AI infrastructure for systems where failure is not an option.
Blanc Quant Systems builds deterministic AI, analytics, and validation infrastructure for financial, energy, and industrial organizations. Validate. Monitor. Explain. Trust.
- Deterministic replay, not sampled observation
- Gates enforced at merge, not after release
- Evidence bundles with a re-run command
- Written findings, including what we advise against
- p50 replay latency
- 42 μs
- p99 replay latency
- 187 μs
- Sustained replay
- 1.96 M msg/s
- Digest algorithm
- SHA3-256
Mission-critical systems fail in the gap between what was tested and what was shipped.
Behaviour changes silently
A release alters what the system does, aggregate dashboards look fine, and the divergence surfaces in production.
Tails are unowned
Median performance is tracked and defended. The p99 that actually causes incidents has no budget and no gate.
Evidence is narrative
Risk, audit, and diligence reviewers ask how behaviour was verified and receive a description instead of an artifact.
Three products, one discipline.
Blanc Quant Systems is the company. Each product applies the same method to a different class of mission-critical system.
AI governance & validation
Deterministic validation harnesses, behavioural regression gates, and sealed evidence bundles for systems whose failure is expensive.
- Reproducible evaluations, not one-off observations
- Thresholds enforced in CI, not in a document
- Controls mapped to release-level evidence
Quantitative infrastructure
Deterministic replay of market workloads, canonical state digests, and latency contracts enforced at merge for market-data and order-book systems.
- ITCH workload replayed end-to-end
- SHA3-256 canonical state digest comparison
- p50 / p95 / p99 / p99.9 gates in CI
Energy & manufacturing AI
Forecasting, maintenance, and process models validated before they touch operations, with evidence that satisfies process-change control.
- Telemetry integrity verified before modelling
- Cost-weighted operational gates
- Replayable evaluation over recorded windows
The same six steps, whatever the system.
Workload, configuration, environment, and versions fixed so a run is a measurement.
The system reprocesses the same work under the same conditions, every time.
State is hashed and compared. A mismatch fails the run and reports where it diverged.
Latency and behaviour budgets enforced in CI so breaching changes never merge.
Metrics, manifest, environment metadata, and gate results bundled and hashed together.
A forwardable artifact with a re-run command, not a claim to be taken on trust.
Built by engineers who have operated mission-critical systems.
The credibility is operational, not theoretical: regulated utility environments, the standards bodies that govern them, and low-latency market infrastructure.
Reliability analytics adopted by 300+ operational users inside a regulated utility.
- Problem
- Asset and reliability decisions were being made from disconnected historian, SCADA, and maintenance records that no reviewer could reproduce.
- What we built
- Telemetry integrity checks ahead of modelling, asset-performance analytics, and reporting that survives process-change control and audit review.
- Outcome
- Adopted as the operational reference for reliability decisions by 300+ users across engineering, maintenance, and planning functions.
Utility & energy analytics
Reliability and asset-performance analytics operated inside regulated utility environments, supporting 300+ operational users.
Standards & safety engineering
Work grounded in the standards frameworks that govern mission-critical electrical and industrial systems.
Quantitative infrastructure
Deterministic replay, canonical state digests, and latency validation for market-data and order-book systems.
One scoped assessment first. Everything else follows the evidence.
A scoped review of one system: failure surfaces, what is measurable today, evidence gaps.
A written artifact your risk, audit, and diligence reviewers can read and challenge.
Harness, gates, and evidence bundles implemented against your workload and CI.
Ongoing gating, drift detection, and sealed evidence as the system keeps shipping.
Teams that have to answer for a system's behaviour.
Engagements start with a scoped assessment of one system. If we cannot measure something usefully, we say so before any work is sold.
- Utilities & Energy Operators
Reliability and asset analytics you can defend to regulators, operations, and audit.
- Quantitative Trading Firms
Deterministic replay and latency gating that catch regressions before production.
- Industrial Manufacturers
Models validated against physical process data before they touch plant decisions.
- Private Equity Operating Teams
Independent verification of technical and AI claims ahead of capital commitment.
Built by an engineer who ships the proof with the claim.
Jean Max Blanc — engineer and AI systems architect. Senior quantitative engineering inside regulated utility operations (Exelon / PECO), industrial R&D (ABB / Thomas & Betts), electrical standards work across NFPA, IEEE, NEMA, and UL, and C++20 deterministic-replay systems engineering.
He founded Blanc Quant Systems to close the gap between what systems are said to do and what can be demonstrated. The work is deliberately verifiable: a public harness, published environment metadata, reproduction commands, and evidence bundles a reviewer can execute independently.
Working with us at company level.
Design partners, channel partners, investors, and co-founder conversations are handled on the company page so product pages stay focused on engineering buyers.
Pick one system. We will tell you what can be proven about it.
An assessment is a scoped, written evaluation: failure surfaces, what is measurable today, the evidence gaps, and a recommended validation path. No sensitive detail required before an NDA is in place.