Practice
Models that reach production, not a notebook.
Pipelines that hold up under real load, and the machine learning and LLM work that sits on top of them. The plumbing compresses, so the engagement is spent on the decisions the data drives.
- Airflow
- Spark
- dbt
- Snowflake
- BigQuery
- Kafka
- MLflow
- LLM integration
What we hear
The conversations that bring people here.
Every dashboard in the company shows a slightly different number.
Head of Analytics
The model works. It has been about to ship for seven months.
Data Science Lead
Half of two teams’ week goes on moving files between systems.
COO
What we do
Four things, done properly.
Data platform & pipelines
Ingestion, orchestration and storage designed for the volume you actually have, with failures that are visible and recoverable rather than silent.
Analytics engineering
Modelled, tested, documented datasets so that a metric has one definition and the definition lives in version control.
Machine learning in production
Feature pipelines, training, serving and monitoring — the parts between a working model and a model the business depends on.
Document & LLM automation
Extraction, classification, redaction and retrieval built into real workflows, with evaluation in place before anything goes live.
What’s different now
Where AI-assisted delivery actually changes this practice.
Specific to this work, not a paragraph repeated across every page.
The pipeline stops being the project
Most data engagements historically spent the majority of their budget on connectors, schemas and transformation boilerplate. That share drops sharply, and the remaining time goes to the questions you wanted answered.
Tests on the data, not just the code
Data quality checks and documentation were always the right thing and always the first thing cut. They are now cheap enough to be default.
An engineering problem now
Document extraction and retrieval used to need a research phase before anyone could scope the build. For most workflows, it is now a build.
Selected work
What changed, in the client’s terms.
Three recent engagements from this practice.
The manual redaction step, removed
Sensitive customer documents were read and redacted by hand before they could move through the workflow. Throughput was capped by headcount, and errors surfaced downstream where they were expensive. Read the case study→ Review management · Conversational AIChatbots their customers build themselves
The platform wanted to give its existing customers a chatbot, which meant the customers had to be able to build one — not file a request for one. Read the case study→ Healthcare · Synthetic data
Training data without patient data
The client’s data was cluttered and incomplete — a poor basis for training — and it was patient data, so the usual remedies for poor data quality were constrained by privacy. Read the case study→A two-week paid pilot
Fixed price, on a real piece of your backlog. You keep the output whether or not you continue.