AI for manufacturing that runs on your data

No. We build on top of what you already have. That means integrating with SAP, Oracle, Microsoft Dynamics, or whatever ERP you run and connecting to your existing sensors and SCADA systems rather than replacing them. The only time we'd recommend replacing something is if it's genuinely blocking progress, and even then we prove it with data before suggesting it.

It depends on the equipment and the failure modes you're trying to predict, but most predictive maintenance systems start with vibration, temperature, and runtime data, the kind most modern production equipment already generates. We assess your current sensor coverage during discovery and tell you honestly what's sufficient and where gaps exist. You don't need a fully instrumented plant to start; you need enough data on your highest-risk assets.

For predictive maintenance, 12-24 months of equipment data with documented failure events is a solid starting point. For production planning and quality AI, 6-12 months of production records, demand history, and quality logs typically gives enough signal to train meaningful models. The discovery process tells us what you have and whether it's sufficient and we'll tell you upfront if the data situation makes a particular approach unworkable for now.

We treat operational technology environments differently from standard IT environments. AI systems that touch the floor run in read-only mode on the sensor and SCADA layer. They observe and alert, they don't control equipment directly unless that's a specific, explicitly scoped requirement with full safety review. Edge deployment options are available for environments with strict network segmentation requirements. Security and compliance go into the architecture from week one, not as an afterthought.

Four to five months to a production-ready system. Most manufacturing engagements run $65,000 to $150,000 depending on scope, sensor integration complexity, and whether the project covers one use case or several. We quote a fixed price after the discovery call: you know the total before we write a line of code, and it doesn't change based on how long things take us on our end. We deliver ROI projections based on your actual downtime costs, scrap rates, or planning cycle time before you sign anything.

You don't need a machine learning team. The systems we deliver are built to be operated by your existing engineering or IT team, with dashboards and alerting interfaces that don't require any data science knowledge to use day-to-day. At handoff you get the full codebase, documentation, and training. Model retraining schedules are built into the system so it stays accurate over time without manual intervention. If you want ongoing support as you expand to new use cases, retainer options are available but you're never locked in.