Drishti
drishti - sight, the faculty of seeing clearly
One role-aware console for equipment health, energy, alarms, and OT security.
Drishti is a real-time operations console for industrial plants, built around an LNG-site data model and generalized from it. It unifies equipment health, predictive maintenance, energy intelligence, alarm management, OT security, and digital-twin data quality in one place, then tailors the view by role so a reliability engineer and a plant director are looking at the same truth through different lenses. It answers what the sensors say; Arjuna answers what the cameras see.
Workflow
Problem
Plant data is scattered across historians, alarm systems, energy meters, and security tooling, so no one role sees the whole picture and alarm floods bury the signals that matter.
Outcome
Equipment health, predictive maintenance, energy intelligence, alarms, OT security, and digital-twin quality unified into one at-a-glance operating view.
01
Unify the plant record
Historian, SCADA, alarm, energy, and OT security feeds are brought into one model.
02
Suppress the noise
Alarm triage collapses floods and known false positives so operators see events, not volume.
03
Detect early
Thermal, vibration, and throughput anomalies are surfaced before they become downtime.
04
Route by role
Each role gets the slice it can act on, with escalation paths that match the operating structure.
Modules
Equipment Health
Live condition across rotating and static assets, with thermal and vibration risk surfaced early.
Alarm Intelligence
Alarm floods triaged and false positives suppressed so the real events stay visible.
Energy Intelligence
Consumption and efficiency tracked against production so energy cost is attributable.
Role-Aware Views
Plant directors, reliability engineers, maintenance managers, energy managers, and security analysts each see their own console.
Representative use cases
At a Glance Multi Plant Operational Telemetry & Anomaly Detection
A global specialty chemicals manufacturer operating 14 continuous processing plants with over 80,000 IoT sensors tracking temperature, flow rate, pressure, and chemical acidity.
Intelligent Alert Triaging & False Positive Alarm Suppression
An automated automotive body stamping and robotic welding plant generating over 45,000 raw sensor alarms daily across 350 robotic welding cells.
Predictive Thermal & Vibration Risk Monitoring in Rotary Equipment
A major power generation and water utility managing 240 high capacity centrifugal pumps, steam turbines, and industrial compressor units.
Cross Line Bottleneck Synchronization & Real Time Throughput Balancing
A high speed beverage bottling and packaging plant running 6 canning lines producing 1,500 cans per minute per line across depalletizing, filling, seaming, pasteurization, and case packing.
Executive Plant Floor Digital Twin & Live Shift Handover Cockpit
An international pharmaceutical packaging enterprise managing 8 sterile packaging facilities producing injectable vials and blister packs across North America and Europe.
Evidence
What Wayam can show for this entry
Curated solution design with an owner and a review date. No performance or production claim.
Allowed claims at this tier: Possible workflow, typical stack, prerequisites.
- Evidence tier
- T4 · Reference pattern
- Integration status
- Typical enterprise system
- Metric status
- Scenario only
- Evidence owner
- Not yet attached
- Validated
- Not yet attached
- Valid until
- —
- Content version
- 2026.09
Pending evidence attachment
This entry represents an enterprise architectural reference pattern. Customer benchmarks, run replays, and metric verifications are established during technical discovery.
Limitations
- Headline metrics are representative until a customer result is attached.
Representative metrics · Reference · scenario only
One console
health, energy, alarms, and OT security
Role-aware
views for five operating roles
These describe the intended outcome of the design. They are not measured customer results until a validated case is attached above.
Architecture & controls
Composed across the operating loop
Ingest
Connect the systems of record and read the signals the work already produces.
Covered by the platform
Reason
Ground context, score options against policy and the stated goal, draft the next step.
3 agent roles
Act
Execute an approved step in the system of record and keep the evidence.
7 agent roles
Govern
Set policy, gate consequential actions on a named approver, and audit what ran.
Covered by the platform
Pack Architecture & Agent Composition Graph
10 Composed Agents- AGT-0157
Production Schedule Optimization Agent
Manufacturing Operations
- AGT-0201
Computer Vision Quality Inspection Agent
Manufacturing Operations
- AGT-0337
Plant Digital Twin Agent
Manufacturing Operations
- AGT-0222
Incident Triage Agent
IT Operations
- AGT-0223
Root Cause Analysis Agent
IT Operations
- AGT-0224
Change Risk Assessment Agent
IT Operations
- AGT-0142
Predictive Maintenance Agent
Asset Management
- AGT-0351
Process Anomaly Detection Agent
Manufacturing Operations
Control gates
- A named approver on every consequential write-back
- Evidence and audit trail kept with every run
- In-boundary deployment available
Capability atoms
- signal-ingestion
- system-connectors
- anomaly-detection
- root-cause-reasoning
- alert-routing
- in-boundary-deployment
Typical enterprise systems
Typical stack for solution design; compatibility is validated during discovery.
- SAP Ariba
- Coupa
- Snowflake
- Dun and Bradstreet API
- Cognex VisionPro
- Siemens MES MCP
- NVIDIA Jetson
- SAP S/4HANA
Designed for
energy-utilities, manufacturing, logistics
Business case
Model a Drishti scenario with your own baseline
Three scenarios from the numbers you enter. Capacity released is time; it becomes a saving only when roles or costs are actually removed or avoided.
Improvement is seeded at the midpoint of this entry's directional band (20–40%). Source status: scenario only. Edit it to your own estimate.
| Scenario | Improvement | Capacity released | Cashable savings | Net annual | Payback |
|---|---|---|---|---|---|
| Conservative | 11% | — | — | — | — |
| Expected | 21% | — | — | — | — |
| Upside | 29% | — | — | — | — |
Enter an annual volume and a baseline cost to see figures.
Illustrative planning scenario, not a guarantee. Results depend on process baseline, adoption, data quality, integration scope, controls, and deployment costs.
Pilot this
From solution design to a bounded pilot
Step 1 · 3–4 weeks
Discovery Sprint
Validate the workflow, data, controls, baseline and business case before anything is built.
Gate: Blueprint and pilot plan signed by the business owner, technical owner and Wayam.
Step 2 · 6–10 weeks
Bounded Pilot
Prove quality and value on agreed data against agreed acceptance tests.
Gate: Acceptance thresholds met on the evaluation set; go/no-go decision recorded.
Agrani · evidence pending6 Agrani candidates in this solution: Computer Vision Quality Inspection Agent, Incident Triage Agent, Change Risk Assessment Agent, Process Anomaly Detection Agent, Maintenance Work Order Optimization Agent, Predictive Maintenance AgentPairs well with