Services
Practical expertise from first analysis to a running system.
We offer hands-on help across data science, machine learning and applied AI. Every project starts with your business problem, and the technology comes second.
Each piece of work has clear deliverables and ends with code, documentation and a walkthrough your team can own.
01
Data Science & Analytics
Clear answers from your data, and models you can trust and explain.
Discuss your projectProblems we solve
- You have a lot of data but aren't sure what it's telling you.
- Forecasts are based on spreadsheets and guesswork.
- You don't know which customers are likely to leave, buy again or need attention.
- Reports describe what happened but don't explain why.
- You want to test whether predictive modelling is worth the investment.
What we deliver
- Exploratory analysis with a clear written summary of findings
- Statistical analysis and experiment (A/B test) evaluation
- Forecasting models for demand, sales, usage or other key metrics
- Customer and product analytics, such as segmentation, churn and retention analysis
- Predictive models with documented assumptions and limitations
- Reproducible notebooks or scripts, so the work can be rerun and extended
Example use cases
- Forecasting weekly demand to improve inventory planning
- Finding which customer behaviours come before churn
- Segmenting users to support more targeted product or marketing decisions
- Evaluating whether a product change had a measurable effect
When this is a good fit
- You have historical data and a specific question you need answered.
- You need analysis that leads to a decision, not just a dashboard.
- You want to check whether ML is worth pursuing before investing in engineering.
Typical technologies
- Python
- SQL
- pandas
- scikit-learn
- statsmodels
- Prophet
- Jupyter
02
Machine Learning Engineering & MLOps
Get ML models out of notebooks and into reliable, maintainable systems.
Discuss your projectBuilding a model vs. taking it to production
Building a model that performs well on a test set is one step. Getting it to serve predictions reliably in your product, retrain on new data and stay accurate over time is a separate engineering effort, and it's where many ML projects stall. We help with both steps.
Problems we solve
- A model works in a notebook but nobody knows how to deploy it.
- Training is manual, hard to reproduce or depends on one person.
- There's no record of which model version is in production or how it was trained.
- Nobody notices when model performance degrades.
- Your engineering team needs a clean API to call, not a data science notebook.
What we deliver
- ML model development and evaluation
- Reproducible training pipelines
- Model deployment as an inference API or batch job
- Experiment and model tracking
- Containerised, documented services ready to hand over to your engineering team
- Basic monitoring for model inputs, outputs and performance, where appropriate
Example use cases
- Turning an existing prototype model into an API your product can call
- Setting up a retraining pipeline so models stay current
- Adding experiment tracking so model results are comparable and reproducible
- Reviewing and improving an existing ML codebase before scaling it
When this is a good fit
- You already have a model, or a clear ML use case, and need it to work reliably.
- Your product or engineering team needs to integrate ML predictions.
- You want ML work to be maintainable after the original author has moved on.
Typical technologies
- Python
- scikit-learn
- PyTorch
- XGBoost
- LightGBM
- MLflow
- FastAPI
- Docker
03
Applied AI / LLM Solutions
Practical AI applications that solve a specific problem, built, tested and integrated.
Discuss your projectOur approach
We focus on AI applications with a clear job to do, such as answering questions from your documents, summarising or classifying text, or automating a repetitive step in a workflow. We start small, test against real examples, and build something you can evaluate and improve.
Problems we solve
- Your team spends time searching documents, wikis or tickets for answers.
- You want to add an AI feature to your product but aren't sure where to start.
- An LLM prototype works in demos but gives unreliable answers in practice.
- Repetitive text-heavy tasks take up skilled people's time.
What we deliver
- LLM-powered applications built around a defined use case
- Retrieval-Augmented Generation (RAG) systems over your documents or knowledge base
- AI assistants for internal teams or customer-facing use
- Embedding and vector search pipelines
- Prompt and workflow design, with test sets to measure answer quality
- Integration with your existing systems through APIs
Example use cases
- An internal assistant that answers questions from company policies and documentation
- Question answering over product manuals or support articles
- Classifying and routing incoming support tickets or emails
- Pulling structured information out of unstructured documents
When this is a good fit
- You have a specific, repeatable task or knowledge source in mind.
- You want a working, testable application rather than a strategy document.
- You're willing to start with a focused first version and expand from there.
Typical technologies
- OpenAI API
- Anthropic API
- Open-source LLMs
- LangChain
- LlamaIndex
- pgvector
- FastAPI
How projects work
Defined scope, visible progress, a proper handover.
You bring a defined problem. We agree on scope, deliverables and milestones, then build and hand over a working solution with documentation.
- Step 1
Discovery call
We talk through your goals, your data and your constraints to understand the problem.
- Step 2
Scoping & proposal
We propose an approach with clear deliverables, milestones and a timeline.
- Step 3
Delivery
We build in short iterations and share progress regularly, so there are no surprises.
- Step 4
Handover & support
You receive working code, documentation and a walkthrough, with optional follow-up support.
What we don't do
To keep our work focused, we generally don't take on:
- General website or mobile app development
- IT support or infrastructure management
- Projects without a clear data, ML or AI component
- Staff augmentation or long-term contract placements
FAQ
Common questions
Do you work with clients in other time zones?
Yes. We work remotely and agree on overlapping hours for meetings and communication.
Will you sign an NDA?
Yes. We're happy to sign an NDA before discussing project details.
Who owns the code and models you build?
Ownership of the deliverables is agreed in the contract. Typically, the client owns the work produced for them.
What do you need from us to get started?
A short description of the problem, what data or systems are involved, and any timeline you're working to. If you don't have all of that yet, that's fine.
Can you work alongside our existing team?
Yes. We work closely with your engineers, analysts or product team throughout a project, and hand over everything they need to own the result.
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.