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OPTINX
SolutionsStrategic Priority

From Machine Data to Decisions and Action

Industrial Intelligence transforms fragmented operational data — manuals, sensor telemetry, technician knowledge — into evidence-backed diagnosis, maintenance guidance, and continuous operational learning.

The Problem

Industrial organizations lose time, expertise, and money because operational knowledge exists in silos — and the people who hold it are retiring.

Why Existing Workflows Fail

1

Machine failure occurs

Technician starts manual diagnosis

2

Search documentation

Manuals outdated, scattered across systems

3

Check maintenance records

CMMS data incomplete or unsearchable

4

Ask senior technician

Expert unavailable or retiring

5

Trial-and-error repair

Extended downtime, no learning captured

The OPTINX Approach

1

Machine failure occurs

Alert triggers automaticcontext retrieval

2

Insora retrieves manuals + history

Knowledge graph instant query

3

Sensor telemetry correlated

Pattern matching against past incidents

4

Evidence-backed diagnosis

Root cause with citations and confidence

5

Step-by-step repair guidance

Procedure with parts, tools, safety notes

6

Resolution captured in Nextmos

Continuous learning for future incidents

Architecture

Four connected layers from raw operational data to continuous learning.

1

Data Sources

Technical manuals (PDF, HTML)SOPs & proceduresMaintenance records (CMMS)Sensor telemetry (SCADA, IoT)Service work ordersTechnician notes
2

Insora Knowledge Layer

Document ingestion & parsingKnowledge graph constructionHybrid retrieval (vector + graph + keyword)Evidence-backed synthesisProcedural guidance generation
3

QUACK Runtime

Query orchestrationTool execution (CMMS, ERP, IoT)Permission enforcementHuman approval gatesAudit trail logging
4

Nextmos Memory

Incident historyResolution patternsTechnician expertise profilesMachine baselinesContinuous learning feedback

Governance & Security

Human Approval Points

Safety-critical repair procedures require supervisor sign-off
Parts ordering requires procurement approval above threshold
Machine shutdown requires operations manager authorization
Procedure deviation requires engineering review

Security Considerations

Data residency: process on-premises or in designated cloud region
PII redaction: technician names and sensitive data masked in logs
Encryption: at-rest and in-transit for all operational data
Access control: role-based permissions for diagnosis, repairs, and knowledge
Audit trail: every diagnosis, recommendation, and action logged with provenance
No secrets shipped client-side; all model inference server-side

Potential Outcomes

These are potential outcomes based on the architecture and approach. Actual results depend on data quality, integration depth, and operational maturity. OPTINX does not present unverified ROI as fact.

Time to Diagnosis

Reduced from hours to minutes

Potential

First-Visit Resolution

Higher rate with evidence-backed guidance

Potential

Knowledge Retention

Institutional memory preserved as technician demographics shift

Potential

Mean Time to Repair

Reduced through procedural guidance and parts pre-staging

Potential

Deployment Approach

1

Phase 1 — Discovery

2-4 weeks. Audit existing data sources, documentation, CMMS, and IoT systems. Identify highest-value use cases.

2

Phase 2 — Pilot

4-8 weeks. Deploy Insora with a focused corpus (e.g., one machine type or one facility). Measure diagnosis accuracy and time savings.

3

Phase 3 — Expansion

8-16 weeks. Extend to additional machine types, facilities, and data sources. Integrate with CMMS, ERP, and IoT platforms.

4

Phase 4 — Production

Ongoing. Full deployment with continuous learning, SLA monitoring, and knowledge graph evolution.

Request Pilot →