AI that makes it to production, not just the slide deck
Senior ML engineers build models, retrieval systems, evaluation harnesses and the MLOps around them, inside your cloud and your team. Measured on business outcomes, with the guardrails UK regulators and your customers expect.
Written reply from a senior person · no pitch deck · no obligation
5 days
to first working prototype
Evals
before every release
Your cloud
models and data stay with you
0
vendor lock-in
What we build
What AI/ML Engineering includes
Senior-owned, AI-accelerated and integrated into your stack. Everything below sits inside your monthly capacity.
LLM applications and RAG
Retrieval-augmented systems over your documents and data with grounding, citations and evaluation on real questions.
Custom models and fine-tuning
Classification, forecasting, recommendation and extraction models trained on your data where off-the-shelf falls short.
Evaluation and safety
Test sets, regression evals, red-teaming and human review loops so quality is measured rather than assumed.
Data pipelines and feature stores
Clean, versioned data flows that make models reproducible and auditable.
MLOps and deployment
Serving, monitoring, drift detection and cost controls on AWS, GCP, Azure or on-premise.
Governance
Model cards, data-protection impact assessments and audit trails aligned with UK GDPR and the ICO's AI guidance.
Process
How we run AI/ML Engineering
Capacity starts in days. Here is what the first weeks look like.
- 1
Week 1
Use-case and data audit
Identify where AI pays off, assess data readiness and define the metric that matters.
- 2
Weeks 1 to 3
Prototype and evaluate
A working prototype with an evaluation harness, tested on real examples with your team.
- 3
Weeks 3 to 8
Productionise
Hardened pipelines, serving, monitoring and guardrails deployed into your cloud.
- 4
Ongoing
Operate and improve
Drift monitoring, cost tuning and iteration inside the same monthly capacity.
Fit
When AI/ML Engineering is the right call
You have data and a hunch
Support tickets, documents, transactions or sensor data that should be driving decisions but is not.
An AI pilot stalled
The demo worked, production did not. We bring the evaluation, engineering and ops discipline to finish it.
You need AI inside an existing product
Search, summarisation, extraction or recommendations added to your platform with a measurable lift.
Compliance matters
Financial services, healthcare or public sector work that needs explainability, audit trails and UK data residency.
Stack
Tools we use for AI/ML Engineering
We use what fits your business and your existing systems, and we say so when the answer is 'not this one'.
Models
- Anthropic Claude
- OpenAI
- Gemini
- Mistral
- Llama
- Open-weight models
Frameworks
- PyTorch
- scikit-learn
- LangGraph
- LlamaIndex
- Hugging Face
Data & vectors
- PostgreSQL + pgvector
- Pinecone
- Weaviate
- BigQuery
- dbt
Ops
- MLflow
- Weights & Biases
- Langfuse
- AWS SageMaker
- Vertex AI
FAQ
AI/ML Engineering questions, answered.
No. We bring the ML engineers and work with whoever owns the data and the business problem on your side.
Related
Related capabilities
Based in London? See AI/ML Engineering in London
Let's build it together.
Tell us what you need from ai/ml engineering and a senior person will reply with a written, no-obligation view.
No pricing pages, no sales sequences. A written reply from a senior person.