Digital DNA™ gives every battery cell a complete genetic record — pairing 12+ years of materials science with live manufacturing and field data. The result is AI that predicts safety risks, extends battery life, and lowers cost — every step from mine to mission.
One unbroken record that follows a battery through its whole life — from the mine where its raw materials come from, to the road or power grid where it works, all the way to recycling. Nothing gets lost. Everything stays traceable.
Where it all starts: we record exactly where a battery's raw materials came from and what's in them.
Lithium, cobalt, nickel and manganese are tracked from the geological source across global mining operations — the first link in the Genomic Thread™, established before a single electrode is made.
Every batch of processed material gets a tag, so it can always be traced back to its source.
Chemical, structural and electrochemical "fingerprints" are captured as raw materials are refined — creating a continuous, searchable record of material identity across every supplier and geography.
Manufacturing problems get caught early — before they become expensive.
Coating, pressing and forming data is combined with the material's known history. Process deviations are flagged in real time, before they pass downstream into cells and packs where the cost to fix multiplies.
Each finished cell carries the full history of the materials inside it.
Formation, capacity grading and pack-integration data are woven into every cell's record — connecting atomic-scale material decisions to how the finished pack actually performs.
Real-world use feeds back into the system, so batteries keep getting smarter.
Live field data — temperature, charge level, cycle history — is matched back to each battery's material record. Field data improves the AI models, and the models improve battery management in return: a closed learning loop.
At end of life, we know exactly how healthy a battery is — and what can be recovered.
A precise health assessment decides whether a battery is fit for a second life in grid storage, and routes recovered materials efficiently through recycling — every material still carrying its full history.
The platform works in three connected layers — from understanding materials at the smallest scale, to linking every piece of data together, to AI that predicts what happens next.
More than a decade of lab research, distilled into a detailed profile of every important battery material — cathodes, anodes, electrolytes and separators. It's the foundation every prediction is built on.
Materials are characterised using XRD, XPS, EIS and electron microscopy. Profiles cover NMC, LFP, NCA, LMO and next-generation chemistries, including silicon-carbon anodes and sodium-ion systems.
A single, searchable data spine that links mine, material, electrode, cell, pack and field behaviour into one coherent record — so problems can be traced and predicted across the entire value chain.
Unlike siloed MES or BMS data, the Genomic Thread™ preserves full provenance. Streaming pipelines ingest from MES, BMS, SCADA and field telematics, with blockchain-anchored checkpoints and sub-second trace latency.
AI trained on real material science — not anonymous fleet averages. It forecasts how a specific battery will age, warns of fire risk well in advance, and tunes usage to make batteries last longer.
Built on physics-informed machine learning (PINN): electrochemical physics equations fused with ML to predict capacity fade with chemistry-specific precision and quantified uncertainty.
Five parts of the battery economy with one shared need — batteries they can't afford to get wrong. One platform serves them all.
Hit yield targets despite messy, variable supply chains. Digital DNA™ flags bad incoming material before it enters production and links every process step to the finished cell — so the factory learns from itself, shift by shift.
Explore gigafactory solutions →Chemistry-aware battery management means longer range, safer vehicles and accurate resale valuations — connected straight to the car's existing systems.
EV solutions →Grid-scale storage needs precise health tracking. Genomic models forecast capacity loss years ahead and optimise when batteries charge and discharge.
Grid solutions →Prove your material quality with hard data linking it to real cell performance — a powerful edge in a market that otherwise competes on price alone.
Supplier programme →When a battery's first life ends, genomic health data shows exactly which packs are fit for reuse in storage — and routes the rest to recycling with full material history intact.
See how it works →Digital DNA™ isn't a dashboard bolted onto generic data. It's a purpose-built scientific platform. Here's the technology underneath — for the technically curious.
More than a decade of materials research, data engineering and AI — converging into one battery intelligence platform.
Deep materials research begins at C4V. Proprietary experimental datasets are built across battery chemistries and failure modes — the genomic foundation.
The full supply chain is mapped from mine to end use. The core Genomic Thread™ architecture is designed and the first industry partnerships form.
Prediction models are trained on genomic data and validated against real field results. Digital Twin infrastructure goes live for first pilot customers.
The platform expands to gigafactory, EV OEM and grid-storage use cases, with integrations into enterprise factory and battery-management systems.
Digital DNA™ grows into a global, continuously learning network connecting gigafactories, OEMs, fleets and recyclers — improving every battery it sees.
Join leading gigafactories, OEMs and energy operators replacing guesswork with genomic certainty. Request a tailored walkthrough of the Digital DNA™ platform.
