The plumbing under everything. Without it, no model matters.

ETL pipelines, NLP processing, classical and deep machine learning, and rigorous evaluation harnesses. The systems that move, clean, label, train, and serve. The boring infrastructure that makes the impressive demos actually work in production.

Scope a project → See case studies

What we build

The menu.

Specific things we deliver under this service. Most projects combine three or four of these into one system.

/01

ETL and pipeline build

Production grade data pipelines with Airflow, Dagster, or Prefect. Incremental, idempotent, monitored, with backfill safety.

/02

Analytics engineering

dbt models, semantic layer, data quality testing, and clean documented warehouses your team can actually query.

/03

NLP processing

Custom NLP pipelines for entity extraction, classification, topic modeling, sentiment, and structured output.

/04

Classical ML systems

Forecasting, churn prediction, credit scoring, recommendation systems. Sometimes XGBoost beats a transformer.

/05

Deep learning training

Custom model training on PyTorch and TensorFlow. Fine tuning, distillation, quantization for deployment efficiency.

/06

MLOps and evaluation

MLflow, Weights and Biases, model registries, A/B testing harnesses, drift detection, automated retraining.

Where it fits

Common use cases.

If any of these match your current operations, this service is probably the right entry point.

Data warehouse modernizationCustomer 360 buildsForecasting and planningChurn and retention modelsRecommendation systemsDocument AI pipelinesFinancial modelingRisk scoringDemand predictionMarketing attribution

Spec

Technical parameters.

Orchestration	Airflow, Dagster, Prefect, Mage, custom
Transforms	dbt, Spark, Pandas, Polars, DuckDB
Warehouses	Snowflake, BigQuery, Redshift, Databricks, Postgres
ML frameworks	PyTorch, TensorFlow, scikit learn, XGBoost, LightGBM
Serving	Triton, vLLM, FastAPI, SageMaker, custom
Tracking	MLflow, Weights and Biases, Comet
Monitoring	Evidently, Great Expectations, custom
Pricing	$15k to $60k per pipeline or model

FAQ

Common questions.

Do we need data engineering before doing AI? +

Almost always. The most common failure mode in AI projects is bad data. We often start with a data audit before scoping the AI work itself, to avoid building on sand.

Will you work with our existing data team? +

Yes. We embed alongside internal teams routinely. We follow your conventions, document everything, and leave behind code your team can maintain after we are gone.

How do you measure ML model quality? +

Task specific metrics, plus rigorous holdouts and time aware validation for time series. For deployed models, drift detection and statistical performance monitoring run continuously.

Do you do real time machine learning? +

Yes. We have deployed sub 50ms inference systems on edge devices, real time feature stores with Tecton and Feast patterns, and streaming pipelines on Kafka and Pub Sub.

What about classical ML versus LLMs? +

We pick based on the problem, not the trend. Tabular forecasting still favors gradient boosting. Sequence labeling still favors fine tuned encoders. LLMs are not always the right answer.