Rebate Analysis AI Agent

The work it takes off your plate

Manual rebate analysis involves tedious invoice verification, contract clause cross-referencing, and rebate calculations, often leading to financial discrepancies, delays, and compliance risks. Finance teams struggle with tracking rebates accurately, resulting in missed opportunities, inconsistencies, and an increased administrative workload. The Rebate Analysis AI Agent overcomes these challenges by automating rebate validation, ensuring accurate calculations, and optimizing financial workflows. By reducing manual effort and accelerating processing times, it enhances financial transparency, improves rebate recovery, maximizes utilization, and boosts overall financial efficiency.

How it works

The Rebate Calculation Agent automates and simplifies the rebate processing workflow, ensuring accuracy and efficiency. The agent is triggered when a new Proof of Delivery (POD) email arrives in a designated inbox, initiating a series of automated steps. Using a Large Language Model (LLM), it analyzes incoming data, cross-references contract details, and calculates rebates in real time. Below is a step-by-step breakdown of the process:

1. Step 1: Proof of Delivery (POD) Detection and Data Extraction

   • Monitor designated mailboxes and repositories for new PODs, invoices, and delivery attachments.
   • Extract supplier, invoice, purchase order, delivery date, SKU, quantity, unit price, and receiver details from PDFs, spreadsheets, and email text.
   • Classify documents by type and associate related PODs, invoices, and purchase orders using identifiers and supplier metadata.
   • Record source locations, extraction confidence, and document timestamps for auditability.

   Result: A normalized transaction package is created for each delivery with traceable source documents and extracted commercial data.

2. Step 2: Invoice Matching Using the Knowledge Base (KB)

   • Search the knowledge base for matching invoices, purchase orders, delivery records, supplier profiles, and prior rebate decisions.
   • Compare invoice numbers, purchase order references, supplier identities, delivery dates, quantities, and totals across source documents.
   • Identify duplicate invoices, missing records, conflicting values, and invoices that require manual review.
   • Assign a match confidence score and retain the evidence used to support the matching decision.

   Result: Each invoice is linked to the most relevant transaction records or routed to an exception queue when matching evidence is insufficient.

3. Step 3: SKU Retrieval & Contract Metadata Verification

   • Retrieve SKU descriptions, product groups, pricing attributes, and eligibility flags from the item master.
   • Locate the supplier contract, rebate schedule, effective dates, expiration dates, and applicable volume tiers.
   • Verify that delivered SKUs, supplier terms, and transaction dates fall within the eligible contract scope.
   • Detect missing metadata, obsolete SKUs, expired agreements, and conflicting contract versions.

   Result: Every qualifying line item is mapped to an active contract rule with verified product and eligibility metadata.

4. Step 4: Rebate Validation & Calculation

   • Calculate line-level rebates using contracted rates, eligible quantities, prices, and applicable volume thresholds.
   • Apply tiered, quarterly, promotional, and bonus rebate rules in the order specified by the contract.
   • Compare calculated amounts with supplier credits, previously claimed rebates, and finance ledger entries.
   • Flag discrepancies such as overclaims, underclaims, unsupported bonuses, quantity variances, and missing approval evidence.
   • Generate an approval recommendation with calculation details, exceptions, and supporting source references.

   Result: Finance receives an auditable rebate amount, discrepancy assessment, and clear approval or escalation recommendation.

5. Step 5: Continuous Learning and Improvement

   • Capture reviewer corrections to extracted fields, document matches, contract interpretations, and exception classifications.
   • Measure approval rates, false positives, calculation variances, processing time, and recovered rebate value.
   • Update extraction mappings, supplier-specific rules, and retrieval priorities using approved feedback.
   • Present recurring discrepancies and contract ambiguities to finance owners for policy or master-data remediation.

   Result: The agent becomes more accurate over time while providing measurable controls for rebate recovery, compliance, and operational improvement.

Why use it

• Automated Rebate Calculation. Fully automates the rebate calculation process, minimizing manual effort and errors while ensuring precise and timely rebate application.
• Faster Processing. Accelerates the rebate cycle by automating data extraction and calculations, reducing delays and improving cash flow.
• Operational Efficiency. Simplifies workflows by automating repetitive tasks, freeing employees to focus on higher-value activities and strategic initiatives.
• LLM-Powered Precision. Uses large language models (LLMs) for accurate data extraction, matching, and rebate application, minimizing errors and maximizing rebate claims.
• Continuous Improvement. Integrates a human feedback loop, allowing the AI agent to refine its accuracy over time and adapt to evolving contract terms and rebate structures.
• Smooth Contract Integration. Connects with contract management systems for real-time access to contract terms, ensuring accurate rebate validation.