Madmatcher provides an AI-powered platform that ingests structured data about entities such as job applicants, products, or partners and generates relevance scores using customizable rule engines and machine‑learning models. Users can define matching criteria via a visual interface or API, receive ranked lists with confidence metrics, and integrate the results into HR, procurement, or product workflows through connectors and export options.
Funding
Funding not disclosed
Founders
Product
Problem
Businesses lack an efficient way to automatically match and rank candidates or items based on complex criteria, leading to time‑consuming manual processes and suboptimal selection outcomes.
Solution
Madmatcher offers an AI‑driven matching platform that ingests structured data about entities—such as job applicants, products, or partners—and computes relevance scores using customizable weighting and machine‑learning models. Users define matching rules through a visual interface or API, and the system returns ranked lists with confidence metrics. The platform integrates with existing data sources via connectors and provides export options for downstream workflows, enabling organizations to streamline selection, allocation, and recommendation tasks.
Target Audience
Target customers include HR teams, procurement departments, and product managers who need to automate candidate screening, supplier selection, or product recommendation processes.
Features
- Configurable rule engine allowing users to set attribute weights, thresholds, and conditional logic
- Pre‑trained machine‑learning models that can be fine‑tuned on proprietary datasets for domain‑specific matching
- RESTful API and webhooks for real‑time integration with HRIS, CRM, or inventory systems
- Dashboard with interactive visualizations of match scores, heatmaps, and audit trails
- Bulk data import/export supporting CSV, JSON, and database connectors