A grid-scale battery is projected to lose 20 to 30% of its capacity in its first decade, and the state-of-health number you are making decisions on can be off by as much as 8%. We tell you what’s going to fail, when, and why — while there’s still time to do something about it.
A decades-long capital decision resting on a number nobody has measured.
Your warranty curve, your replacement schedule and your runtime commitment all rest on reference conditions — a stated temperature, a stated depth of discharge — that your assets never actually see. Field history exists. It just no longer matches how the assets are being used.
Your design life is the strongest part of the story and the part buyers push hardest on. Sodium-ion is young; graphene-modified formats are younger. No product in either has decades of field history, because not enough time has passed for anyone to have it.
Monitoring reports the present — voltage, current, temperature, a coarse state of charge. Neither problem is answered by knowing today's reading.
Time replacement early and you spend capital you did not need to spend. Time it late and you buy cells under duress.
Meanwhile capacity guarantees go unclaimed, because the burden of proof sits with the operator and almost nobody can meet it.
The decision is not whether to model degradation — every one of these numbers already is a model. It is whose model, and how honest it is about its own limits.
Sources: Uptime Institute, Annual Outage Analysis 2026 (2025 survey); NREL Annual Technology Baseline; augmentation cadence per published industry reporting. Figures are directional industry data, not Choir results — your numbers come from the hindcast.
A physics core models the degradation mechanism; the learned layer captures the real-world residual it does not. You get a projection expressed in mechanisms a reviewer can interrogate, not a curve fit.
Degradation is driven by temperature, so temperature is a first-class structural input rather than an afterthought.
No-leakage, cell-level testing, held out across temperature regimes the model never saw in training. This is the first thing a serious reviewer looks for, and the reason our numbers survive one.
Confidence on every prediction, widening honestly with distance from the data. Green, amber, red: on defined gates the model refuses the call rather than returning a number you might act on.
Runs at the asset, not cloud-only — including bandwidth-constrained and secured sites. Sits on your existing BMS and EMS. No rip-and-replace.
A vendor who will not is asking you to guess. This is the slide our competitors do not bring.
Physics-informed remaining-life and degradation forecasting, benchmarked on the public NASA prognostics datasets.
ProvenGreen / amber / red abstention on defined missing-input and staleness gates — the model refuses the call rather than returning a number you might act on.
ProvenExecution at the asset, including bandwidth-constrained and secured sites.
ProvenAn accuracy advantage over a tuned statistical baseline, on a held-out temperature band bracketed by the training data. The figure stays under diligence until we have measured-device validation behind it.
Early resultTransfer of the method to LFP.
In validationTransfer to sodium-ion and other emerging chemistries.
Not startedBecause our own testing does not support them.
That our physics extrapolates better than statistics outside trained conditions. In one strict extrapolation test a tuned statistical baseline beat ours.
That abstention makes us more accurate under degraded inputs. It does not. It tells you when not to act on the number.
It has cost us shipped collateral.
Battery degradation, power-electronics prognostics, edge model compression, and graceful degradation under missing inputs — each with a written report, an ablation, and an internal review pass.
Every performance number passes a claims gate before it ships. Two of our own documents are out of circulation right now until their numbers are corrected.
Sensing and connectivity through our partners — Spearix for WirelessHART instrumentation, LinkWorx for private-APN cellular. SENTRIX™ sits on the BMS and EMS you already run.
The method is benchmarked on lithium-ion. Transfer to LFP is in validation. Transfer to sodium-ion is not yet demonstrated — establishing whether it holds on your cells is exactly what a scoped feasibility is for. We would rather tell you that now than have you find it in your own diligence.
Before you commit to anything.
We look at what your BMS and EMS actually log and tell you whether it supports a defensible degradation call.
You send historical cycle data and hold back the outcome. We predict it, then show our answer against what happened.
Integrated with your BMS and EMS. Live remaining-life, runtime and augmentation forecasting on a defined asset group.
Ongoing intelligence across the fleet, with the claim ladder re-tested as your own data accumulates.
Send us history from one asset group and hold back the outcome. If we are wrong, you have spent a scoped fee and learned something about your fleet. If we are right, you have a number you can put in front of your board, your insurer and your OEM.
Mutual NDA and data-ownership terms are papered first, with no exceptions — your test data is your IP and stays that way. Then a fixed-fee feasibility on cycle-plus-calendar life, which tells you plainly if your data will not carry the projection.
The data readiness review costs nothing. The hindcast is a scoped fee, creditable against what follows — and it tells you whether the number is worth betting capital on.
