Do we need to replace our existing ERP or equipment to use this?

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. We would only suggest replacing something if it is blocking progress, and even then we prove it with data first.

What sensor data do we need for predictive maintenance to work?

It depends on the equipment and the failures you want to predict. Most predictive maintenance systems start with vibration, temperature, and runtime data, which most modern production equipment already produces. We check your current sensor coverage during discovery and tell you honestly what is enough and where the gaps are. You do not need a fully instrumented plant to start. You need enough data on your highest-risk assets.

How much historical data do we need before AI models are useful?

For predictive maintenance, 12-24 months of equipment data with documented failure events is a good starting point. For production planning and quality AI, 6-12 months of production records, demand history, and quality logs is usually enough to train useful models. Discovery shows what you have and whether it is enough. We will tell you upfront if the data makes a particular approach unworkable for now.

How do you handle deployment in an OT environment with safety requirements?

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

What does a manufacturing AI project typically cost and how long does it take?

Four to five months to a production-ready system. Projects start from $20K. The price depends 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 does not change based on how long things take us. We deliver a payback model based on your actual downtime costs, scrap rates, or planning cycle time before you sign anything.

What does our team need to manage the system after handoff?

You don't need a machine learning team. Your existing engineering or IT team can run the systems we deliver. The systems come with dashboards and alerts that need no data science knowledge to use day to day. At handoff you get the full codebase, documentation, and training. Model retraining schedules are built in, so the system stays accurate over time without manual work. If you want ongoing support as you add use cases, retainer options are available but you're never locked in.

AI for manufacturing
that runs on your data with EdgeFirm

Mid-market manufacturers use EdgeFirm to cut unplanned downtime with predictive maintenance, give plant managers real-time answers from their ERP data, and automate production planning across systems that were never meant to talk to each other. Custom systems. Fixed price. Production in 4 to 5 months.

Book Strategy Call See the Manual Minds AI Case Study

Predictive maintenance, planning and quality4 to 5 months from discovery call to productionYou own the code at handoff, no lock-in

In production

What We BuildWhat AI looks like on a manufacturing floor that's actually using it

We don't sell a platform. We build systems designed around your specific equipment, your existing data sources, and the operational decisions your team makes every day. Everything below ships in 4 to 5 months.

Live operations,ranked in one view.

plant.yourcompany.com/lines

LineUptime hrsTrendOEE

Line 1 · Stamping1,28491%

Line 2 · AssemblyLive97288%

Line 3 · Paint61281%

Line 4 · Welding39874%

Line 5 · Packaging24169%

CNC cell A19886%

CNC cell B15683%

Head-to-head,against your targets.

plant.yourcompany.com/lines

AssetVibrationTempAlert

Press #4 bearing7.8mm/s72°CService

Conveyor M22.1mm/s41°CNormal

Compressor C13.4mm/s58°CNormal

Spindle S96.2mm/s66°CService

Pump P31.9mm/s39°CNormal

Robot R72.6mm/s44°CNormal

Every request,routed automatically.

plant.yourcompany.com/lines

Defect classDetected byStation

Surface scratchVision modelPaint exit

Misaligned weldVision modelWeld cell 2

PorosityInspectorX-ray bay

Dimensional driftProcess AICNC cell A

Label errorVision modelPackaging

What we build

Purpose-built forManufacturing & Industrial Operations AI.

1From Sensor Data to Maintenance Alert Before the Failure

Predictive Maintenance Pipeline

Ingest vibration, temperature, pressure, and runtime data from your existing sensors and SCADA systems
Anomaly detection models trained on your own failure history, not generic equipment benchmarks
Maintenance alerts pushed to the right team member before a failure, with time-to-action recommendations
Work order integration with your CMMS so alerts automatically create scheduled tasks

2Demand Signals, Inventory, and Capacity All in One Place

Production Planning AI

Unified data pipeline pulling from ERP, WMS, supplier portals, and demand forecasting tools into a single clean layer
AI-generated production schedules that weigh real-time capacity, material supply, and demand signals together
Scenario modeling: planners can run "what if" changes in demand or supplier lead time and see the production impact in minutes
Natural language interface so planners ask questions in plain English rather than running ERP reports

3Catch Problems at Step Three, Not Step Ten

Quality Defect Detection & Process AI

Process parameter monitoring that flags deviations correlated with defects before the end of a production run
Root cause analysis AI that connects defect patterns to upstream process variables tells you what caused it, not just that it happened
Automated quality check triggers at defined process checkpoints, based on real-time sensor readings
Dashboard for quality managers showing defect rate trends, top failure modes, and recommended corrective actions

4Your ERP and MES Data, Answerable in Plain Language

Operations Intelligence Interface

LLM-powered interface over your ERP, MES, and production data. Plant managers ask questions and get answers without IT involvement
"Why did yield drop on line 4?" "What's our real capacity for the next three weeks?" Answered in seconds, sourced from actual system data
Automated daily briefings sent to shift supervisors, no dashboard login required
Works with your existing ERP systems (SAP, Oracle, Microsoft Dynamics) with no replacement needed

Reporting

One clear view,ready for every stakeholder.

Live dashboards and pipelines turn scattered operational data into one picture your team can act on, exportable for leadership.

Trained on your equipment's own failure history
Integrates with SAP, Oracle, and your SCADA systems
No platform fees or per-device pricing after handoff
Fixed price, scoped before we write a line of code

See the Manual Minds AI Case Study

plant.yourcompany.com/lines

LineUptime hrsTrendOEE

Line 1 · Stamping1,28491%

Line 2 · AssemblyLive97288%

Line 3 · Paint61281%

Line 4 · Welding39874%

Line 5 · Packaging24169%

CNC cell A19886%

CNC cell B15683%

Production stack

Built on Production-Grade InfrastructureThe stack behind manufacturing AI that runs on the floor

Every system we deliver runs on infrastructure your team can own and extend, no black boxes, no per-seat licensing.

Manufacturing Systems

SAP / Oracle ERP integration · Microsoft Dynamics · SCADA / PLC data ingestion · CMMS (Maximo, SAP PM) · MES systems via API

AI & Models

Time-series anomaly detection · Forecasting models (LSTM, XGBoost) · LangChain / LlamaIndex for NLP · Computer vision for visual QC · Custom fine-tuning where needed

Data Infrastructure

Apache Kafka / MQTT (real-time) · TimescaleDB / InfluxDB · dbt data transformation · PostgreSQL / Snowflake · Full audit logging

Infrastructure

Python & FastAPI · React / Next.js dashboards · AWS / Azure / on-premise · Docker / Kubernetes · Edge deployment where needed

Observability & Ops

CI/CD pipelines · Logging & metrics · Error tracking · Uptime monitoring · Automated backups

Full production stack

The models, vector stores, data tooling and infra behind every build.

Common questions

Questions about AI for manufacturing

: 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. We would only suggest replacing something if it is blocking progress, and even then we prove it with data first.
: It depends on the equipment and the failures you want to predict. Most predictive maintenance systems start with vibration, temperature, and runtime data, which most modern production equipment already produces. We check your current sensor coverage during discovery and tell you honestly what is enough and where the gaps are. You do not 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 good starting point. For production planning and quality AI, 6-12 months of production records, demand history, and quality logs is usually enough to train useful models. Discovery shows what you have and whether it is enough. We will tell you upfront if the data makes a particular approach unworkable for now.
: We treat operational technology differently from standard IT. AI systems that touch the floor run in read-only mode on the sensor and SCADA layer. They observe and alert. They do not control equipment directly, unless that is a specific, explicitly scoped requirement with a full safety review. Edge deployment options are available for sites with strict network segmentation. Security and compliance go into the design from week one, not as an afterthought.
: Four to five months to a production-ready system. Projects start from $20K. The price depends 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 does not change based on how long things take us. We deliver a payback model based on your actual downtime costs, scrap rates, or planning cycle time before you sign anything.
: You don't need a machine learning team. Your existing engineering or IT team can run the systems we deliver. The systems come with dashboards and alerts that need no data science knowledge to use day to day. At handoff you get the full codebase, documentation, and training. Model retraining schedules are built in, so the system stays accurate over time without manual work. If you want ongoing support as you add use cases, retainer options are available but you're never locked in.