The system combines deterministic validation with contract-aware AI. Routine requests receive a short, cited explanation; bulk files are automatically screened so managers focus only on risky items; and every decision retains a timestamped evidence trail. The result is materially faster review without transferring final accountability away from the business.
Design Properties — What the System Guarantees by Construction
blind approvals on uncertainty conflicts escalate to humans
Rules first
deterministic checks outrank the model the LLM never has final say
Replayable
every decision reconstructable clauses + state + timestamp
Before vs. After — Pricing Review Workflow
Step
Before — manual workflow
After — agent-assisted workflow
Expected improvement
Contract and policy lookup
Search across PDFs, policies and prior correspondence
Hybrid retrieval returns exact clauses and citations
minutes → seconds
Routine price-change review
5–10 min to verify clause, region, date and threshold
1–2 min to review evidence and approve
75%+ faster
Bulk vendor CSV upload
Large files create a manual-review bottleneck
Rules screen safe rows; RAG evaluates exceptions
70–90% less review
Exception handling
Managers interpret ambiguous language individually
Low-confidence or conflicting cases escalate with evidence
focused human effort
Audit or escalation response
Reconstruct the decision from email and memory
Open the record and see clause, reasoning and timestamp
no re-investigation
Overall operating model
Human searches, interprets and documents every case
AI prepares evidence; rules and people retain control
faster + safer
Where a bulk upload ends up
Share of submitted rows at each screening stage. Deterministic rules clear the routine majority before the model is asked anything. 90–95% fewer rows reach a person.
The gap between the first and last bar is the whole point of the design: rules absorb the volume, retrieval is spent only on genuine exceptions, and a person sees the small set where judgement actually matters.
Routine price-change review time
Estimated minutes to verify clause, region, date and threshold for one routine request. Bar marks the midpoint; whisker shows the full range. ~80% less time, roughly 5× faster.
The remaining 1–2 minutes is review, not search: the clause, the region and date conditions and the threshold check are already on screen, so the manager is confirming a decision rather than assembling one.
Basis of estimate. Values are derived from the modeling inputs above using a standard labor-capacity method: reviews × time saved × workdays × loaded labor rate. The estimate represents review capacity returned to the business rather than direct cash reduction, and should be recalibrated against validated headcount, request volume, Finance-approved labor rates and post-launch adoption data before use in budget planning.
Key Technical Achievements
🔍
Hybrid contract retrievalCombined exact keyword matching for clause IDs and limits with semantic vector search for conceptually similar language.
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Multi-agent RAG designSeparated retrieval, interpretation and decision responsibilities rather than relying on one unconstrained prompt.
⚙️
Deterministic workflow orchestrationLangGraph and Step Functions provide branching, retries, checkpoints, workflow visibility and escalation paths.
🧾
Evidence-first decision recordsEach AI-assisted outcome stores its explanation, supporting clause links, workflow state and timestamp.
🛡️
Compliance guardrailsRules override AI; missing evidence, weak retrieval or inconsistent reasoning causes escalation instead of guessing.
3 High-Value User Journeys
Use case 01
Manager-Assisted Routine Approval
A vendor requests a small 1–2% increase. The app retrieves the exact clause, verifies date and region conditions, and presents a short explanation for manager approval.
Value: 5–10 minutes reduced to roughly 1–2 minutes while the manager remains accountable.
Use case 02
Bulk Upload Auto-Filter
A large vendor uploads thousands of SKU changes. Rules approve clear cases; RAG checks contract exceptions; only the small risky subset reaches a reviewer.
Value: people review approximately 5–10% instead of 100% of submitted rows.
Use case 03
Audit and Escalation Evidence
Weeks later, a leader or auditor asks why a change was approved. The stored record immediately shows the decision, exact clauses and timestamped reasoning.
Value: faster, defensible answers without reconstructing decisions from memory or email.
Agentic Workflow & AWS Architecture
Service architecture by layer
Request flow from intake through orchestration into the 4 agent stages, with the data and model services each stage depends on.
Validate sits ahead of Retrieve and Interpret on purpose: rules and thresholds are checked before any model call, so a request that fails a deterministic condition never reaches the LLM and never consumes retrieval budget.
Why this is agentic AI: The system does more than retrieve text. Specialized components choose and execute actions across validation, retrieval, interpretation, confidence assessment, branching and escalation. The path changes based on the evidence and risk of each request.