Turn production data into better production decisions.
Orders ship on time and lines stop less. Capacity, scheduling, downtime and yield are connected to demand and cost, so the schedule reflects the business, not just the machine.
What makes this hard today.
The plant produces enormous volumes of data and still schedules on experience. When throughput drops, the cause is debated after the shift rather than identified during it.
Schedules built without visibility of real available capacity
Downtime explained retrospectively, in the morning meeting
Yield loss absorbed into an average and never traced
Production plans disconnected from the demand they serve
The cost of deciding blind.
Capacity is the most expensive asset in the business and the least reversible decision. An hour of unplanned downtime cannot be recovered, and a schedule built on an optimistic capacity assumption fails quietly, late.
Production Intelligence
Veraius reads MES, ERP and machine telemetry together, models real available capacity, and evaluates schedule options against demand, material availability and cost — with the constraint that binds made explicit.
Capacity modelling
Available capacity by line and shift, from actual performance rather than nameplate.
Schedule evaluation
Options compared against demand, materials and cost before committing.
Downtime prediction
Where downtime is likely, and which machine carries the risk.
Yield analysis
Yield loss traced to line, product and process parameter.
Quality drivers
Which parameters correlate with quality outcomes, per line.
Cost per unit
Production cost decomposed so increases can be attributed.
Connect. Understand. Predict. Decide. Act.
The same pipeline runs behind every solution: your systems are connected, the data is put in business context, the outcome is predicted, the decision is reasoned with its sources attached, and the result is carried into workflow.
ERP, POS, MES, WMS, CRM and IoT — into one place.
Operational data related and put in business context.
Demand, stock, production and performance, ahead of time.
Decision models and AI weigh the options with you.
Decisions executed through workflow and automation.
What changes in the way you work.
Schedules rest on measured capacity instead of assumed capacity
Maintenance is planned against risk rather than a fixed calendar
Yield and quality problems are traced to a process, not to a shift
Production decisions stay connected to demand and margin
Where it is used in practice.
Weekly production planning
What to produce on which line, given demand and material coverage.
Capacity risk review
Where the coming schedule exceeds realistic available capacity.
Downtime investigation
Which machines and conditions precede unplanned stoppages.
Cost variance analysis
Why unit cost moved, decomposed into its contributing factors.
What your teams gain from Veraius agents.
Capacity against orders
Open orders are compared with available capacity so delivery risk shows before a due date is missed.
See the Manufacturing agents ManufacturingAnswers from the real query
Every figure comes from a query against your production data and links back to the records behind it.
See the Manufacturing agents Supply ChainSuppliers judged on performance
Delivery, quality and compliance are tracked per supplier.
See the Supply Chain agentsIn every department
- Agents prepare the work on your own data; your people approve what happens next
- Plain-language questions answered from live ERP data, with tables and interactive charts
- Grounding check: numbers that cannot be traced to a source are not shown
- Explainability, confidence and risk scores with decision rationale
- Slack, Teams, WhatsApp and Signal channels with an approval inbox
- Department-based access policy with personal data masking
Frequently asked questions
Straight answers about integrations, deployment, security and cost — including where the honest answer is "it depends".
Ask your own questionWhat decisions does production intelligence cover?
Sequencing, batch sizing, changeover timing and capacity commitment. These are made together in practice and modelled separately in most systems, which is why a schedule that is optimal on paper collides with a changeover nobody costed.
How is this different from what our MES already does?
An MES executes a plan and records what happened. It is authoritative on the present and silent on the alternative. Production intelligence sits above it: it decides what the plan should be given demand, capacity and constraint, and it records what it compared before concluding.
What data does it need?
Order book, routing and capacity, changeover matrix, and enough history to learn where the standard times are wrong. That last one matters more than it sounds: standard times drift and nobody updates them, so a schedule built on them is optimal against a factory that no longer exists.
Can it run alongside our current planning process?
That is the recommended start. Run it in parallel for a few weeks and compare its schedule to the one the planner produced. Where they agree, confidence builds; where they differ, one of the two is holding a constraint the other does not know about, and finding that out is worth the exercise on its own.