QuantumCore · WaveCore
Real answers on the hardware you already own.
A physics-first prediction engine that reads systems from their governing physics instead of inferring them from mountains of data.
We don’t add more power. We help the power you have stay in step.
Data-first models learn patterns and need ever more compute. Physics-first works from governing physics, and stays strong where novel problems live and labeled history is scarce. WaveCore is built for that side.
Compute is hitting cost and energy walls.
Built to run where the work actually happens.
WaveCore runs on commodity and edge hardware against telemetry you already collect. No GPU farm, no rip-and-replace. Where we instrument a facility, every deployment is engineered passive and read-only, least-privilege, and auditable by design.
Many domains. Shared principles.
Near-term lines with public proof (materials, batteries) fund the higher-ceiling work. Domains share a physics-first approach — not five disconnected toolchains.
Battery & energy-storage diagnostics
Non-destructive state-of-health (SOH), remaining-useful-life (RUL), and early-warning for cells and banks — no teardown or destructive discharge required.
- On public NASA PCoE lithium-ion data, it flagged end-of-life 87–114 cycles ahead of variance baselines on the cells tested.
- Runs on modest on-site hardware against the battery-management telemetry you already collect.
- Built for data-center UPS banks, grid-scale storage (BESS), and second-life grading.
Honest scope: an early, interpretable analytical signal that informs maintenance, grading, and replacement decisions. It does not replace certified safety testing, and broader-chemistry validation is a roadmap milestone.
Materials screening
Property prediction and ranked candidate lists for same-day triage, before expensive simulation or lab work.
- On the public SuperCon benchmark, predicts critical temperature at R² 0.876 — competitive accuracy without first-principles simulation in the loop.
- ~0.02 s per prediction on a single commodity board: over a million candidates a day.
- Same-day turnaround on hardware you already own, fast enough to fit a research cycle rather than an HPC queue.
Honest scope: competitive, not state-of-the-art. A triage tool, not a replacement for first-principles accuracy.
Energy systems & power quality
A built stack for microgrid and facility energy: supervisory control emphasizing stability and harmonic behavior, paired with an observability layer for telemetry, operator-facing explanations, and full audit logging.
- Shadow / read-only supervisory mode before any control-authority transfer.
- Standards-aligned to IEEE 519 and IPMVP as buyer language.
- Edge-deployable and governance-first, with immutable audit trails.
Honest scope: designed for facility pilots; passive and read-only by design.
Datacenter & AI-compute efficiency
Physics-first methods that target compute cost and energy on the infrastructure customers already run — a complement to data-first AI, not a rip-and-replace.
- Runs on commodity and edge hardware. No GPU farm required.
- Complements data-first AI rather than replacing it.
- Pairs with battery/UPS diagnostics on the same facility footprint.
Honest scope: quantified efficiency figures are being verified against ground truth before we publish them as headline claims.
Drug-discovery support
A physics-first complement to data-first structure tools, aimed at novel-regime and deployment-constrained workflows.
- Built for settings where labeled history is scarce and interpretability matters.
- Fits on-prem / air-gapped environments where cloud-only tools don’t.
- Shares the same physics-first approach as our materials and energy work.
Honest scope: early option value with benchmarks on the roadmap. Not a current revenue claim, and not a replacement for in-distribution structure tools.
Shared principles, many domains
Each domain is a vertical application of the same physics-first approach. Progress in one informs the rest — one validation story instead of five disconnected ones.
- Materials, batteries, energy systems, and related verticals share the same posture.
- Near-term, publicly validated lines fund the higher-ceiling work.
- Inspectable results in the open; mechanisms stay IP-protected.
What we share vs. protect is deliberate: results in public, methods under NDA.
Where we stand
Confident where we have public proof; explicit about what is still ahead.
| Signal | Current view | Why it matters |
|---|---|---|
| Engine | Built & validated | Public SuperCon and NASA PCoE results today — not only a research thesis. |
| Hardware | Commodity / edge | Runs where conventional methods need clusters. Cost per prediction stays low. |
| Accuracy | Competitive | We say competitive, not state-of-the-art — on purpose, against defensible public baselines. |
| IP & methods | Protected | IP-protected and trade-secret; full methodology disclosed only under NDA. |