Production AI for manufacturers, supply-chain operators, and industrial OT teams. Predictive maintenance that survives the drift, vision defect detection that hits line speed, OT/IT bridges that respect operations, and an eval cadence that does not break a 24/7 shift.
We have shipped against this shape with discrete manufacturers, process-industry operators, supply-chain platforms, and industrial robotics teams. The constraints repeat.
The fix is not a fancier model. The fix is operations-discipline: edge inference, drift detection, OT-respecting integration, and an architecture that survives the next five-year capex cycle.
Sensor calibration changes, supplier lots change, line conditions change. A model that was 92% accurate at install drops to 70% in six months and nobody notices until a critical asset fails.
SCADA, PLCs, MES, historians, ISA-95 boundaries. Most AI vendors treat the factory like a SaaS app. OT engineers reject that on day one and the project never reaches the line.
Industrial systems do not get rebuilt every two years. Edge hardware, model registry, OT bridge — those decisions get inherited by the next plant manager and the one after that. Wrong call costs a capex round, not a refactor.
Eight-week engagements, OT-respecting integration, edge or cloud deployment shape, and an eval cadence that does not break a shift.
Time-series + LLM-on-event scoring that flags the asset before it fails. Drift-detected, retrained on a schedule, alerts the right OT engineer.
Real-time defect classification on the line. Edge inference where bandwidth or latency requires, cloud where flexibility wins.
The middleware that lets your data science team see PLCs without giving them PLC access. Read-only by default, OT-engineer approved.
Models on the line, on the gateway, or in the cloud. One registry, vendor-neutral, with rollback that does not require an OT engineer to drive.
Per-line latency, drift alerts, energy-per-inference metering. Plant manager and CFO see the same numbers as the on-call.
One-week audit of your manufacturing-AI stack. Edge hardware, model registry, OT bridge, eval cadence, drift response — all named in writing.
Most engagements bundle two: an edge build (01, 02) paired with the discipline that keeps it shipping (03, 05). Bring the shape closest to your blocker.
Scope your engagement →Want to see the K-Framework discipline behind every item? Read the K-Framework.
Tools your OT engineers have already approved. Self-host where edge requires, cloud where flexibility wins.
The store, the index, the search
Embeddings, providers, fallbacks
The eval bar, the cost meter, the drift alarm
Type-safe everything
iOS + Android, native or cross
Whatever your infra already runs
Sensors per deployment in production
Edge inference round-trip target
OT integration default
Production-grade uptime expectation
Eight weeks, fixed scope, eval suite + drift response at handoff. Direct LLM and ML engineering on top of the K-Framework. Two Q3 slots remain.
