Generative AI Development
A demo that impresses in a meeting and a system that holds up across ten thousand real requests are different products. The gap is evaluation, retrieval quality and knowing what to do when the model is confidently wrong.
Discuss your AI project
What We Build
Generative systems that go into production rather than staying a prototype.
Retrieval Systems (RAG)
Question answering grounded in your documents, with citations and an honest answer when the source is missing.
Content Generation
Drafting, summarising and rewriting inside your workflows, with human review where it matters.
Image & Media Pipelines
Generation, editing and variation at scale, with moderation and provenance handling.
Document Processing
Extraction and classification from PDFs, scans and email, with confidence thresholds and human fallback.
Fine-Tuning
LoRA and full fine-tunes where prompting genuinely is not enough — and an honest answer when it is.
Self-Hosted Models
Open-weight models deployed in your own infrastructure where data cannot leave your estate.
What Production Requires
Citations
Every answer traceable to the source passage, so users can verify rather than trust.
Guardrails
Input and output filtering, prompt-injection defences and refusal behaviour you have chosen deliberately.
Cost control
Caching, routing and model tiering so unit economics work at real volume.
Full request logging
Prompt, context, response and score retained for debugging and audit.
Human in the loop
Review queues and approval steps wherever an error would be expensive.
Model portability
An abstraction layer so you can change provider without rewriting the product.
Where We Put AI To Work
We take on AI projects where the outcome can be measured — a cost that falls, a queue that clears, a decision that gets more accurate.
Talk to our teamAdvanced AI Engineering Capabilities
The difference between a demo that impresses and a system you can rely on.
Evaluation harness
A scored test set built from your real data, run on every prompt, model or retrieval change.
Guardrails
Input and output filtering, prompt-injection defences and refusal behaviour you have chosen.
Cost engineering
Caching, model tiering and context trimming so unit economics work at real volume.
Provider abstraction
One interface across providers, so switching model is configuration rather than a rewrite.
Self-hosted options
Open-weight models served in your own infrastructure where data cannot leave your estate.
Full request logging
Prompt, context, response and score retained for debugging and audit.
How We Deliver
Six to ten weeks from first conversation to something in production, typically.
Find the use case
Where AI genuinely beats the current process, with a measurable outcome attached.
Build the eval
Test set and scoring harness created from your real data before feature work.
Build and iterate
Retrieval, prompting and interface developed against the score.
Ship and monitor
Production release with logging, cost tracking and ongoing evaluation.
Get Past The Prototype
Tell us the task you want AI to do. We will tell you whether it is a good fit, and scope it with evaluation built in.
Talk to our AI teamGenerative AI Questions
Everything you need to know about our ai development work.