Your assets.
Self-driving.
AndonEAM is the AI platform that deploys specialized agents to build reliability strategies, diagnose asset health, and generate execution plans autonomously.
Built for asset-intensive operations. Governed by your engineers.
Why a Platform
Break the cycle of
decisions that don't scale.
Organizations face significant barriers in scaling asset reliability: skill gaps leading to inconsistent results, effort-intensive processes overloading engineers, and complex systems resulting in low adoption.
Adding another dashboard won't fix this. To break the cycle, you need a unified AI platform that turns engineering context into approved, repeatable decisions — across every operational domain.
- 📋 Effort Intensive: Overloaded engineers building RCM
- 🔔 Skill Gaps: Inconsistent results & endless alerts
- ⏳ Poor Data: Drag on planning work orders
- 🔍 Complex Systems: Low adoption & siloed context
- 💸 Relying on expensive "tribal" knowledge
- ⚡ Reliability Agent: Strategy generated in hours
- 🧠 Monitoring Agent: Root-cause prescriptive action
- ✅ Planning Agent: Work orders auto-built instantly
- 📋 Seamless workflow adoption for engineers
- 💎 One platform. Every decision. Autonomous.
AndonEAM is the enterprise intelligence layer built for heavy industries
Built from the ground up for the AI era and designed for the future of how humans and AI agents will work together in asset-intensive industries
An operating system of AI agents that overlays a semantic map over traditional databases. Agents are grounded in the ontology, bringing domain expertise directly to the data.
We bridge the massive disconnect between corporate database records and the messy reality of the factory floor by integrating AI intelligence, the tribal knowledge of workers, and structured/unstructured data.
We enable the orchestration of AI agents across proprietary platforms, removing the barriers of siloed data across systems to streamline enterprise workflows.
We bring deterministic trust to AI through granular, human-in-the-loop control and ontological constraints in databases. Humans remain the ultimate author and legal controller of every action.
We bring rapid time to value, ensuring our platform is ready to use in 3–5 days (not months).
Running on platform neutral architecture, we offer infinite operational extensibility and can quickly scale from one site to twenty.
An operating system of AI agents that overlays a semantic map over traditional databases. Agents are grounded in the ontology, bringing domain expertise directly to the data.
We bridge the massive disconnect between corporate database records and the messy reality of the factory floor by integrating AI intelligence, the tribal knowledge of workers, and structured/unstructured data.
We enable the orchestration of AI agents across proprietary platforms, removing the barriers of siloed data across systems to streamline enterprise workflows.
We bring deterministic trust to AI through granular, human-in-the-loop control and ontological constraints in databases. Humans remain the ultimate author and legal controller of every action.
We bring rapid time to value, ensuring our platform is ready to use in 3–5 days (not months).
Running on platform neutral architecture, we offer infinite operational extensibility and can quickly scale from one site to twenty.
An operating system of AI agents that overlays a semantic map over traditional databases. Agents are grounded in the ontology, bringing domain expertise directly to the data.
We bridge the massive disconnect between corporate database records and the messy reality of the factory floor by integrating AI intelligence, the tribal knowledge of workers, and structured/unstructured data.
We enable the orchestration of AI agents across proprietary platforms, removing the barriers of siloed data across systems to streamline enterprise workflows.
We bring deterministic trust to AI through granular, human-in-the-loop control and ontological constraints in databases. Humans remain the ultimate author and legal controller of every action.
We bring rapid time to value, ensuring our platform is ready to use in 3–5 days (not months).
Running on platform neutral architecture, we offer infinite operational extensibility and can quickly scale from one site to twenty.
An operating system of AI agents that overlays a semantic map over traditional databases. Agents are grounded in the ontology, bringing domain expertise directly to the data.
We bridge the massive disconnect between corporate database records and the messy reality of the factory floor by integrating AI intelligence, the tribal knowledge of workers, and structured/unstructured data.
We enable the orchestration of AI agents across proprietary platforms, removing the barriers of siloed data across systems to streamline enterprise workflows.
We bring deterministic trust to AI through granular, human-in-the-loop control and ontological constraints in databases. Humans remain the ultimate author and legal controller of every action.
We bring rapid time to value, ensuring our platform is ready to use in 3–5 days (not months).
Running on platform neutral architecture, we offer infinite operational extensibility and can quickly scale from one site to twenty.
An operating system of AI agents that overlays a semantic map over traditional databases. Agents are grounded in the ontology, bringing domain expertise directly to the data.
We bridge the massive disconnect between corporate database records and the messy reality of the factory floor by integrating AI intelligence, the tribal knowledge of workers, and structured/unstructured data.
We enable the orchestration of AI agents across proprietary platforms, removing the barriers of siloed data across systems to streamline enterprise workflows.
We bring deterministic trust to AI through granular, human-in-the-loop control and ontological constraints in databases. Humans remain the ultimate author and legal controller of every action.
We bring rapid time to value, ensuring our platform is ready to use in 3–5 days (not months).
Running on platform neutral architecture, we offer infinite operational extensibility and can quickly scale from one site to twenty.
An operating system of AI agents that overlays a semantic map over traditional databases. Agents are grounded in the ontology, bringing domain expertise directly to the data.
We bridge the massive disconnect between corporate database records and the messy reality of the factory floor by integrating AI intelligence, the tribal knowledge of workers, and structured/unstructured data.
We enable the orchestration of AI agents across proprietary platforms, removing the barriers of siloed data across systems to streamline enterprise workflows.
We bring deterministic trust to AI through granular, human-in-the-loop control and ontological constraints in databases. Humans remain the ultimate author and legal controller of every action.
We bring rapid time to value, ensuring our platform is ready to use in 3–5 days (not months).
Running on platform neutral architecture, we offer infinite operational extensibility and can quickly scale from one site to twenty.
Three Agents. One Platform.
Meet the agents
inside the engine.
Each agent is a specialized AI system — built for one domain, grounded in your engineering reality, and governed by your team.
Reliability Agent
Accelerate RCM from months to days. AI generates the strategies from complete asset context—your team reviews, edits, and hits approve
Document Intelligence
Parses both structured and unstructured engineering data — including work order history, P&IDs, and handwritten maintenance logs — with native multimodal AI and deep semantic understanding
Reliability Agent
Accelerate RCM from months to days. AI generates the strategies from complete asset context—your team reviews, edits, and hits approve
Asset Monitoring Agent
Moves from noisy threshold alarms to contextual, prescriptive diagnostics. Cross-references live telemetry with approved failure modes to explain what's degrading and what to do.
Six Analytical Engines
Statistical fingerprinting, ARIMA forecasting, multivariate regression, unsupervised clustering, and supervised classification — each autonomously assigned to the failure mode it was designed to detect.
Asset Monitoring Agent
Moves from noisy threshold alarms to contextual, prescriptive diagnostics. Cross-references live telemetry with approved failure modes to explain what's degrading and what to do.
Planning Agent
From bottlenecked planners to instant, execution-ready work orders. Generates job plans, parts lists, permits, and task sequencing with embedded safety from day one.
Intelligent Work Order Generation
AI reads the notification, cross-references equipment history, manufacturer documentation, and your maintenance library to auto-generate complete work orders with operations and materials.
Planning Agent
From bottlenecked planners to instant, execution-ready work orders. Generates job plans, parts lists, permits, and task sequencing with embedded safety from day one.
Measurable platform impact.
Accuracy, Accountability,
and Adoption
Customer-governed by design. AndonEAM handles context assembly, drafting, and traceability while your reliability teams retain approval control inside the SaaS workflow.
Accuracy
Outputs are bounded by verified documentation, plant history, operating context, and engineering standards. No generic AI guesses.
Accountability
AI-generated output stays reviewable through product states, rationale, edit history, and explicit approval before strategy adoption.
Adoption
Designed for daily engineering use, with guided review surfaces, CMMS handoff, and workflows that reduce training burden.
Domain expertise
meets deep technology.
Decades of industrial reliability leadership, enterprise-scale operations, and production AI engineering — purpose-built to earn the trust of the teams who keep critical infrastructure running.
Vina Devi
Founder
Driving the vision to transform industrial maintenance through autonomous AI from strategy to execution.
Mukesh Kumar
President, Global Enterprises
Bridging enterprise reliability needs with AI-powered solutions across asset-intensive industries.
LinkedIn- Shared cloud with strict data isolation
- Standard API connectors & data exports
- Fixed monthly AI credits
- Annual SaaS subscription
- Ideal for single-site pilots
- Physically isolated cloud, custom subdomain & SSO
- Bi-directional sync: SAP, Maximo, OT Historians
- Unrestricted AI credit pool — flex for turnarounds
- Phased 30–45 day deployment with formal UAT
- Implementation milestone + monthly retainer
Questions, answered.
Everything you need to know about AndonEAM before getting started.
Point solutions solve one problem in isolation. AndonEAM is an ontology-driven platform where every agent — Reliability, Monitoring, and Planning — operates on a shared engineering context: your asset hierarchies, failure modes, operating conditions, and approval history. When the Reliability Agent drafts a strategy, the Monitoring Agent already understands those failure modes in real time, and the Planning Agent can generate work orders from the same approved context. This shared intelligence layer is what eliminates the data re-entry, context loss, and integration overhead that plagues multi-vendor toolchains.
Yes. The agent architecture is domain-agnostic by design. While our initial agents target reliability engineering, condition monitoring, and maintenance planning, the underlying ontology engine and human-in-the-loop workflow can be extended to any operational domain where engineering context drives decisions — including inspection management, regulatory compliance, capital planning, and operational risk assessment. New agents inherit the same trust model, approval controls, and audit trail without requiring a separate integration.
AndonEAM is built for organizations operating across multiple facilities, asset classes, and regulatory environments. Each site maintains its own engineering context and approval workflows while sharing a unified taxonomy and agent configuration at the enterprise level. This means a reliability strategy approved at one facility can be adapted and redeployed across similar assets at other sites — without starting from scratch. Role-based access, site-scoped data isolation, and centralized reporting are native to the platform, not bolt-on features.
Most analyses that traditionally take 3-6 months of workshops can be drafted in 4-8 hours with AndonEAM. The platform ingests the complete engineering context, generates FMEA and maintenance strategies, and gives your engineers a structured review workflow instead of a blank-page workshop.
Absolutely. All data is encrypted in transit (TLS 1.3) and at rest (AES-256). Your documents and analysis outputs are isolated to your tenant and never used to train shared models.
Yes. AndonEAM seamlessly connects with your existing systems to eliminate manual data entry. Our platform automatically pulls in the necessary asset data and pushes approved maintenance plans, task lists, work orders, etc. directly into leading CMMS platforms like SAP PM, Maximo, Hexagon EAM, Oracle eAM, and many more.
AndonEAM accepts PDF, Word (.docx), Excel (.xlsx), images of P&IDs, and plain text files. The AI can parse scanned documents via OCR, structured datasheets, and unstructured maintenance logs. There is no required template — upload what you already have.
AndonEAM is delivered as a multi-tenant SaaS platform. Customers onboard assets, run AI workflows, review outputs, manage approvals, and control access inside the application. Our team supports onboarding and integration while the repeatable workflow runs through software.
Your engineers do. AI-generated strategies move through in-product approval controls where reviewers can edit failure modes, tasks, intervals, and rationale before anything is finalized. This keeps engineering accountability inside the SaaS workflow without turning delivery into a services project.