Every Engineer On Shift
Is Your Best
AssetBlue captures how the best engineers reason — and turns that judgment into a living system that can diagnose, investigate, guide, and teach across every asset and every shift.
The gap isn't information. It's engineering judgment.
Plants already have alarms, manuals, maintenance logs, and SOPs. What they don't have is a scalable way to preserve and apply the reasoning that turns signals into decisions.
If the same problem happened at another thermal plant, we don't have access to that knowledge. We call NTPC, call BHEL, wait for callbacks.
A comprehensive list of failures of that particular equipment worldwide — that would be helpful.
We have a history book only. If he has not faced such problems, he can go through that — he'll get some idea.
Industrial reasoning breaks traditional AI.
Complexity explodes
Industrial systems are highly interconnected. As variable count rises, the space of possible causal structures grows too quickly for naive discovery.
Failure data is sparse
Critical assets don't fail often enough to generate rich training data. Real-world failure events are rare, uneven, and incomplete.
Black boxes don't earn trust
Classification models can label likely causes, but they can't show the reasoning path, causal mechanism, or evidence chain behind the answer.
Expertise doesn't scale
Traditional RCA methods like 5 Whys and FTA are powerful, but they remain manual, slow, and dependent on scarce experts.
Not just when things break. When things don't look right.
The same reasoning engine that supports post-failure diagnosis helps engineers investigate anomalies before they become shutdowns.
An engineer opens AssetBlue, describes what they're seeing or hearing, and shares a photo or video. The system reasons through possible causes, retrieves similar patterns from prior cases, and helps the engineer investigate before the issue escalates.
Structured reasoning. Not keyword search.
AssetBlue guides engineers through structured investigation using Fishbone, 5 Whys, Fault Tree, and FMEA — not as static templates, but as live reasoning paths.
Diagnose and learn. Simultaneously.
Most systems automate workflow. AssetBlue develops judgment. Every interaction transfers reasoning — not just answers — so the workforce gets stronger with every investigation.
Every session makes it smarter.
Wrong diagnoses are auto-tagged, classified, and reviewed weekly. The knowledge base and the workforce improve together — automatically.
The switching cost isn't data migration. It's the relationship.
Live on the first asset class. Measured against reality.
Structured evaluation against 171 expert-validated failure cases across 9 industries. Each case scored on five dimensions against a plain-LLM baseline.
On the hardest cases — unusual mechanisms, cross-industry failures, sparse prior documentation — the knowledge base turns misses into diagnoses.
Different role. Same conviction.
Your diagnostic partner at 2am.
AssetBlue works the way you work — voice-first, shift-aware, and built for the field.
Start in under 60 seconds
Open the app. Select the asset. Describe what you see. Investigation begins.
Works before failures, not just after
Investigate anomalies, unusual sounds, and drift — not just breakdowns.
Your plant's history, instantly searchable
Every past investigation feeds a queryable knowledge base specific to your site.
Built for the field
Voice-first. Glove-friendly. Works across shifts. No dashboard to navigate.
Six commitments. Non-negotiable.
Investigations persist across shifts
A Case survives shift changes, dead batteries, and interruptions. Any engineer can continue from where it stopped.
Case-basedEvery output has a next step
Every hypothesis includes the action, the owner, the priority, and whether it's safe without supervisor sign-off.
Action-completeEvery claim cites its source
Traceable to a case study, OEM standard, or technical reference. Tappable. Disputable.
Cited and verifiableBuild once. Deploy across 32 industries.
41 asset types span $36T of industrial output. One boiler model works in power, mining, chemicals, and refining.
Universal assetsAdapts to the engineer automatically
Detects expertise and urgency in real time. Senior gets speed. Junior gets reasoning. No mode to select.
Automatic coachingDesigned for 2am, not 2pm
Voice input. Glove-friendly targets. No dashboard. The app opens to one screen: Start RCA.
Worst-shift testedPurpose-built beats general-purpose.
- ✗3–7 days per investigation
- ✗Knowledge locked in heads
- ✗No cross-plant learning
- ✗No audit trail
- ✗Repeat failures uninvestigated
- ⚠₹2–10 crore implementation
- ⚠12–18 month deployment
- ⚠Requires data science team
- ⚠RCA is a checkbox feature
- ⚠Predicts failures, doesn't diagnose them
- ✓Root cause in hours, not days
- ✓Knowledge captured and queryable
- ✓Cross-plant learning from day one
- ✓Cited evidence on every output
- ✓Complements Maximo/SAP — not a replacement
- ·Conversational UI wrapper around an LLM
- ·Basic RAG pipeline over technical PDFs
- ·Generic troubleshooting chatbot
- ·Mobile form-factor for field teams
- ✓Curated, semantically indexed failure corpus per asset class
- ✓Structured causal reasoning over domain-specific failure graphs
- ✓Coaching-first interaction calibrated to engineer expertise
- ✓Compounding knowledge from every completed investigation
- ✓Field-validated across real plant conditions and languages
Others monitor. Others document. Others route service knowledge. AssetBlue is building the reasoning layer.
Not a target — a floor. A wrong diagnosis acted on once means the engineer never opens the app again. We validate against physical outcomes, not satisfaction surveys.
Correctness over speed
A fast wrong answer destroys trust permanently.
Cited, never asserted
Every output traces to a case study, standard, or validated pattern.
Human-reviewed improvement
Nothing deploys without expert sign-off.
60-day recurrence tracking
If the same failure recurs, the diagnosis was wrong — and the system learns.
Six researchers across computer vision, NLP, edge AI, and applied ML — spanning IIIT Hyderabad and industry.
One engine. Every critical asset.
Questions from every plant visit.
Describe the problem.
Let's reason through it together.
Request a demo. We'll walk you through a live diagnostic session in 30 minutes. Or request a pilot implementation with your asset class.
From the team that built Embibe — India's largest AI education platform, backed by Reliance Jio.