Manufacturing context product
Give every plant agent one operational truth.
Connect live plant conditions to the order, material, quality, maintenance, workforce, and delivery commitments behind each decision.
What you get
Grounded in your systems. Governed by your rules.
Ungoverned agents either see too little to be useful or too much to be safe. This is what keeps a plant agent both.
One view of the plant. Only what your role allows.
Every agent reads live MES, ERP, QMS, and CMMS state through the context layer—never a stale export. Role, site, and shift decide what it can see and do, so an operator gets their line, not the whole plant's cost and HR data.
Every action leaves a trail. Every deployment stays yours to govern.
Identity, policy decision, sources used, and the resulting action are all recorded. Deploy managed, or inside your own AWS, Azure, or GCP environment—governed by your identity, encryption, and data-residency controls.
Outcome story · Throughput loss
A line is drifting. Decide before throughput becomes scrap.
Bodhi assembles the exact asset, active order, material genealogy, live process state, quality limits, maintenance history, and downstream schedule.
Shared contextAsset→Order→Material→Process→Quality→Maintenance→Shipment
- SeeConnect the signal to the affected product, material, work, and commitments.
- DecideCompare continue, slow, or stop against quality risk and production impact.
- ActCoordinate maintenance, quality, and planning with approvals and a complete evidence trail.
Manufacturing outcomes
Move from plant signal to coordinated action.
Resolve production exceptions
Choose the safest action with schedule, material, labor, and customer impact attached.
MESERPAPSPrioritize maintenance
Rank work by failure risk, current run, spares, skills, and lost output.
IoTEAMCMMSTrace quality genealogy
Follow a non-conformance across lots, suppliers, parameters, assets, and changes.
QMSPLMLIMSRespond to supply change
Replan capacity, inventory, and commitments when supply or demand shifts.
PlanningWMSOrdersWho needs this context
Four roles, one disconnected plant.
Each role below already owns a system of record. What none of them has is the context sitting in the other three—which is exactly where a production exception gets stuck.
PersonaWhat they needThe gap todayHow the context layer closes it
Production / plant operator
Live process state tied to the active order, material genealogy, and the quality limits behind it.
Sees the alarm, not the order, material, or quality context that explains whether it's safe to keep running.
Entity-resolves the alarm to its order, material genealogy, and quality limits at query time—one packaged answer, not four systems to open.
Maintenance / reliability engineer
Failure risk ranked by the current production run, spare availability, technician skills, and lost output.
Work orders get prioritized by ticket age or CMMS queue position, not actual production impact.
Reranks the CMMS queue against live production run state, spare stock, and technician skill graphs—not ticket age.
Quality engineer
A full genealogy trace from a non-conformance back to lot, supplier, process parameters, and asset.
QMS, PLM, and LIMS don't share identifiers—tracing a defect back to its source takes days of manual cross-referencing.
Resolves QMS, PLM, and LIMS identifiers into one traceable genealogy automatically, replacing days of manual cross-referencing with a single query.
Supply / production planner
Real-time visibility into how a material or demand shift cascades into schedule and customer commitments.
Only about 30% of manufacturers have real-time cross-system visibility—most planners are reacting to yesterday's picture.
Propagates a material or demand event through temporal assembly, so schedule and commitment impact update the moment supply changes—not on the next planning cycle.
Why manufacturing is built this way
IT and OT were never designed to collide.
Half the plant floor predates the systems meant to read it.
Operational technology and information technology evolved on separate timelines, vendors, and standards—one built to run for decades unchanged, the other built to integrate and update constantly.
- Older OT assets get the same governed context as anything shipped this year—no rip-and-replace required
- Real-time visibility doesn't depend on every system sharing one vendor or one refresh schedule
- Each new AI use case reuses the same integration instead of rebuilding it from scratch
An alert is not a decision. A dashboard is not a coordinated response.
Traditional plant analytics stops at a prediction or an alarm; without connected context, nobody moves from insight to a safe, coordinated next action.
- Every alarm resolves with the order, material, and quality context needed to act on it safely
- IT and OT entry points are governed under one identity and policy layer, not left for each system to guard alone
- Downtime and quality escapes get caught at the handoff, before they compound across systems
Engineered, not assembled
Depth becomes fewer unplanned stops.
Digital twins, multi-agent coordination, and industrial context modeling are architecture decisions made here, not integration afterthoughts—validated against production, genealogy, and maintenance accuracy before anything ships.
Context is its own AI modality, not a side effect of prediction.
Environmental and operational awareness are treated as a distinct layer from generative and predictive AI—the layer adaptive, real-time plant decisions actually run on.
Multi-agent maintenance needs a shared source of truth.
Every maintenance, quality, and planning agent reads from the same governed context instead of reconciling separate, drifting views of the plant.
Point an agent at raw plant data, and it re-derives the logic every time.
Certified definitions and lineage travel with the data, so the same question about yield or downtime returns the same number no matter how it's asked.
Alerts and dashboards are the old ceiling. Coordinated action is the new one.
The agent determines what changed, diagnoses why it matters, recommends a response, and coordinates the next step—it doesn't stop at a dashboard.
How we validate: Every architecture decision is checked against production, genealogy, and maintenance accuracy before it reaches an agent.
Why BodhiContextAI
Give agents the right context—and prove why it was right.
We are building the operating layer that structures complex business, customer, product, operational, and financial knowledge—making AI agents accurate, contextual, and directly useful for every permitted persona.
Real-time, hybrid truth
Route across SQL, keyword, semantic, graph, and live API context, kept current through events and CDC.
Role-bound MCP access
Expose context and tools through MCP so agents exchange evidence and act only within role, purpose, and permission.
Compounding knowledge
Every correction, decision, and evaluation strengthens the context layer—so the next agent starts ahead of the last one, not from zero.
Deploy where you govern
Run cloud-native with us or inside your cloud, close to the systems that remain authoritative.