Battery-process priors
Built around real upstream failure pathways, not generic anomaly scores.
Reads from
AVEVA PI System · Siemens Opcenter · Aspen InfoPlus.21 · Ignition by Inductive Automation · custom historian and CSV exports
Data security
Process data stays in your environment. On-premises and private-cloud deployments supported.
Product stack
Four layers that compound together
Lychee combines battery process knowledge, hybrid physics-and-ML inference, resilience to messy plant data, and outputs engineers can act on.
Hybrid inference
Built for sparse defect labels, slow feedback loops, and conditions training data didn't cover.
Messy-data readiness
Works around fragmented, incomplete, and uneven plant history.
Engineer-usable outputs
Returns ranked risk, likely drivers, and investigation priorities.
Hybrid inference
Battery physics and machine learning, working together
Lychee combines battery process knowledge with machine learning trained on plant data. Physics gives the model structure where defect labels are sparse and outcomes arrive late. The learned components adapt to differences between lines, lots, and operating conditions.
Time-series modeling
Lychee uses time-series foundation models — large machine-learning systems pretrained on diverse industrial data — that produce calibrated confidence intervals, not single-number anomaly scores. Engineers see a probability range and a driver, not just an alert. Lychee is advised by the lead architect of Chronos at AWS.
Plant-scale modeling
Lychee’s approach to scaling across plants — adapting to new lines, transferring what’s learned without retraining from scratch, and bounding risk in safety-critical environments — is laid out in Linda Hong Cheng’s ICLR P-AGI workshop paper.
Battery manufacturing makes machine learning hard for specific reasons. Defects are rare against millions of normal lots. Inspection labels are noisy. Lines and chemistries differ enough that a model tuned for one breaks on another. And missing a defect costs orders of magnitude more than flagging a false alarm at gigafactory scale. Lychee is built for these conditions — not retrofitted from general-purpose anomaly detection.
Why it compounds
The advantage compounds with each plant deployment
What sets Lychee apart over time isn't the model. It's everything that builds up around it as plants deploy.
01 — Battery priors
Lychee carries battery-specific failure knowledge into the model — the causal links between upstream process variance and downstream yield outcomes — coating, drying, calendaring, formation. This isn’t in a general ML library, and a competitor can’t reconstruct it from public data alone.
02 — Plant-specific signal
Each plant deployment adds plant-specific knowledge to the model: equipment fingerprints, lot-genealogy patterns, shift-by-shift variance, and the defect families that show up on this chemistry, this line, this operating window.
03 — Compounding gap
A new competitor starts from zero every time they sign a customer. Lychee’s advantage isn’t the model itself — it’s the battery process knowledge plus the deployment history that makes each new flag on a customer’s line more precise than a generic anomaly detector can be on day one.
Lychee vs generic industrial AI
Why a generic industrial-AI platform doesn't translate
| Dimension | Generic industrial-AI platform | Lychee approach |
|---|---|---|
| Domain priors | Cross-industry equipment health, no battery-specific failure pathway encoding (e.g. Cognite Data Fusion, AspenTech Mtell, Uptake) | Trained around battery-process priors: coating, drying, calendaring, formation |
| Time-series modeling | Per-sensor anomaly detection, decoupled from chemistry | Foundation-model-grade inference, tied to lot context and process windows |
| Label assumption | Built for plentiful failure labels (rotating equipment, condition monitoring) | Built for sparse defect labels and delayed downstream confirmation |
| Output shape | Anomaly score per sensor or asset | Ranked upstream drivers with calibrated confidence and engineer-readable trail |
| Deployment model | Platform-first; lengthy services engagements; rip-and-replace integration | Pilot-first; scoped around existing systems; first prediction in 5 days |
Battery priors
Where battery process knowledge meets plant data
Lychee gets sharper as plant-specific data meets battery process knowledge — the model learns the failure pathways particular to your chemistry, line, and operating window.
Mixing instabilityCoating driftDrying variabilityCalendaring shiftsLot genealogyFormation outcomes
Data architecture
Process data stays inside the plant perimeter
Lychee operates in cloud-isolated, on-premises, or private-cloud environments. No raw process data is transmitted to Lychee infrastructure. Model outputs and investigation summaries return to the plant team’s existing review and quality interfaces.
Built for manufacturing environments with strict data governance — including defense-adjacent supply chains, EU GDPR obligations, and IP-sensitive gigafactory operations.
Next step
Turn technical interest into a scoped discussion
Discuss strategic deployment
For manufacturing groups evaluating a plant-facing rollout path.
Discuss Strategic DeploymentScope a pilot
Start with one line, one defect family, and one KPI.
Scope A PilotSee product architecture
Review the architecture and workflow logic behind the product.
See Product Architecture