Ripik.AI

Plants are airframes. Operators are pilots.
We are the autopilot.

The plant is already the airframe. The DCS is already the cockpit. Operators are flying solo with 1970s instruments. We retrofit the autopilot — earned per loop, per shift.

Sense. Reason. Act. Learn.

Hybrid physics-informed ML across BF charge dynamics, slag chemistry, hot-blast control. Pure ML fails on rare events. Pure first-principles fails on sensor drift. We pair them — the only approach that holds up at the edge, on a real plant, under shift-change conditions.

Sense CAMERAS + SENSORS Reason PHYSICS + ML Act SETPOINT TO DCS Learn CROSS-PRIME FLYWHEEL SUB-200MS · NO DATA LEAVES THE PLANT

Three layers of containment, by default.

01

Safety envelope

Setpoints are bounded by physically-safe ranges per asset. The autopilot cannot push beyond them. Ever.

→ Zero unsafe-action incidents across production loops.

02

Human-in-loop escalation

High-impact actions require captain confirmation. Tiers configurable per loop, per shift, per criticality.

→ Operators stay in command — accept rates climb as trust compounds.

03

Earned autonomy

Shadow → advisory → closed-loop, per loop. Never assumed. Always reversible.

→ Once installed, never replaced. Every deployment widens the moat.

Earn the loop. Then earn the plant.

The captain stays in the chair. The autopilot earns each loop, per shift. Pilots ship inside 8 weeks — the first loop is live by quarter end.