Growth focus
AI for Manufacturing & Supply Chain
Predictive maintenance, demand forecasting, and industrial data platforms.
Business outcomes AI can move
- Reduce unplanned downtime and lost production revenue
- Improve order fill rate and on-time delivery
- Cut inventory carrying cost without stockouts
- Accelerate distributor and OEM sales cycles
AI capabilities
Each capability maps to a business KPI, revenue, cost, risk, or cycle time, not model accuracy on a slide.
Revenue
Predictive maintenance
Forecast equipment failure before line stops, protect output revenue and avoid emergency repair premiums.
Distributor sales intelligence
Score accounts by reorder likelihood and upsell potential, field reps prioritize highest-revenue opportunities.
Cost
Demand & inventory optimization
Better forecasts reduce both stockouts and excess inventory, margin improvement at scale.
Risk
Quality anomaly detection
Catch defects earlier in production, less scrap, fewer recalls, protected brand revenue.
Production use cases
- Equipment failure prediction
- Demand & inventory optimization
- Quality anomaly detection
Frequently asked questions
How do you scope AI for our industry?
Week-one discovery locks business KPIs—revenue, cost, risk, or cycle time—not model vanity metrics. We map your data sources, compliance constraints, and existing stack, then propose a fixed-scope mandate with acceptance criteria or redirect you to a simpler path.
Do you have experience in our sector?
Proven depth sectors (financial services, federal, real estate, telecom, higher education) reflect production systems we've shipped. Growth sectors are selective mandates where agentic and ML patterns transfer with industry-specific tuning. Biotech, insurance, and regulated work often runs under NDA.
What do we get at the end of an engagement?
Deployable code in your repos, monitoring and runbooks, documentation your team can operate, and knowledge-transfer sessions. The Production Standard is contracted in writing—no handoff to a junior bench after the sale.
What's the fastest way to start?
Book a discovery call or start with a bounded diagnostic: $2,500 Postgres audit, insurance claims-fraud diagnostic from $7,500, or the free MLOps readiness assessment. Each path surfaces whether AI is the right tool before a larger commitment.
The Production Standard
The Production Standard
Six commitments contracted in writing on every engagement.
01
Deploy or redirect
If AI isn't the right tool, we say so in week one and redirect budget to what will ship. No POC theater.
02
Metrics before models
Business KPIs locked in discovery. Model selection follows the metric, not the hype cycle.
03
Governance from day one
Model cards, access controls, evaluation harnesses, and audit trails, not a compliance bolt-on at go-live.
04
Production artifacts
Deployed code, monitoring dashboards, CI/CD pipelines, and incident runbooks. Not recommendations for someone else to implement.
05
6-week production target
Scoped projects designed for production in 6–10 weeks. Boutiques move; global programs wait for steering committees.
06
Capability transfer
Your team can operate, extend, and maintain what we build. IP and documentation transfer is included, not upsold.
Every scoped project includes a production deliverable checklist signed off before close. If we can't commit to deployable output, we won't take the engagement.
Selective intake · 2026
Your next system shouldship to production.
Agentic development from architecture through deployment, with the Production Standard contracted in writing.