Indirect Spend Classification Agent
Automatically classifies every purchase transaction as direct or indirect spend and assigns standardized category codes using a trained machine learning model.
Category managers currently rely on analysts to manually tag thousands of GL and PO line items each month with UNSPSC or internal category codes, a slow and error-prone process
Different business units and ERPs code the same purchase inconsistently, so category managers cannot get a true, comparable view of total spend in any category
Tail spend transactions with vague vendor descriptions are especially hard to classify, and most get dumped into generic buckets that hide real spend concentration
This agent applies an NLP and machine learning classifier trained on historical coding patterns, vendor descriptions, and product text to assign consistent direct/indirect flags and category codes automatically, learning from every human correction to keep improving accuracy
The agent ingests raw AP invoice and PO line-item text along with vendor names and descriptions, then applies an NLP-based classification model trained against the organization's UNSPSC or custom taxonomy and direct/indirect rules. It cross-references each vendor's primary NAICS code and historical purchasing pattern to increase confidence, and routes any classification below a configurable confidence threshold to a human reviewer. Reviewer corrections are captured and fed back into the model on a scheduled retraining cycle, continuously improving classification accuracy over time.
Ingest and Preprocess Transactions
- Pull AP and PO line items from ERP
- Clean and normalize free-text descriptions
- Attach vendor NAICS and historical category data
- Deduplicate near-identical line items
Classify with ML Taxonomy Model
- Run NLP classifier against UNSPSC/custom taxonomy
- Assign direct vs indirect spend flag
- Score classification confidence
- Compare against vendor historical patterns
Route Low-Confidence Items for Review
- Flag classifications below confidence threshold
- Present suggested category with supporting evidence
- Capture reviewer decisions
- Log override reasons
Publish Classified Spend and Retrain
- Push classified spend to reporting layer
- Aggregate direct vs indirect totals by business unit
- Feed reviewer corrections into training set
- Schedule model retraining