The self-improving loop for robotic factories.
Kilnara builds a physics-accurate twin of your robotic line, retrains its robot policies overnight in simulation, and deploys only the ones that pass a safety check. A line that changes at 5pm is running again by morning.
Trusted by automation teams across automotive, electronics, and logistics.
Built on the NVIDIA Physical-AI stack · deployed on real lines
Robotic lines are brittle. One change and the robots stop earning.
When a part, layout, light level, or SKU changes, robot policies degrade — and teams spend weeks hand-tuning and re-collecting real-world data on a live line. It's slow, expensive, and unsafe to gather.
Weeks of downtime
Every changeover triggers manual re-tuning while the line sits idle or runs below rate.
Data you can't collect safely
Edge cases and failures are dangerous and costly to reproduce on physical hardware.
Twins that only watch
Visualization twins show the plant but never train it. One-shot sims train once, then drift.
One autonomous loop that keeps the twin, the data, and the robots in sync.
Kilnara connects simulation to reality and back again — so the line improves overnight and wakes up smarter.
Four products. One loop.
Adopt the full platform or start with a single module — each one is built to snap into the loop.
KilnSim
The physics-accurate twin. Author, calibrate, and keep your line synced with reality.
Learn more →KilnTrain
Nightly RL/IL retraining with synthetic data and a validation gate you control.
Learn more →KilnOps
The agentic copilot. Ask the twin questions and run change-impact analysis in plain language.
Learn more →Train on the changes that haven't happened yet.
New SKUs, worn grippers, glare, occlusions, a shifted fixture — Kilnara simulates the variations that break real robots and generates labeled data for every one.
- World-model scene generation — photoreal variations across the reality gap.
- Domain randomization — lighting, texture, physics, and pose jitter for robust transfer.
- Auto-labeling — 6-DoF pose and segmentation, free from the simulator.
No policy touches hardware until it earns it.
Every retrained policy is validated in the twin against performance bounds and safety constraints. Human sign-off, a full audit record, and one-tap rollback are built in.
- Performance thresholds — cycle time, success rate, and force limits gated before release.
- Human-in-the-loop sign-off — approvals routed to the right engineer.
- One-tap rollback — revert to any prior validated policy instantly.
| Pick success | 98.7% | ≥ 97% |
| Cycle time | 4.1s | ≤ 4.5s |
| Peak force | 18N | ≤ 25N |
| Collisions | 0 | = 0 |
From line scan to smarter robots in four moves.
A repeatable path from your physical cell to a loop that compounds every night.
See the platformBuild the twin
We scan your cell and author a calibrated, physics-accurate twin in OpenUSD.
Generate & retrain
KilnTrain simulates variations, generates data, and retrains policies nightly.
Validate & deploy
Policies pass the safety gate, then ship to KilnEdge at the cell.
Stream telemetry back
Real line data refines the twin — and the loop gets sharper every run.
Wherever robots meet change.
The same loop adapts across discrete manufacturing and fulfillment.
We used to lose the better part of a month every time a line changed. With Kilnara the twin retrains overnight and we validate before anything touches a robot. It changed how our floor plans for change.
It plugs into the stack you already run.
Robots, controllers, cameras, PLCs, MES, and your cloud — Kilnara connects to the floor without a rip-and-replace.
Browse integrations- On-prem, VPC, or air-gapped deployment
- SSO / SAML, SCIM, and role-based access
- Your line data stays yours — never shared
- Full audit log of every policy and deploy
Enterprise-grade by default.
Kilnara runs where your data does. Deploy in your own environment, keep proprietary line telemetry in-house, and give auditors a complete record.
Read our security postureLatest thinking on Physical AI.
Closing the sim-to-real gap with world models
Why domain randomization and generated scenes beat hand-collected data on changing lines.
Designing a safety gate robots can't skip
How we validate every policy against performance and safety bounds before deploy.
What overnight retraining looks like on a real cell
A walk through one night in the loop, from telemetry to a validated v38.
Give your line a twin that keeps it learning.
See Kilnara run an overnight loop on a cell like yours. Book a 30-minute demo with our team.