Contact UsContact UsRISHI delivers decision intelligence to the energy value chain, translating complex process behavior into margin protection and operational stability.
RISHI is an AI-powered industrial intelligence platform for predictive troubleshooting, automated RCA, and early deviation detection, reducing failures and downtime.
A real-time view of equipment condition, emerging risks, and active cases helps teams intervene early and protect reliability.
The Knowledge Hub offers step-by-step guides, videos, and manuals to assist users in performing and verifying.
RISHI's asset view shows health scores, live telemetry, safety intelligence, and predictive root cause analysis.
Visualizes how material, energy, and constraints move across interconnected units, revealing where issues propagate and where control actions are most effective.
RISHI’s AI assistant, Prockie, uses voice or text to provide unit-level answers and maintenance insights.
RISHI’s RCA tree uses color-coded nodes to help users analyze active faults and investigate specific root causes.
We address the recurring troubleshooting barriers that slow decisions, increase downtime, and cause repeated failures across refinery and process operations.
RISHI has demonstrated measurable impact across real operational environments
Root causes identified in 1.5–3 hrs instead of 4–6 hrs, enabling earlier corrective action.
Process stabilization achieved in 4–5 hrs instead of 8 hrs, reducing downtime exposure.
Avoiding even one CDU slowdown per quarter can prevent $0.5–1.5M in production losses.
Knowledge reuse + validated corrective workflows reduce recurring flooding/fouling events.
Engineers save 5–8 hours/week previously spent manually gathering, correlating, and reviewing data.
RISHI accelerates predictive troubleshooting by automating RCA, detecting process deviations early, validating actions with simulation, and improving refinery reliability.
RISHI connects detection, diagnosis, and validation into one intelligent workflow, replacing manual correlation and enabling faster, confident troubleshooting across refinery operations.
RISHI continuously analyzes thousands of process variables with ML-based anomaly detection to surface abnormal patterns early and trigger risk-based alerts before failure events occur.
RISHI uses AI/ML and generative reasoning to analyze past incidents and live sensor data, generating evidence-based RCA paths with risk-ranked recommendations and probable causes.
RISHI uses simulation and thermodynamic models to test corrective actions under virtual conditions, enabling “what-if” scenario validation before execution to reduce risk and uncertainty.
RISHI stores each resolved event in a global knowledge hub, enabling cross-site learning, standardized RCA, and access to lessons learned to prevent tribal knowledge loss as experts retire.
See how RISHI helps teams detect issues early, diagnose faster, and make confident operational decisions. It turns complex process behavior into clear, actionable guidance to resolve unit upsets and slow degradation.
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