Phase 1: Scoping (1 to 2 weeks). We sit with your subject-matter experts, examine the raw data and agree on a success metric. If the data is too sparse or the problem is better solved without AI, we say so. Honesty saves everyone money.
Phase 2: Build and validate (4 to 8 weeks). Our engineers prepare the dataset, experiment with model architectures and run cross-validation tests. You receive weekly progress reports with accuracy figures, not vague status updates.
Phase 3: Deploy and monitor (ongoing). The model goes into your existing infrastructure. We set up drift detection, retraining triggers and alerting dashboards. After handover, your internal team can maintain the system. We stay available for quarterly reviews.
This phased approach means you never commit a large budget before seeing evidence that the model works on your data. The discovery call is free and carries no obligation.