One intelligence layer between plant-floor signal and operational decision.
Orion unifies the systems. Neo makes performance visible in real time. B3rry reasons over both and names the next action. Three modules, one governed data model, deployed on the plant you already run.
Unify machines, historians, MES, ERP, and operational sources into one governed model with lineage per field.
Render live performance across lines, shifts, assets, and sites — visible while it can still be influenced.
Reason over the model, resolve conflicting signals, and return the next action with its sources attached.
Most manufacturers already instrument everything: PLCs, historians, MES, ERP, maintenance systems, downtime logs, shift notes. The signal exists. It is fragmented across systems that were never designed to resolve into one operating picture — so the pattern is recognized after the output is already lost.
Operational data is distributed across machines, departments, and tools that were never designed to resolve into one system of record.
Teams learn what happened after downtime, quality loss, or throughput variance has already been absorbed by the production plan.
Engineers spend their capacity stitching reports together and re-explaining the same numbers instead of improving the process.
Dashboards report the metric. Teams still need what changed, why it changed, and which intervention is warranted next.
Every source resolves into one governed model of the plant. Every outcome is computed from that model — never a parallel extract maintained by hand.
The objective is not another reporting surface. It is reducing the interval between an operational event and a competent response to it.
Connect machines, telemetry, production records, and operational events into one foundation with source lineage retained per field.
Express downtime, OEE, throughput, and KPI movement against expected performance, so deviation is legible without interpretation.
Reason across the model to identify what changed, adjudicate between conflicting signals, and issue the next action as a work item.
Value appears in the operational areas where fragmentation and delay create measurable drag. Each case is owned by a specific module, so deployment scope stays explicit.
Recurring stoppages, loss drivers, and early signals identified against your reason-code taxonomy rather than after the fact.
Delayed reporting replaced with live performance across lines, shifts, products, and sites, computed from one model.
Plant-floor systems bridged to business systems without introducing another parallel workflow to maintain.
One operating picture for leadership and the floor, instead of a static report that explains yesterday differently to each audience.
Events, machine behavior, process context, and historical precedent joined so investigations close inside the shift that opened them.
Operational context used to explain what changed, why it matters, and which intervention is warranted — with the evidence attached.
Book a short walkthrough with our team and we’ll show how Orion, Neo, and B3rry can connect your systems, improve visibility, and identify practical opportunities for operational intelligence across your sites.