logo of Infracta™

Engineering Research-Grade Quantitative & Simulation Systems

We design and operate high-performance quantitative infrastructure supporting portfolio analytics, stress testing, and simulation-driven decision workflows. Systems are built for reproducibility, deterministic evaluation, and capital-sensitive reliability under live production conditions.

Core Operating Principles

  • Deterministic Quantitative Workflows
  • Versioned datasets • Backtesting frameworks • Artifact lineage • Controlled model promotion • Reproducible simulation environments
  • Performance-Aware Modeling Systems
  • Distributed simulation • Time-series ingestion & return computation • Parallel compute orchestration • Low-latency portfolio evaluation • Throughput optimization under concurrent load
  • Operational Discipline for Capital-Sensitive Systems
  • Observability-first architecture • Failure-domain isolation • SLA/SLO-backed reliability • State consistency guarantees • Audit-ready access controls (when required)
Capabilities

Deterministic quantitative workflows, built for reproducibility

Versioned datasets, backtesting frameworks, artifact lineage, and controlled model promotion for capital-sensitive systems.

Explore capabilities
Proven at scale

Over $100M in capital exposure, handled with discipline

Platform case studies across production quantitative systems: distributed pricing, risk analytics, and deterministic validation under live load.

View case studies
Platform

State-consistent execution under production load

Low-latency portfolio evaluation, parallel compute orchestration, and SLA/SLO-backed reliability engineered for live conditions.

See the platform

Capabilities

Quantitative Research Systems

Reproducible research infrastructure for portfolio stress testing, backtesting, and scenario evaluation at scale.

RESEARCH DATARESEARCH COREEVALUATIONRESULTSHISTORICAL · REPLAYMARKET · LIVEDETERMINISTICMarket datasetsmulti-assetFactor shocksstress setLive pricesas-ofScenario setkey-rateResearch engineREPRODUCIBLEsimulate()backtest()aggregate()evaluate()deterministic state replayparallel scenario evaluationreplay(asof) -> metricsreproducible · seededSCENARIO GRIDfactor × key-rateCOVERAGEfactor + key-rateunder concurrent loadreplay_parity✓ MATCHreproducibleRESEARCH RUN LEDGER · reproducible, parallel, at scaletsrunmodeoutcomestatus09:12backtest eq-usreplayreproducible✓09:18factor shockparallelstable✓09:24key-rate scanparallelwithin band✓09:31exposure aggconcurrentDV01/dur/cvx✓09:37ts ingestionstreamingreturns computed✓historical / replaymarket / livematchwatchDistributed simulationstress testingDeterministic backtestsstate replayHigh-throughput ingeststime-seriesScenario pipelinesparallelExposure aggregationDV01/dur/cvxReproducibleseeded
Distributed simulation frameworks for portfolio stress testing and factor shock analysis
Deterministic backtesting infrastructure with reproducible state replay
High-throughput time-series ingestion & return computation across market datasets
Scenario analysis pipelines for parallel and key rate shock evaluation
Portfolio-scale exposure aggregation (DV01, duration, convexity) under concurrent load

Quantitative Research & Modeling Infrastructure

Governed modeling infrastructure: tracked experiments, versioned artifacts, and controlled promotion of pricing and risk models with live drift monitoring.

ARTIFACTVALIDATIONPROMOTIONMONITORINGRESEARCH · STAGEDLIVE · MONITOREDGOVERNEDExperiment runstrackedModel artifactsversionedRisk configgovernedPricing serviceliveModel infra engineGATED · VERSIONEDtrack()version()promote()monitor()reproducibility controlsconfig governancepromote(model) -> env + trailmonitored · drift-awarePROMOTION LANEresearchstaging✓production✓GOVERNANCEversioned artifactsconfig governancedrift_watch✓ STABLEdeviation monitoredMODEL PROMOTION LEDGER · tracked, versioned, monitoredtsmodelgateenvstatus10:02px-eq v3.1experimentstaging✓10:08px-eq v3.1validationstaging✓10:15px-eq v3.1promotionprod✓10:20risk-fx v2driftwatch⚠ hold10:28px-eq v3.1monitorprod✓research / stagedlive / monitoredpassedwatchExperiment trackingreproducibleVersioned artifactsgovernedCI/CD valuationrisk servicesControlled promotionacross envsDeviation monitoringsensitivity driftRollbackprior version
Deterministic experiment tracking & reproducibility controls
Versioned model artifacts & pricing configuration governance
CI/CD for valuation and risk computation services
Controlled promotion of pricing engines & risk models across environments
Real-time monitoring of pricing deviations & sensitivity drift

High-Performance Distributed Systems

Streaming and batch execution on a state-consistent, latency-profiled fabric with compute segmentation for capital-sensitive modeling workloads.

STREAMCOMPUTE FABRICSEGMENTATIONRESULTSSTREAM · LIVEBATCH · REPLAYDETERMINISTICMarket streamorderedPortfolio dataliveBatch jobsshardedCheckpointsreplay stateExecution fabricPARALLEL · ORDEREDingest()orchestrate()segment()replay()state-consistent executionlatency-profiled · regressionmap(partition) -> reduce(ordered)deterministic replayCOMPUTE SEGMENTScapital-sensitiveLATENCYprofiled · regressionsegmented loadreplay_consistent✓ ORDEREDstate-consistentEXECUTION LEDGER · streaming + batch, segmented, replayabletspartitionworkerstagelatencystatus09:30p-01w-04ingest7ms✓09:30p-02w-11orchestrate9ms✓09:30p-03w-07segment6ms✓09:31p-04w-22replay8ms✓09:31p-05w-15observe5ms✓live streambatch / replayhealthywatchStreaming + batchingestionParallel orchestrationsimulationCompute segmentationcapital-sensitiveDeterministic replaystate-consistentObservabilitylatency profilingRegression validationpricing
Streaming + batch ingestion for market and portfolio data
Parallel workload orchestration for simulation and stress testing
Compute segmentation for capital-sensitive modeling workloads
Deterministic replay & state-consistent execution
Observability, latency profiling & regression validation for pricing pipelines

50

M+

Capital Exposure Impacted Annually
via portfolio-scale pricing, risk analytics, and deterministic validation controls

≤4s

End-to-End Portfolio Evaluation Latency
across distributed pricing and exposure pipelines under production load

70%

Reduction in Audit & Traceability Overhead
via deterministic artifact lineage and model governance controls

99.9%+

SLA-Backed Reliability
across capital-sensitive production systems

60%

Latency Reduction
across distributed compute and time-series processing workflows

37%


Compute Efficiency Gain
through workload-aware scaling and portfolio-aligned resource segmentation

“Infracta™ brings a rare combination of quantitative rigor and production engineering discipline. They architected portfolio-scale pricing and exposure systems that reduced evaluation latency while strengthening deterministic validation across our trading workflows. Their systems thinking materially improved the reliability of capital-sensitive processes.”

– Head of Systematic Strategies

“Infracta’s™ approach to risk and modeling infrastructure was precise and methodical. They implemented deterministic controls and artifact traceability that significantly improved our audit posture without slowing down quantitative workflows. The result was a more resilient and transparent production environment.”

– Quantitative Research Lead

“Infracta™ engineered high-performance distributed systems that supported large-scale portfolio analytics under production load. They reduced latency, improved compute efficiency, and maintained strict availability targets, all while ensuring correctness in capital-impact calculations.”

– Portfolio Manager

“We inherited a multi-tenant cloud architecture problem most teams spend quarters untangling. I delivered a working solution in a fraction of that time, structured cleanly enough that the client’s own quant team carried it forward as the path ahead.”

– Zahava Levy, Founder, Infracta™

“On that engagement I built the production trade-entry screen, the forward-stage indicative risk checks behind it, and the API surface tightening it together, moving from a blank page to a running system without a scaffolding team behind me.”

– Zahava Levy, Founder, Infracta™

“The work holds up because it is deterministic and auditable end to end. That is the standard I bring to every system Infracta™ builds.”

– Zahava Levy, Founder, Infracta™

Engineering Research-Grade Quantitative Systems

At Infracta™, we design and operate high-performance quantitative infrastructure for capital-constrained and performance-sensitive environments.

INPUTSQUANTITATIVE COREEVALUATIONOUTCOMESAS-OF · VERSIONEDMARKET · LIVEDETERMINISTICMarket & portfoliomulti-assetVersioned featuresdeterministicModels & configsgovernedLive pricesas-ofQuantitative systemsCORRECT · REPRODUCIBLEprice()risk()simulate()promote()correctness · reproducibilitylatency discipline, non-negotiableevaluate(asof) -> pricing, riskdeterministic · capital-awareDISTRIBUTED FABRICcapital-sensitiveDISCIPLINEcorrectness firstlatency disciplinereproducible✓ PINNEDseed · precisionPERFORMANCE SNAPSHOToutcomemeasurestatusSLA-backed availability99.99%✓pricing & evaluation latency30-60% reduction✓portfolio evaluation latency≤ 5s under load✓validation & promotion cycles50% faster✓compute efficiency37% gain✓as-of / versionedmarket / livewithin targetwatchCorrectnessnon-negotiableReproducibilityseed-pinnedLatency disciplinebudgetedCapital-awarecost + riskDistributedat scaleObservabletraced · telemetry

What we build

Our systems support portfolio-scale pricing, risk analytics, stress testing, and distributed simulation workflows where correctness, reproducibility, and latency discipline are non-negotiable.

Capital impact

$50M+ in annual capital exposure impacted through deterministic modeling across capital-sensitive production environments.

Distributed streaming & batch time-series architectures
Deterministic data versioning & feature computation frameworks
Backtesting and portfolio-scale scenario evaluation systems
Controlled artifact lineage & pricing model promotion
Simulation harnesses with regression validation controls
Observability-first quantitative systems (tracing, telemetry, latency profiling)
Secure deployment in capital-sensitive environments

Let’s build quantitative infrastructure that performs under production load, preserves deterministic correctness, and supports capital-sensitive modeling and simulation workflows.