Your assets.
Self-driving.
AndonEAM is an AI-assisted platform for drafting reliability strategies, reviewing asset-health context, and preparing execution plans with human approval.
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 scoped operational workflows.
- 📋 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: Structured strategy drafts
- 🧠 Monitoring Agent: Root-cause prescriptive action
- ✅ Planning Agent: Reviewable work-order drafts
- 📋 Seamless workflow adoption for engineers
- 💎 One platform. Shared context. Human approval.
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.
Granular review states and human-in-the-loop controls keep customer engineers responsible for editing, approving, and applying AI-assisted recommendations.
Pilot scope and rollout timing are agreed after reviewing data readiness, integration requirements, security controls, and customer governance.
The architecture supports a focused pilot and a governed path to additional sites, asset classes, and operational workflows.
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.
Granular review states and human-in-the-loop controls keep customer engineers responsible for editing, approving, and applying AI-assisted recommendations.
Pilot scope and rollout timing are agreed after reviewing data readiness, integration requirements, security controls, and customer governance.
The architecture supports a focused pilot and a governed path to additional sites, asset classes, and operational workflows.
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
Create a structured first draft from available asset context, then let your team review, edit, and approve the engineering rationale.
Document Intelligence
Extracts candidate context from structured and unstructured engineering sources. Source quality and extracted fields must be verified before use.
Reliability Agent
Create a structured first draft from available asset context, then let your team review, edit, and approve the engineering rationale.
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
Supports multiple analytical approaches, with model selection and configuration validated against the asset, data quality, and monitoring objective.
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
Prepare reviewable job-plan drafts, parts suggestions, permit prompts, and task sequencing from approved engineering context.
Work Order Drafting
Uses the notification and available approved context to prepare a job-plan draft for planner validation, completion, and release.
Planning Agent
Prepare reviewable job-plan drafts, parts suggestions, permit prompts, and task sequencing from approved engineering context.
Platform scope at a glance.
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 can link to reviewed documentation, plant history, operating context, and the customer's chosen engineering method. Reviewers verify the evidence and judgement.
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 for governed, AI-assisted industrial maintenance from strategy through approved execution.
Mukesh Kumar
President, Global Enterprises
Bridging enterprise reliability needs with AI-powered solutions across asset-intensive industries.
LinkedIn- Shared-cloud option with tenant-aware access controls
- Scoped data import and export workflows
- Usage allowance defined in the order form
- Annual SaaS subscription
- Designed for a focused, single-site pilot
- Dedicated-environment, custom-domain, and SSO options
- Integration scope agreed for CMMS and operational data sources
- Usage and capacity sized to the agreed workload
- Phased deployment with formal UAT and agreed milestones
- Commercial terms defined in the customer order form
Questions, answered.
Everything you need to know about AndonEAM before getting started.
AndonEAM connects Reliability, Monitoring, and Planning workflows to a shared engineering context: asset hierarchies, approved failure modes, operating conditions, and review history. That shared foundation can reduce duplicate data entry and preserve context between supported workflows, subject to the customer's configuration and integrations.
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.
Timing depends on asset scope, source quality, and the customer review process. AndonEAM is designed to accelerate the first draft by assembling available engineering context and giving engineers a structured workflow for reviewing FMEA and maintenance-strategy recommendations. Pilot milestones are agreed during onboarding.
AndonEAM applies access controls, tenant-aware data handling, and encryption safeguards appropriate to the configured service. Security requirements, retention, model-provider handling, and deployment options are reviewed with each customer. See the Security page for the current control overview and contact us for a detailed assessment.
Integration options are scoped around the customer's CMMS, data model, permissions, and governance requirements. AndonEAM can prepare approved maintenance-plan data for controlled handoff; the exact import, export, or synchronization method is validated during discovery rather than assumed to be plug-and-play.
Common onboarding sources include PDFs, Word documents, spreadsheets, images, and plain text. Extractability varies with scan quality, formatting, and document structure, so source material is validated before it is relied on in an engineering workflow.
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.