AI & data engineering
Data platforms and models with an evaluation harness, so you can tell whether they work.
Overview
The hard part of applied machine learning is rarely the model. It is the data pipeline underneath it, the evaluation set that tells you whether the thing improved, and the plan for when it degrades.
We build all three, and we will tell you when a problem does not need a model.
What it includes
- 01
Data foundation
Ingestion, lineage and quality checks first. Models built on unmonitored pipelines fail silently.
- 02
Evaluation before deployment
A held-out set and a metric tied to a business outcome, agreed before any model ships.
- 03
Retrieval and language systems
Where language models fit, we build them with grounded retrieval, citation and a measured refusal rate.
- 04
Monitoring for drift
Input distribution and output quality tracked in production with alerting and a documented rollback.
What you walk away with
- Instrumented data pipeline
- Evaluation harness and baseline
- Deployed model or retrieval service
- Drift monitoring and rollback plan
Typically
- Python
- PyTorch
- dbt
- Airflow
- Spark
- pgvector
- MLflow
Tell us whatyou are building.
A short, paid discovery engagement ends with a plan and an estimate you are free to take elsewhere.
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