Production data systems, statistical models, and decision software built as one stack.
We take data science past the notebook: ingestion, warehouse semantics, model pipelines, serving interfaces, and operator workflows shipped together into live operations.
Data pipelines shipped
Production ML models
Decision platforms delivered
End-to-end data pipeline
Sources
ERP, CRM, APIs, files
Ingestion
Contracts, validation, CDC
Warehouse
Conformed entities, metrics
Modeling
ML, forecasting, NLP
Decisions
APIs, dashboards, alerts
What We Deliver
Analytics substrate, model layer, and product surface, scoped as one engagement.
Data science projects fail when warehouse logic, model code, and UI behavior evolve independently. We scope cross-functionally so operators, analysts, and executives trust the same system.
Typical engagement time allocation
Percentage of effort across delivery phases
Delivery Phases
Four stages from raw data to governed decisions
Phase 1
Data Platform Engineering
Source mapping, ingestion contracts, warehouse structure, transformation logic, and reporting semantics. Stack: Python, SQL, Airflow, dbt, Snowflake, BigQuery, Supabase.
Phase 2
Applied ML & Forecasting
Training set construction, forecasting, classification, NLP, and vision pipelines with backtesting and model review. Stack: PyTorch, Hugging Face, spaCy, OpenCV, FastAPI.
Phase 3
Serving Layer & Decision UX
Inference APIs, operator dashboards, scenario-planning workflows, and decision narratives connecting model output to recommended action. Stack: Next.js, React, TypeScript, Supabase.
Phase 4
MLOps & Governance
Dataset and model versioning, CI gates, drift monitoring, RBAC, audit logging, environment separation, and infrastructure automation. Stack: Docker, Kubernetes, AWS, Terraform.
Phase 1
Data Platform Engineering
Source mapping, ingestion contracts, warehouse structure, transformation logic, and reporting semantics. Stack: Python, SQL, Airflow, dbt, Snowflake, BigQuery, Supabase.
Phase 2
Applied ML & Forecasting
Training set construction, forecasting, classification, NLP, and vision pipelines with backtesting and model review. Stack: PyTorch, Hugging Face, spaCy, OpenCV, FastAPI.
Phase 3
Serving Layer & Decision UX
Inference APIs, operator dashboards, scenario-planning workflows, and decision narratives connecting model output to recommended action. Stack: Next.js, React, TypeScript, Supabase.
Phase 4
MLOps & Governance
Dataset and model versioning, CI gates, drift monitoring, RBAC, audit logging, environment separation, and infrastructure automation. Stack: Docker, Kubernetes, AWS, Terraform.
EquiOps: district decision support built on normalized operational data
Fragmented enrollment, attendance, meal count, labor, and finance signals consolidated into a governed data model with forecasts, prioritized alerts, and scenario-planning workflows for district operators.
Turn raw data into production decisions.
Bring the source systems and decision bottlenecks. We define the architecture, modeling approach, and operating controls to make it work in production.