Applied ML · Deterministic Decision Systems
Research and production, one code path — proven run by run.
Solaris Quantum Intelligence builds a deterministic decision engine for continuous, high-consequence data streams. One code path runs both research and production, and every decision it emits can be reproduced — byte for byte — back to its cause.
What we build
We engineer systems that have to make correct decisions on data as it arrives — continuously, under load, with consequences attached. That means streaming feature computation that never looks ahead, classification of the environment's state before acting, validation that survives scrutiny, and an audit trail that still holds up long after the decision was made.
Our core is a single deterministic decision engine. Research and production run the same build, so what we study is exactly what runs — there is no gap between the model on the whiteboard and the system in the field. Every decision the engine emits carries the full record of what produced it: which inputs, which gates, which parameters, under what conditions. Any past decision can be replayed and verified by cryptographic fingerprint.
We license this software and we conduct research. That is the whole business.
One code path
One engine, one code path
A single deterministic decision engine. Research and production run the same logic — proven equivalent, run by run.
Continuous ingestion
Multi-asset streams evaluated cycle by cycle. State is carried forward, never reconstructed from convenient snapshots. The engine holds until it has every input a decision requires — it acts on facts, not on gaps.
Layered gate hierarchy
Each cycle passes through an ordered sequence of evaluation gates. Every advance is explicit; every advance is recorded. Nothing reaches an outcome without leaving a trace of how it got there.
Auditable intents
The engine emits intents, not opinions. Each intent carries its full causal decomposition — the inputs, gates, parameters, and regime that produced it — so any decision can be explained after the fact, not merely asserted.
Proven parity
The same logic runs from edge devices to datacenter accelerators, and the two are proven equivalent by cryptographic hash — run by run. What is studied is what runs, everywhere it runs.
Method
Quantitative-finance methodology, taken past the usual bar — and made verifiable.
Everyone claims to use machine learning. The distinction here is that ours is provable. Every feature, every model, every tuned parameter is reproducible, provenance-tagged, and validated out-of-sample on the same code that runs in production. The research doesn't live in a notebook that flatters itself and then gets re-implemented for live use — research and production are one build, so a result that survives validation is, by construction, the thing that runs.
Illustrative schematics · Method only, no results depicted
- Engineered feature space
- A large, purpose-built set of streaming features — statistical, cross-sectional, regime-conditional — computed online, without look-ahead, each carrying its own provenance. A feature behaves identically in a study and in the field.
- Regime classification
- The state of the environment is classified before any decision is made, and that classification conditions everything downstream. Models are not asked to generalize across regimes they were never meant to see.
- Bayesian optimization
- Parameters and gate thresholds are tuned by Bayesian optimization over rigorously constructed, multi-criteria objectives — not grid-searched, not hand-fit — and the optimizer is held to out-of-sample behaviour rather than in-sample fit.
- Validation at scale
- Backtesting runs over very large historical datasets — millions of evaluation points across long horizons — as a byte-identical replay of the production engine, not a separate research script. A backtest here is the real system, run over history.
- Out-of-sample discipline
- Walk-forward evaluation with purged and embargoed splits to prevent leakage across time, and combinatorial cross-validation so a finding must hold across many resamplings — not one convenient partition.
- Null-hypothesis testing
- Candidate structure is tested against permutation and null baselines. Beating the noise that any flexible model will find if you let it is a gate, not a footnote.
- Calibrated confidence
- Decisions carry a measured notion of their own reliability. Low-confidence states are gated, not forced. Calibration is verified, not assumed.
Doctrine
- 01
- Byte-identical replay
- Any historical decision is reproducible, and verified by cryptographic fingerprint. Reproducibility is not a feature we added; it is the property the system is built around.
- 02
- Every input accounted for
- Each input is explicit and recorded. The system acts on facts and holds until it has them. A missing input is a reason to wait, never a blank to fill with a guess.
- 03
- Provenance on every parameter
- Each value carries the record of where it came from and under what conditions it was derived. There are no unexplained constants in the decision path.
- 04
- Hash-proven parity
- Edge and datacenter deployments are proven equivalent, run by run. Determinism is enforced, not hoped for.
The problem we solve
Most systems that make automated decisions on live data share a quiet weakness: after the fact, they cannot prove precisely why a given decision was made, or reproduce it exactly.
Parameters drift. Research code and production code diverge. A snapshot gets refilled a slightly different way on replay. When something matters — an audit, a post-mortem, a regulator, a partner's due diligence — "it's complicated" is the honest answer, and that is not good enough.
We built the opposite. Determinism, provenance, and cryptographic replay mean a decision made months ago can be reconstructed today, byte for byte, with a complete account of its cause. That property is what makes a system trustworthy under scrutiny — and it is the hardest thing to retrofit, so we designed for it from the first line.
How we work
Platform licensing
Access to the decision engine and its verification tooling under a software licence.
Applied-research partnerships
Joint work on open methodological problems in streaming inference, regime classification, and validation that survives scrutiny.
Custom system builds
Purpose-built deterministic pipelines for environments where correctness carries real weight and decisions must be explainable after the fact.
Methodology consulting
For teams that want their machine learning to be verifiable as well as performant — leakage controls, replay discipline, and audit-grade provenance.
Scope
We license software and conduct research. We are not an investment adviser, broker-dealer, fund, or financial-services provider; we do not manage capital, solicit investment, or give advice. Our work is the engineering of systems that must be correct under load — streaming computation, regime classification, validation that withstands scrutiny, and an audit trail that holds up after the fact. Where that work touches markets, it touches them as an engineering problem in determinism and provenance, not as a financial service.
Work with us
Licensing, research partnerships, and custom builds. Tell us what has to be correct.