Services · Step 03
Custom Software & AI
For manufacturers who have the data foundation in place and are ready to extract measurable ROI from custom automation, ML models, and purpose-built operational software.
What we build
- ✓ Predictive quality and defect detection models trained on your production data
- ✓ Computer vision systems for automated visual inspection on production lines
- ✓ Demand forecasting and inventory optimization models
- ✓ Workforce knowledge capture tools — systematic documentation of institutional knowledge before it walks out the door
- ✓ Custom operational applications that replace spreadsheet-based workflows with governed, integrated systems
- ✓ ML-powered anomaly detection for equipment, process, or quality data
Engagement details
- Timeline
- Varies by scope (typically 3–9 months)
- Primary deliverable
- Production-ready software or ML system
- Best for
- Manufacturers with clean, connected data ready for automation
- Prerequisite
- Data foundation in place (Data Transformation or equivalent)
Ready for this stage?
Not every manufacturer is ready for Custom Software & AI — and that's fine. Let's have an honest conversation about where you are and what the right next step is.
Schedule a ConversationWhat this looks like in practice
Tier-2 Auto Parts Manufacturer
Phase 1
Inspection moved off the floor
Manual inspection redesigned and tooled to move into a centralized office, where a single inspector can manage multiple lines simultaneously.
Phase 2
Up to 95% reduction in manual inspection
Structured data collection built across the line to train an ML model that flags high-risk components before manual review, with a projected reduction in manual inspection volume of up to 95%.
Phase 3 · In Development
Self-directed model training for new parts
Inspectors begin feeding data using the software — the system auto-configures and trains a new detection model for each new part, no data scientist required.
How we scope these engagements.
We scope Custom Software & AI engagements carefully. That means we start with the ROI math: what does this cost to build, what does it cost to maintain, and what does it return in year one, year two, and year three? If the numbers don't justify the investment, we say so.
We avoid two failure modes that are common in this space: building technically impressive systems that don't connect to the operation, and building for a use case that isn't ready for automation because the data underneath it is still unreliable.
The result is a smaller number of engagements that go all the way to production — and a higher fraction that deliver measurable ROI inside twelve months.