We fit proprietary neural networks to one enterprise's data at a time — custom architectures, sub-millisecond surrogate functions replacing 40-minute simulations, and quantitative strategies strictly validated out-of-sample. Zero weights pooled. 100% IP ownership.
Commodity foundation models optimize for broad web text. When semiconductor yields, power grid reliability, or trading alpha depend on extreme corner cases, general competence collapses.
Foundation models are optimized for mainstream distributions. Your profit margin and physical constraints reside strictly in the tail — the precise operational regime where public models have zero domain knowledge.
A few hundred well-instrumented physics runs or private tick records beat billions of scraped web tokens for your exact task. The bottleneck is not parameter count; it is fitting the exact mathematical surrogate.
Any network can overfit historical training samples. We audit and report held-out, walk-forward performance before any deployment invoice — because that is the only metric that survives contact with production.
Each capability unlocks proprietary intelligence trapped in physical processes, expensive simulators, or trading signals. The engineering differs; the mathematical rigor does not.
A private neural network trained specifically on your data and evaluated against your current benchmark baseline. If it does not empirically beat what you already run, it does not ship.
A finite-element or CFD physics simulation that takes forty minutes becomes a surrogate function that executes in 1.2 milliseconds, with stated mathematical error bounds across your operating domain.
A financial hypothesis becomes a risk-sized, cost-modeled strategy with strict walk-forward validation. We report the out-of-sample number first, because it is the only one that survives reality.
Three interactive engines built on the exact mathematical core we deploy to enterprise clients — an active-learning surrogate, an out-of-sample backtester, and a neural network training live on your CPU.
Ridge regression on a Chebyshev basis, re-solved in closed form on every edit. Replaces expensive CFD/FEM simulations.
Polynomial order. High n memorises; low n blurs.
Shrinks high-order terms first. The dial between memorising and generalising.
The dashed curve is the process that generated your samples.
Click canvas to add sample · drag to move · double-click to delete. Active learning automatically queries the highest uncertainty regime.
Same data, penalty off, degree 0→20. Past the trough the model is fitting noise, and the held-out error says so.
Vol-targeted trend filter with stops and slippage costs. The split is fixed — you cannot tune into unseen data.
Indexed to 1.00× at inception. Everything right of session 673 was never used to choose parameters.
The crossover that defines the trend state.
Position size scales inversely with realised vol.
Slippage plus commission. Most published backtests forget this.
Trade the downtrend, or sit in cash.
A parameter set that only works on one path is not a strategy. Re-roll the market and watch which numbers survive.
A 144 parameter neural net learning in real time directly on your CPU — 100% compute transparency, no pre-baked video frames.
Training and held-out loss, most recent 160 evaluations.
Capacity per layer. Too little and the boundary stays blunt.
Live-editable mid-run. Push it and watch the boundary shatter.
Simulate the financial impact on your cloud bills and team velocity. Replacing compute-heavy solvers with sub-millisecond neural surrogates yields payback periods under 30 days.
Simulate the economic impact of replacing slow PDE/FEM solvers or backtesters with sub-millisecond neural surrogates.
Total hours your team runs CFD, FEA, or backtests daily.
Active cluster size dedicated to model evaluation.
Blended cloud or on-prem hardware depreciation.
Engineers whose velocity is blocked waiting on simulation outputs.
Engineered for mission-critical enterprise environments. We deploy the training stack directly into your VPC or air-gapped cluster. No external data scraping, zero pooling, zero vendor lock-in.
Click across the architecture flow to inspect security boundaries and IP guarantees.
Proprietary sensor logs, CFD meshes & alpha datasets remain behind your firewall.
Automated active learning & error-bounded surrogate architecture search.
Full weights, runtime C++ libraries, and validation harnesses committed to your git.
All training code and surrogate harnesses run within your isolated AWS/GCP/Azure VPC or bare-metal cluster. No weights, tokens, or embeddings are ever pooled, cached, or transferred to external servers.
We ingest a slice of your data inside your isolated perimeter. At the end you receive a written engineering verdict with concrete numbers on whether a model beats your baseline. If no, we halt and you keep the audit report.
Architecture, regularisation, and feature representations are optimized against held-out validation splits. Every design decision is logged with reproducible mathematical justification.
Out-of-sample, walk-forward, and stress-regime tests. We show you exactly where the model fails before you encounter it in production — including automated circuit-breaker protocols.
Weights, runtime C++/CUDA binaries, and retraining runbooks land in your private Git repository, or we operate the high-availability API. Includes continuous drift monitors and named engineering support.
Contractual clarity, IP ownership, and data isolation protocols for technical leaders and investors.
No — contractually and architecturally. Every engagement runs in an isolated environment or directly inside your VPC. Weights and embeddings are never pooled, and nothing derived from your data ever leaves your perimeter. This is Clause 1.1 in our master agreement.
That is the explicit purpose of the two-week paid feasibility study. If the empirical verdict is that your current baseline is already near the theoretical ceiling, we deliver the comprehensive written report and stop billing immediately.
You do, outright. All trained weights, optimization code, runtime C++/WASM binaries, and validation test harnesses are committed directly to your company repository with zero residual IP claims or per-inference runtime taxes.
Smaller than you think, provided it covers key non-linear regimes. Surrogate functions frequently achieve high fidelity with just a few hundred well-chosen simulator runs using active learning. What kills projects is not row count — it is uncurated data.
Yes. Over half our engagements never move a single byte of client data out of the customer perimeter. We bring our containerized model engineering stack directly into your AWS/GCP/Azure VPC or air-gapped on-premise hardware.
Fixed scope, fixed fee per phase. No open-ended hourly billing and no predatory metering on core inference. You should not have to model our invoice while you are running your business.
Every partnership opens with a paid feasibility study. You get a written mathematical verification on whether a fitted model beats what you run today — and if it does not, that is the deliverable, and we stop.
Indicative only — every engagement starts with a paid two-week feasibility study with guaranteed exit clause.
Everything ships to your repo. No lock-in.