Resolve business entities
Connect assets, products, sites, services, resources, orders, and customers across conflicting identifiers.
Enterprise AI context layer
Your agents know the model. Give them your operations.
Connect plant-floor reality and telecom network state with the business rules, customer impact, and evidence AI agents need to recommend—and safely execute—the next best action.
Why context breaks
Your operational truth is spread across systems and teams.
A machine alarm means little without its work order, product run, maintenance history, and quality limits. A network alarm means little without the affected service, customer SLA, order state, and revenue exposure. The context exists; it does not travel with the decision.
Carry operational meaning
Encode relationships, policies, hierarchies, units, and business definitions alongside raw data.
Keep context current
Blend durable knowledge with real-time events, inventory, telemetry, status, and exceptions.
Enforce access once
Apply user, role, site, region, and purpose controls before context reaches a model or tool.
Show the evidence
Return source, timestamp, lineage, and confidence so people can verify every consequential answer.
Reuse across agents
Serve governed context through stable retrieval and action interfaces instead of rebuilding every integration.
Why enterprises need it
A capable model still lacks enterprise truth.
The model supplies language and reasoning. A context layer supplies the current facts, relationships, permissions, and evidence that make those capabilities dependable inside a real operation.
Models do not know your private or current state.
Model knowledge is bounded by training data. It does not automatically include today’s work orders, network incidents, customer entitlements, engineering changes, or internal policy.
Grounding guidance · Google Cloud ↗The right document is only part of the answer.
Operational decisions also depend on live events, entity relationships, business definitions, constraints, and the user’s authority. The layer assembles that task-specific context before a model reasons.
Every new agent can multiply inconsistency.
Without shared context, each agent rebuilds identity matching, retrieval, permissions, and business rules. A common layer gives every agent the same governed meaning and reusable interfaces.
Trust requires provenance, evaluation, and monitoring.
For consequential work, teams need to know which source informed an answer, how fresh it was, what policy applied, and how the system behaves after deployment.
Generative AI Profile · NIST ↗The architecture
From systems of record to systems of action.
The context layer sits between enterprise systems and AI experiences. It interprets, governs, and packages the smallest useful context for each task.
Operational sources
ERP · MES · PLM · QMS
OSS · BSS · NMS · CRM
Orders · Billing · Field service
Documents · IoT · Events
BodhiContextAI
Governed enterprise context
Entity resolution + semantic modelRetrieval + event contextPolicy + permission enforcementProvenance + evaluation
Agent experiences
Plant operations copilots
Network assurance agents
Customer + field service
Coordinated workflows
What we are building
Cloud native by design. Hosted on your terms.
We are building the BodhiContextAI enterprise context layer as modular, API-first cloud infrastructure. It is designed to connect to the systems you already run, use the model stack you choose, and fit the security boundary your organization requires.
Now buildingA governed context foundation for production agents
Containerized servicesPortable building blocks rather than a closed appliance.
Private connectivityConnect systems without exposing private data sources publicly.
Model choiceUse approved commercial, cloud, or self-hosted models.
Built-in evidenceCarry source, freshness, policy, and lineage with every context response.
Private networking is a standard enterprise pattern for agent workloads; for example, Microsoft documents agent deployments using customer virtual networks and private endpoints. View the architecture guidance ↗
01 · Manufacturing
Move from plant signals to coordinated action.
Production, quality, maintenance, engineering, and supply teams each see part of an exception. The context layer gives their agents one governed operational picture, so a recommendation accounts for the whole plant—not one system at a time.
Outcome story · Production exception
A line is losing throughput. What should happen next?
The agent connects the live machine signal with the active production order, recent engineering change, maintenance history, quality limits, material availability, and downstream schedule.
- DetectCycle time is drifting beyond the product-specific control range.
- ExplainA changed component and an overdue calibration are correlated with the deviation.
- EvaluateContinuing risks a quality hold; stopping now affects two downstream orders.
- CoordinateRecommend a controlled maintenance window, resequence orders, reserve the part, and open the quality check—with evidence and approvals attached.
Production exception resolution
Bring schedule, material, asset, labor, and process constraints into one decision instead of reconciling them in a war room.
MES · ERP · APS · WMSMaintenance in operating context
Prioritize work by failure risk, current product run, spare availability, technician skills, and downstream production impact.
IoT · EAM/CMMS · manuals · inventoryQuality and traceability
Trace a non-conformance through lots, suppliers, process parameters, inspection results, and engineering changes.
QMS · PLM · genealogy · supplier dataConnected planning
Surface how a demand, capacity, supplier, or maintenance change cascades through production, inventory, and customer commitments.
S&OP · planning · procurement · orders02 · Telecom OSS + BSS
Connect network state to customer and revenue impact.
OSS knows what failed. BSS knows who is affected and what was promised. A shared context layer lets assurance, care, field service, order management, and revenue agents reason over the same service reality.
Outcome story · Service degradation
Thousands of alarms fire. Which one matters first?
The agent correlates RAN, transport, and core events with service topology, affected products, open orders, customer SLAs, recent complaints, planned work, and field capacity.
- CorrelateCollapse symptom alarms into one probable fiber-path failure.
- PrioritizeRank impact by critical services, enterprise SLAs, customer count, and revenue exposure.
- DecideCompare reroute, remote remediation, and dispatch options against capacity and policy.
- CoordinateOpen one incident, trigger the approved network action, brief care, notify affected customers, and preserve the evidence trail.
Service assurance and triage
Correlate alarms across domains, map them to services and customers, and prioritize remediation by business impact.
NMS · fault · performance · topologyOrder fallout and activation
Explain where an order stalled across catalog, orchestration, inventory, provisioning, billing, and partner dependencies.
CRM · order management · activation · billingCustomer care resolution
Give care agents the live service state, entitlement, device, interaction, and incident context needed for the next best action.
CRM · product catalog · SLA · knowledgeRevenue and capacity decisions
Connect usage, charging, leakage signals, demand growth, and network capacity to guide assurance and investment decisions.
Mediation · charging · billing · planningStart narrow. Compound value.
Prove one workflow, then turn its context into shared infrastructure.
We begin with the decision or action that matters, map the minimum context it needs, establish quality and governance, and expose it through a reusable contract. The next agent starts ahead.
Choose the decision
Define the user, workflow, systems, risk, and evidence that signal a useful outcome.
Build the context slice
Connect sources, resolve meaning, apply policy, and evaluate answers against real cases.
Productize the contract
Serve stable context interfaces, monitor quality, and extend across agents and business units.