Services
Technology solutions built around your business.
Six areas of work. Each one starts from a problem a business actually has, and ends with something engineered to keep running after we hand it over.
AI & Machine Learning
We build machine learning solutions that turn business data into predictions, recommendations and decision support, with the preprocessing, evaluation and serving layers that make them usable beyond a demo.
- Python
- scikit-learn
- PyTorch
- MLflow
- FastAPI
The problem
Most machine learning effort stalls between a promising notebook and something a business can rely on. The model works once, on one dataset, on one machine, and there is no path from there to a system anyone can use.
Typical deliverables
- Data preprocessing and feature engineering pipelines
- Trained models with documented evaluation
- Prediction APIs with defined contracts
- Model deployment and versioning
- Monitoring for drift and failure
- Handover documentation your team can maintain
Who it is for
Teams with data and a decision they want to make faster, more consistently, or at a scale people cannot match by hand.
Data Engineering
We build the ingestion, transformation and storage layer underneath everything else: scheduled, tested, observable and documented, so it keeps working when nobody is watching it.
- Python
- SQL
- PySpark
- Apache Airflow
- PostgreSQL
The problem
Data sits in separate systems, in different shapes, refreshed on different schedules. Every question costs a day of manual exports and reconciliation, and two people answering the same question get two answers.
Typical deliverables
- Source ingestion from APIs, databases and files
- Transformation logic with tests and data validation
- Orchestrated workflows with retries and alerting
- Warehouse or time-series storage modelled for querying
- Backfill and recovery procedures
- Pipeline documentation and runbooks
Who it is for
Businesses whose data is spread across tools, and who need one dependable version of it before analytics or AI is worth attempting.
Intelligent Automation
We automate the process end to end: the integrations, the rules, the document and language handling where a model genuinely helps, and the exception path for cases that still need a person.
- Python
- FastAPI
- OpenAI APIs
- Airflow
- Docker
The problem
Skilled people spend hours every week moving data between systems, re-keying forms, checking documents and chasing exceptions. The work is too rule-based to be interesting and too irregular for an off-the-shelf tool.
Typical deliverables
- Workflow analysis and automation scope
- System integrations across APIs and internal tools
- Document and text processing where it fits the task
- Scheduled or event-driven execution
- Human review and exception handling
- Audit logging of every automated action
Who it is for
Operations, finance and back-office teams whose headcount is absorbed by process rather than judgement.
Analytics & Insights
We define the metrics that matter, model the data behind them once, and build reporting that refreshes itself, so the conversation moves from whether the number is right to what to do about it.
- SQL
- Python
- Pandas
- Power BI
- Grafana
The problem
Reporting lives in spreadsheets that one person maintains. Numbers are argued over instead of used, and by the time a report is ready the decision has already been made.
Typical deliverables
- Metric definitions agreed with the people who use them
- Analytical data models
- Dashboards for operational and management reporting
- Automated refresh and delivery
- Ad-hoc analysis of specific questions
- Enablement so your team can extend the reporting
Who it is for
Teams making operational decisions weekly who currently rely on manual exports, memory and assumption.
AI Integration
We integrate AI capability into software you already run: retrieval over your own content, structured extraction, classification and assistive features, with evaluation, guardrails and a clear view of what it costs to run.
- Python
- OpenAI APIs
- FastAPI
- PostgreSQL
- Docker
The problem
Adding AI to an existing product is rarely a model problem. It is a context, cost, latency, evaluation and failure-handling problem, and those are what decide whether anyone keeps using the feature.
Typical deliverables
- Use-case assessment and feasibility review
- Retrieval and context pipelines over your own data
- Structured extraction and classification services
- Prompt and output evaluation harness
- Guardrails, fallbacks and rate limiting
- Cost and latency instrumentation
Who it is for
Product and engineering teams who want an AI feature that survives contact with real users.
API & Backend Development
We build the backend layer: documented APIs, background processing, storage, authentication and deployment, engineered so it can be operated, changed and handed over.
- Python
- FastAPI
- PostgreSQL
- Docker
- GitHub Actions
- AWS
The problem
Models and pipelines are only useful once something can call them. Without a solid backend, every integration becomes a bespoke script and every deployment is a manual event.
Typical deliverables
- REST APIs with documented schemas
- Database design and migrations
- Background jobs and queues
- Authentication and access control
- Containerisation and CI/CD
- Logging, monitoring and alerting
Who it is for
Businesses that need a dependable service layer behind an internal tool, a product, or a data and AI system.
How we work
From idea to production.
A short, legible process. You always know which stage we are in, what is being built, and what comes next.
01
Understand
Understand the business problem, data and requirements.
We start with the problem, not the technology: what decision or process is failing, what data exists, who is affected, and what a good outcome actually looks like.
02
Design
Design the architecture, technology and implementation approach.
We agree the architecture and the technology choices before building, including what is out of scope, so there are no surprises halfway through.
03
Build
Develop the solution using modern engineering practices.
Version control, tests, code review and short feedback cycles. You see working software throughout, not only at the end.
04
Deploy
Integrate the solution into the required environment.
Deployment into your environment with configuration, access control, documentation and a handover your team can act on.
05
Improve
Monitor, optimise and continuously improve the system.
Monitoring and alerting so problems surface early, and a clear route for changes as the business and the data move.
Not sure which of these you need?
Describe the problem instead of the solution. We will tell you which approach fits, or that you don't need us.