Replay market systems exactly. Prove they did not get slower.
BQL Engine 2.0 turns deterministic replay, latency gates, runtime protection decisions, canonical symbol IDs, and evidence bundles into a CI-enforced proof contract for market infrastructure teams.
One PDF: deterministic replay, latency gates, canonical digest, evidence bundle.
Benchmarks validate controlled replay and regression-detection behavior, not production exchange throughput without environment-specific validation.
Deterministic synthetic replay, digest verification, latency budget enforcement, and evidence packaging using controlled workloads.
Production-shaped adapters, deployment controls, dashboards, compliance evidence retention, and support for real ITCH/FIX market-data workflows.
Dynamic proof, not dynamic decoration.
Two artifacts make the contract legible at a glance: the pipeline that turns raw market input into sealed evidence, and the benchmark that shows what changes when that pipeline is in the loop.
Each stage emits machine-verifiable artifacts. A run is only valid if every stage produces a matching digest and every gate decision is recorded.
Toggle to compare a naive replay against the BQL Tier C proof pipeline on the same input stream. Numbers reflect the public harness; enterprise pilots produce environment-specific values.
Pain → use case → proof → pilot → brief.
Five steps from problem to signed pilot. Every step links directly to the artifact or section that answers the question it raises.
- 01Pain
Performance claims are not machine-verifiable.
Benchmarks live in scripts. Replay state lives in someone's head. Latency budgets are slides, not gates. Diligence stalls.
See the failure mode - 02Use case
Match the system to a real workload.
Pick the module that fits: deterministic replay, latency contracts, canonical symbol IDs, or evidence packaging — each maps to a concrete team failure.
Browse modules - 03Proof artifact
Inspect the evidence the harness produces.
Hashes, benchmark artifacts, gate decisions, manifests. Open the console, replay the run, confirm the digest.
Open the evidence console - 04Pilot
Run a 4–6 week enterprise pilot.
Baseline proof, then enforce. We bring deterministic replay, benchmark gates, and evidence packaging to your representative market-system workload.
Request Pilot Brief - 05Executive Proof Brief
One PDF for the technical decision.
Replay model, latency contract, digest scheme, evidence bundle. Forwardable internally to a CTO, quant lead, or compliance reviewer.
Request the brief
Built for the next generation of AI-supervised market infrastructure.
As market systems become more automated, more distributed, and increasingly AI-supervised, performance claims need to become machine-verifiable. BQL Engine 2.0 is designed around that shift: every replay, latency budget, gate decision, digest, and artifact becomes part of an evidence trail that can be inspected, diffed, retained, and eventually integrated into intelligent operational workflows.
Machine-verifiable replay
Deterministic inputs and outputs create a stable foundation for automated review.
AI-ready evidence trails
Structured artifacts make benchmark and incident data easier to analyze.
CI-enforced performance contracts
Regressions fail before they become production ambiguity.
Audit-grade operational memory
Each run produces artifacts that future reviewers can inspect.
One deterministic contract for replay, latency, gates and evidence.
Low-latency teams do not fail because they lack benchmark scripts. They fail because benchmark results, replay state, runtime protection decisions, and evidence artifacts usually live in separate worlds.
BQL Engine 2.0 brings those worlds into one deterministic contract: replay the same workload, preserve the same gate decisions, verify the same digest, and package the evidence every time.
Latency claims without deterministic replay are not engineering evidence. They are anecdotes.
Recognize the problem. Then prove it.
One proof model: same input, same gates, same digest, reviewable evidence. Five places teams put it to work.
Releases get slower and nobody catches it before production.
p50 / p95 / p99 / p999 budgets, build pass/fail, benchmark report.
Market-system anomalies need reconstruction, not screenshots.
Same input + same gate decisions → same digest + evidence.
Aggregate verification changes with symbol order or shard count.
Canonical symbol IDs and aggregate digest verification.
Performance evidence must be reproducible, retained, reviewable.
benchmark JSON, metrics, hashes, manifests, provenance bundle.
Team needs validation against a representative replay workload.
replay harness, latency profile, CI gate, dashboard, readout.
One proof model. Five ways teams close the gap between “we think it’s fast” and “we can prove it.”
Forward internally to a CTO, quant lead, or compliance reviewer — get the Executive Proof Brief.
The proof contract, decomposed.
Four product modules. One deterministic contract.
Deterministic replay
Replay the same workload and get the same state, every time.
- Controlled event streams
- Golden-state digest checks
- Canonical symbol aggregation
- Repeatable fixture generation
Latency contracts
Turn latency budgets into CI gates.
- p50 / p95 / p99 / p99.9 tracking
- sample-validity flags
- regression budgets
- build failure on budget breach
Runtime protection logic
Make gate decisions replayable.
- DEG / SCM state machine
- smallest-breaker-first escalation
- journaled protection decisions
- deterministic recovery criteria
Evidence packaging
Package the proof trail for review.
- bench.jsonl
- metrics.prom
- HTML report
- SHA-256 manifests
- system and compiler metadata
The deterministic proof path is explicit.
Binary capture
ITCH feeds and gen_synth workloads enter as controlled event streams.
Replay harness
C++20 scheduler applies events through the same deterministic path.
Canonical IDs
Symbol IDs normalize ordering and shard-count behavior.
DEG / SCM
Gate decisions are journaled so protection actions replay exactly.
Evidence bundle
Hashes, benchmark artifacts, metrics, reports, and manifests package the proof trail.
The numbers that change a release decision.
| Tier | Scope | Current | Throughput | Status |
|---|---|---|---|---|
| Tier A | Match-only: parse + book update | ~510ms / 1M events | 1.96M events/sec | Baseline |
| Tier B | In-process end-to-end | ~813ms / 1M events | 1.23M events/sec | Separated |
| Tier C | Full proof pipeline w/ 128MB journal | ~836ms / 1M events | 1.20M events/sec | Evidence pipeline |
Benchmark context guard. Benchmarks validate deterministic replay and regression-detection behavior under controlled workloads. They are not a claim of production exchange throughput without environment-specific validation.
Evidence you can inspect, diff, and retain.
- bench.jsonl
- metrics.prom
- report/index.html
- sha256 manifests
- benchmark CSV / log / summary
- compiler metadata
- system metadata
- canonical ID evidence docs
Blanc Quant Systems is seeking external replication by low-latency C++ engineers, quant infrastructure reviewers, and reproducibility reviewers.
Need something to forward internally?
Request the BQL Engine 2.0 Executive Proof Brief: a one-page summary for CTOs, advisors, investors, and enterprise buyers.
Enterprise BQL 2.0 Pilot.
A 4–6 week proof engagement for teams that need deterministic replay, benchmark gates, and evidence packaging on representative market-system workloads.
Workload fit
review workload, architecture, latency risks, and evidence goals
Baseline proof
establish deterministic replay, digest checks, and latency profile
CI contract
configure budget gates, artifact packaging, and regression reports
Executive readout
deliver pilot findings, roadmap, deployment recommendation, and evidence plan
- Real ITCH/FIX ingest
- Prometheus/Grafana dashboards
- CI/CD templates
- Enterprise auth / SSO
- Reproducible benchmark reports
- Legal/compliance evidence bundles
- Customer pilot path
- Commercial support terms
Built by Jean Blanc.
Blanc Quant Systems is led by Jean Blanc, an award-winning engineer, quantitative systems builder, and infrastructure strategist with 15+ years at the intersection of reliability engineering, asset performance, risk analytics, R&D, and data-driven innovation.
Jean’s career has been built around high-consequence infrastructure: systems where reliability, traceability, performance, and evidence matter. That background shapes the design philosophy behind Blanc Quant Systems.
Jean has built and led systems work across high-consequence infrastructure environments, including reliability analytics, asset performance, R&D programs, regulatory-facing evidence workflows, and deterministic proof systems.
The company is not being built as a generic market-technology project. It is being built from an infrastructure mindset: deterministic workflows, reproducible results, auditable performance, transparent benchmarking, and proof-oriented system design.
In serious systems, performance claims are not enough. The system must be able to prove itself.
- Infrastructure disciplinereliability, traceability, auditability.
- Quant systems directiondeterministic replay, latency evidence, reproducible benchmarks.
- Commercial thesisenterprise proof layer for performance-critical market systems.
reliability, asset performance, risk analytics, R&D, and data-driven innovation
infrastructure where proof and traceability matter
performance claims are not enough; systems must prove themselves
Build market systems that can prove themselves.
BQL Engine 2.0 is for teams that need more than fast claims. It is for teams that need replayable proof, enforceable latency contracts, and evidence that survives technical diligence.