Overview
An ML-driven accounting solution that automates reconciliation of customers' and vendors' statements, eliminating manual matching work, catching anomalies and speeding up month-end close.
Auto reconciliation of customers' and vendors' statements using ML.

An ML-driven accounting solution that automates reconciliation of customers' and vendors' statements, eliminating manual matching work, catching anomalies and speeding up month-end close.
Phased delivery from discovery through scale. Each milestone tied to a measurable outcome.
Consolidated customer and vendor statements across ERPs.
Key outcome: Clean training corpus for matching models.
Trained ML models for statement matching and anomaly detection.
Key outcome: High-accuracy auto-reconciliation.
Built exception workflow and reconciliation dashboards.
Key outcome: Faster review and approval cycles.
Rolled out to finance teams with training and change management.
Key outcome: Dramatic drop in manual effort and faster month-end close.
More case studies

A robust ticketing system to manage inter-departments & vendors.
Read case study
Self-care development for Smile users in Nigeria.
Read case study
An AI-powered postpaid dunning solution designed for telecom operators to automate revenue recovery and reduce churn.
Read case studyTalk to our engineering team. We'll shape an approach tailored to your business and prove value fast.