The gap in the market. The science behind the system. The proof it holds up.
Every other player monitors, documents, or digitises. AssetBlue is the reasoning layer the industry has been missing — grounded in an authoritative industrial corpus and validated against reality.
The pieces exist. The integrated system does not.
A $17B market of point solutions. None combines structured causal reasoning, a curated failure knowledge base, and zero-infrastructure entry. AssetBlue sits in the empty quadrant.
Predictive Maintenance & APM
Enterprise EAM and anomaly detection over telemetry. $500K–$2M+ entry, 12–24 month deployment, requires sensors and historians.
RCA Documentation Software
Templates for recording Fishbone, 5 Whys, and fault trees. Some AI-assisted auto-fill, but the human drives every reasoning step.
Service Resolution AI
Conversational troubleshooting over enterprise tickets and manuals. Workflow-based fix playbooks, retrieval-based not causal.
Field Ops / Workflow Digitisation
Digitised SOPs, inspection checklists, and mobile workflows for deskless teams. Execution platforms, not reasoning platforms.
The plants that need diagnostic intelligence most — under-instrumented facilities with retiring workforces and no CMMS — are the ones that can never afford enterprise APM.
The entire market is designed for the top 5% of facilities. AssetBlue is the first platform for the other 95%.
What's easy to copy vs. what's hard to replicate.
- ·Conversational UI wrapper around an LLM
- ·Basic RAG pipeline over technical PDFs
- ·Generic troubleshooting chatbot
- ·Mobile form-factor for field teams
- ✓Domain-specialised SLM trained on failure ontology, not general web data
- ✓Multimodal reasoning across text, voice, image, and video in field conditions
- ✓Curated, semantically indexed failure corpus per asset class
- ✓Shared reasoning layer across RCA, compliance, and asset life
- ✓Coaching-first interaction calibrated to engineer expertise
- ✓Field-validated across real plant conditions and languages
Anyone can wrap an LLM. Few can assemble the corpus, field-validate across plant conditions, and compound knowledge one asset class at a time. That's the moat.
Built from authoritative industrial sources.
The knowledge base is curated from OEM maintenance manuals, IEEE and ASME technical standards, EPRI and VGB PowerTech industry research, and 171 real failure case studies. Every diagnostic output traces to a specific document in this corpus. Nothing is generated from general training data.
Qualitative first. Probabilistic over time.
AssetBlue starts by capturing engineering reasoning before enough failure data exists. As evidence accumulates, the system becomes increasingly quantitative, probabilistic, and self-improving.
Knowledge-First
Extract causal structure from manuals, engineering text, prior cases, and domain knowledge. Use Ishikawa, FTA, and related reasoning frameworks as scaffolds for structured investigation.
Prior Elicitation
Structured prompting to generate initial priors over likely relationships, mechanisms, and hypotheses. Treat these as starting beliefs, not ground truth.
Bayesian Learning
As case data accumulates, refine priors with observed evidence and transition toward data-driven probabilistic inference and confidence-weighted hypothesis ranking.
Five layers. One auditable reasoning loop.
Text, photo, video, voice, operating evidence
Retrieve context, history, manuals, and prior cases
Maintain hypotheses, evidence, eliminated causes, and confidence
Adapt to the engineer's experience and explain the logic
Every case updates the knowledge base and future reasoning
Every investigation updates an auditable state: symptoms, hypotheses, evidence, eliminated causes, confidence, and next best question.
Live on the first asset class. Measured against reality.
Automated evaluation across all 171 expert-validated failure cases spanning 10 industries. Four dimensions scored by an independent LLM judge against expert ground truth.
On the hardest cases — unusual mechanisms, cross-industry failures, sparse prior documentation — the knowledge base turns misses into diagnoses.
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.
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.