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Data science · Machine learning · Applied AI

Data, ML and AI work that makes it into production.

We help startups, SMBs and product teams turn data into working models, pipelines and AI applications, scoped as clear projects and handed over ready to run.

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Tools and platforms we work with

  • Python
  • SQL
  • PyTorch
  • scikit-learn
  • MLflow
  • FastAPI
  • Docker
  • AWS
  • GCP
  • Databricks

01What we do

Three ways we help teams get more out of their data.

  • 01

    Data Science & Analytics

    Turn your data into clear answers and reliable forecasts.

    We help you understand what your data is telling you, from finding patterns in customer behaviour to forecasting demand. You get analysis and models you can explain to your team and act on.

    Learn more about Data Science & Analytics

  • 02

    Machine Learning Engineering & MLOps

    Take models out of notebooks and into your product.

    A model that works in a notebook isn't a working system. We build the pipelines, APIs and tracking that turn ML models into something your product and team can run, update and monitor.

    Learn more about ML Engineering & MLOps

  • 03

    Applied AI / LLM Solutions

    Practical AI applications built on your data and workflows.

    We build focused LLM applications, such as assistants that answer questions from your documents or workflows that automate repetitive text tasks. The aim is a useful, testable tool, not an experiment.

    Learn more about Applied AI / LLM Solutions

02Who we work with

Built for growing teams with a clear problem.

Startups
You have data and an idea, but no data or ML team yet. We help you test whether an ML or AI feature is viable and build a first version that can grow with you.
Small and medium-sized businesses
You collect data every day but rarely use it to make decisions. We help you set up practical analysis, forecasting and reporting that answer real business questions.
Product companies
You want to add ML or AI features to your product without stalling your roadmap. We design, build and integrate those features alongside your engineering team.
Technology teams
Your team is capable but stretched, or missing a specific skill. We take on a well-defined data, ML or AI piece of work and hand it back documented, so your team can own it from there.

03How engagements work

A clear path from first call to handover.

  1. Step 1

    Discovery call

    We talk through your goals, your data and your constraints to understand the problem.

  2. Step 2

    Scoping & proposal

    We propose an approach with clear deliverables, milestones and a timeline.

  3. Step 3

    Delivery

    We build in short iterations and share progress regularly, so there are no surprises.

  4. Step 4

    Handover & support

    You receive working code, documentation and a walkthrough, with optional follow-up support.

04Selected work

A few examples of the problems we've helped solve.

Illustrative example

Logistics · Mid-sized business

A documentation assistant for an operations team

Replacing an unreliable chatbot prototype with a retrieval-augmented assistant that cites its sources and is measured against a test set written by the people who use it.

87%answers rated correct (from 52%)

Illustrative example

Food distribution · SMB

Weekly demand forecasting for a regional food distributor

Replacing spreadsheet averages with SKU-level forecasts that feed straight into the ordering process, cutting stock-outs on fast movers and write-offs on perishables.

31%lower forecast error vs. baseline

Illustrative example

B2B SaaS · Product company

Taking a churn model from notebook to production API

Turning a promising but unmaintainable churn model into a tested, monitored service that the product team can call and anyone can retrain.

<50 msp95 prediction latency

All case studies

Have a data, ML or AI problem worth solving?

Tell us what you're working on. We'll help you work out whether and how we can help.

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