The Agentic AI Platform for
Industrial Reliability.
AndonEAM is a product-led SaaS platform that automates reliability engineering for industrial operations. Built on Google Cloud with Vertex AI and Gemini, it coordinates specialized agents for RCM analysis, predictive diagnostics, and maintenance execution planning.
An AI-first SaaS startup on a mission.
Operating from India and the USA (Houston), AndonEAM is building an agentic AI SaaS platform purpose-built for industrial reliability engineering. We replace months of manual RCM workshops, noisy alert triage, and disconnected maintenance planning with autonomous, engineer-governed product workflows powered by Google Cloud and Gemini.
AI-native reliability software, deployed as SaaS.
AndonEAM runs reliability workflows as software: Gemini supports engineering reasoning, Google Cloud hosts the orchestration and data layer, and customers manage assets through secure tenant workspaces.
Gemini powers the core workflow
AndonEAM uses Gemini and Vertex AI to generate reliability strategies, diagnostics, and maintenance plans inside the product experience.
Repeatable workflows across plants
Tenant workspaces, subscriptions, guided onboarding, and in-app approvals make the workflow repeatable across facilities and teams.
Built on scalable Google Cloud services
Cloud Run, Workflows, BigQuery, Cloud Storage, Pub/Sub, Firebase, and Secret Manager support secure orchestration for industrial workloads.
Built by engineers,
for engineers.
A team combining industrial reliability expertise, enterprise operations experience, and full-stack AI product engineering.
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.
LinkedInEnterprise-grade AI infrastructure.
Built on Google Cloud Platform with a scalable, secure architecture for AI-native industrial workflows, from context assembly to orchestration and approved execution.
Multi-Agent SaaS Orchestration
Reliability, Monitoring, and Planning agents are orchestrated through Google Cloud Workflows to deliver repeatable, tenant-isolated reliability workflows inside one SaaS product.
Full-Context Engineering Intelligence
Instead of RAG, AndonEAM assembles complete asset context packages from documentation, history, telemetry, and standards so Gemini can reason over the full engineering record.
Engineer-Governed Approval
Every strategy passes through productized review, edit, and approval controls. Your reliability engineers stay accountable while the SaaS platform handles context assembly, drafting, and traceability.
The Paradigm Shift
Break the cycle of
intensive manual effort.
Organizations face significant barriers in scaling asset reliability: skill gaps leading to inconsistent results, effort-intensive processes overloading engineers, complex systems resulting in low adoption, and dirty data dragging down planning.
Adding another complex APM dashboard or running another one-off improvement project won't fix this. To break this cycle, organizations need productized AI workflows that turn engineering context into approved, repeatable maintenance decisions.
- 📋 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
- 💎 Scalable, standardized autonomous intelligence
The Industry's First Autonomous Engine
Powered by three
specialized AI Agents.
Full-context reliability workflows delivered as scalable SaaS.
Reliability Agent
Builds FMEA, maintenance strategy, and task recommendations from complete asset context including OEM manuals, P&IDs, history, and operating conditions.
Learn moreAsset Monitoring Agent
Cross-references live telemetry with approved failure modes and history to explain what is degrading, why it matters, and what actions to take.
Learn moreMaintenance Planning Agent
Generates execution-ready job plans, parts lists, permits, and task sequencing from the same approved engineering context.
Learn moreQuestions, answered.
Everything you need to know about AndonEAM before getting started.
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.
No. AndonEAM is designed around full-context engineering intelligence. Asset manuals, P&IDs, maintenance history, operating context, and review instructions are assembled into complete context packages so Gemini can reason over the whole engineering record instead of relying on fragmented retrieval.
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.