From prototype models to production ML.
Common roadblocks we remove
- Training/inference mismatch and brittle feature logic
- Data leakage and label shift that inflate backtests
- Slow experiments with no tracking or reproducibility
- Models that drift silently with no monitoring or rollback
- Inference latencies that miss the market
Outcomes you can expect
- Versioned feature store with strict leakage guards
- Automated training pipelines with experiment tracking and lineage
- Real time/near real time inference services with predictable latency
- Offline/online evaluation that matches execution policies
- Model governance: approvals, model cards, alerts, and rollback plans
What We Build
Feature Store & Data Contracts
Deterministic features for research and live use, with schema/version controls and parity tests.
Training Pipelines
Distributed training, HPO, cross validation, and artifact tracking (datasets, models, metrics, seeds).
Model Serving & Inference
Low latency APIs, streaming consumers, and batch schedulers with autoscaling and resource guards.
Evaluation & Monitoring
Offline backtests, A/B & shadow deployments, drift/shift detection, calibration, and SLA/latency dashboards.
Model Risk & Governance
Approvals, change management, explainability hooks (feature attributions), and incident runbooks.
Integrations & Tooling
Plug ins for execution, risk, and research stacks; clean JSON/CSV contracts for internal front ends.
Game-changing solutions
tailored to your unique needs
Artifacts You Keep:
- Feature Store Schemas
- Pipelines
- Trained Artifacts
- Model Cards
- Runbooks
- Dashboards
Discovery & ML Readiness
Inventory data, features, models, and constraints. Deliver an ML architecture plan.
Pipelines & Feature Store
Build reproducible features and training workflows with leakage tests and experiment tracking.
Serving & Evaluation
Deploy inference services, shadow/paper validation, and offline/online parity checks.
Operations & Handover
Monitoring, alerts, rollback, CI/CD, and model governance. Handover or shared ops.
Who We Serve
Empowering quantitative funds and research desks with predictive models, NLP pipelines, and adaptive decision engines.
Asset Classes
Equities
Futures
FX
Options
Digital Assets
Horizons
Intraday
Swing
Multi‑day
Teams
Hedge Funds
Family Offices
Proprietary Traders
Fintech Product Teams
Engagement Models
Our Technology Stack
We utilize a cutting-edge stack of deep learning frameworks and MLOps infrastructure to train, validate, and deploy predictive financial models.
AI Governance & Integrity
Rigorous model validation, bias monitoring, and explainability protocols to ensure regulatory compliance.
Access & controls
RBAC, network allow lists, segregated training/serving.
Confidentiality
Your datasets, features, and model parameters remain private.
Auditability
Model lineage, approvals, and explainability artifacts.
