AI & Machine Learning Development
What we do
The hard part of machine learning is rarely the model — it is everything around it. We specialise in taking work that is promising in a notebook or a research paper and turning it into a system that runs reliably, at scale, in production.
Problems we solve
Typical reasons clients come to us for this work:
- A model performs well in research but there is no path to production.
- A proof of concept cannot handle real data volumes or real concurrency.
- Model output needs surfacing to non-technical users in a way they will actually act on.
- Data pipelines are too slow or too fragile for the decisions resting on them.
- You want LLM capability applied to a specific domain problem rather than bolted on generically.
Who it’s for
- Companies with research or a whitepaper and no production engineering capacity.
- Data science teams needing an engineering partner to productionise their models.
- Organisations whose analytics need to reach operational users, not just analysts.
- Businesses exploring on-device or privacy-preserving ML.
Technologies & capabilities
ML & NLP
Deep learning classification, natural-language processing, Gensim, NLTK, SciPy.
Platforms
AWS SageMaker, PySpark for large-scale data, Palantir Foundry.
On-device
CoreML and TensorFlow.js for local inference without per-call cost or data leaving the device.
LLM work
Domain-specific extraction, categorisation and metadata generation via Bedrock.
Delivery
Front-end dashboards in TypeScript and React so results reach the people who act on them.
How we deliver
1. Understand
We start with the hard part — the constraint the project actually turns on. Latency, power budget, data volume, regulatory limits. That is what shapes the architecture.
2. Prove
Where there is genuine technical risk, we prove it early with a focused prototype rather than discovering it late in the build.
3. Build
Iterative delivery with working software you can see. Test-driven where it earns its place, continuous integration throughout.
4. Harden
Scale, failure modes and cost. We design for intermittent connections, awkward data and real concurrency rather than the happy path.
5. Support
Handover that leaves you able to run it — infrastructure as code, documentation and, where wanted, ongoing help.
Relevant work
Frequently asked questions
We have a research paper and a rough prototype. Can you build the real thing?
Yes — that is one of our most common engagements. We have taken an academic whitepaper and a simple proof-of-concept codebase and engineered them into a scalable production system that became a cornerstone of the client’s platform.
Do you build the interface as well as the model plumbing?
We do. Model output is worth little if nobody can act on it, so we build the dashboards and front ends too — including live operational dashboards used daily by non-technical staff.
Can inference run offline or on-device?
Yes. We have prototyped offline, privacy-focused OCR and question-answering running entirely on-device with CoreML, avoiding external services and per-call costs altogether.
Do you work with large or sensitive datasets?
Yes. We have optimised data pipelines for large-scale health data and built classification systems for compliance screening, where accuracy and traceability matter as much as speed.
Related services
Projects like these often involve computer vision development, AWS cloud development.
Start a project
Tell us what you’re trying to build and the constraint you’re up against. We’ll tell you honestly whether it’s something we can help with.