Generative AI that works in production, not just in a demo
We build LLM-powered products and workflows around your data and processes, with the evaluation, guardrails and monitoring needed to trust them in front of customers and staff.
What we deliver
RAG & knowledge assistants
Retrieval-augmented systems over your documents and databases, with citations, access control and measurable answer quality.
AI agents & workflow automation
Tool-using agents that handle multi-step tasks such as triage, research, data entry and reporting, with human-in-the-loop checkpoints.
Copilots in your product
Embedded assistants, summarisation, search and content features designed around real user journeys.
Document & data intelligence
Extraction, classification and understanding of contracts, invoices, forms, emails and call transcripts.
Evaluation & safety
Test sets, automated evals, prompt and model regression checks, red-teaming and guardrails.
LLMOps & cost control
Model routing, caching, tracing, latency and spend monitoring across hosted and self-hosted models.
What you can expect
- Quality measured against your own test cases
- Provider-agnostic design so you avoid lock-in
- Data privacy and access controls designed in
Tools we use
- Claude
- OpenAI
- Llama
- LangGraph
- Vector databases
- pgvector
- Python
- TypeScript
- MCP
- OpenTelemetry
How we work together
- 1
Discover
We learn your goals, users and constraints, and define what success looks like.
- 2
Design
Architecture, UX and a delivery plan, with risks surfaced early.
- 3
Build
Iterative delivery in short cycles with demos, automated tests and CI from the start.
- 4
Launch
Staged rollouts, monitoring and runbooks so go-live is uneventful.
- 5
Evolve
Measure, learn and improve, with ongoing engineering support as you grow.
Questions about ai & generative ai engineering
What is the difference between RAG and fine-tuning?
RAG gives a model access to your up-to-date knowledge at query time, while fine-tuning changes the model's behaviour or style through additional training. Many business use cases are best served by RAG first, with fine-tuning added where it measurably helps.
Can you keep our data private?
Yes. We can use providers with enterprise data-protection terms, deploy in your own cloud account, or run open models on your infrastructure, depending on your requirements.
How do you stop an AI system from making things up?
We ground answers in your data, require citations, add validation and guardrails, and measure hallucination rates with automated evaluations before and after release.
Which models do you use?
We are model-agnostic and select between leading hosted models and open-weight models based on quality, latency, cost and data requirements for each task.
Related services
- Web & Platform EngineeringFast, search-friendly websites, web apps and API platforms built to scale.
- Cloud & DevOps EngineeringSecure, cost-aware infrastructure and delivery pipelines on AWS, Azure and Google Cloud.
- Core Machine LearningPredictive models, computer vision, NLP and MLOps that turn your data into decisions.
Ready to set your course?
Tell us what you are building. We will reply within one business day with honest next steps.