Our Services
Our AI engineering capabilities
Every stage of the ML lifecycle — from problem framing to production model monitoring.
ML Problem Framing
Most ML projects fail not because of bad models but because of bad problem framing. We start by defining the business problem, the ML task that solves it, and the success metrics before touching any data.
Business Problem Translation
Map the business question to a specific ML task — classification, regression, ranking, generation.
Data Feasibility Assessment
Evaluate whether sufficient training data exists for the problem as defined.
Baseline Definition
Define a simple baseline (rules engine, heuristic) to measure ML model value against.
Success Metrics
Define offline (precision, recall, RMSE) and online (conversion, revenue impact) success metrics.