Assistants & product experiences
Design writing, research, summarisation and analysis features with a clear user journey. Define which outputs are drafts, how sources are shown and when the user should intervene.
Build / From an idea to a working feature
Generative AI development.
Build custom AI assistants and product features around your users, information and workflows. We take language and multimodal applications through design, integration, evaluation and release.
Discuss this with usThe opportunity
Give your product a useful new capability.
A model can produce a compelling answer in a demo. A useful product also needs the right context, a clear interface, access controls, reliable integrations and a way to handle uncertainty.
We begin with the user task and representative examples. We compare suitable models against quality, latency, cost and deployment needs, then develop the application around the chosen approach. The model is one part of an engineered product.
What we can do.
Language & multimodal applications
Work with text, images, audio or combinations of them where the task calls for it. Connect inputs to usable outputs and the systems where people already work.
Model selection & evaluation
Compare prompts, retrieval, model choices and targeted adaptation using your task examples. Track improvements and regressions as the application evolves.
Tangible outputs
What you receive.
The exact scope is agreed around your workflow. A typical engagement can include:
- A defined user journey and working interface
- A custom AI application integrated with your systems
- Representative evaluations and release criteria
- Cost and latency measurements for the agreed workload
- Deployment documentation and a handover plan
Illustrative workflow
An assistant inside a business application
Understand the request → retrieve authorised context → prepare a grounded output → show sources and review options → save the approved result.
An example of how this capability can fit a business process.Define what better looks like.
We agree useful measures before implementation and compare them with the current workflow.
- ↗Task completion and output usefulness
- ↗Quality across typical and difficult examples
- ↗Latency and running cost per completed task
Your questions, answered.
Do we need to train a new model?
Usually the first question is whether an existing model, well-designed prompts and relevant context can do the job. Training is considered when there is a specific reason and suitable data.
Can you improve our existing prototype?
Yes. We can assess the prototype, evaluate it against real tasks and build the integration, interface and operating controls needed for the next release.
Can we choose the model provider?
Yes. Provider choice is part of the architecture discussion, alongside data handling, capabilities, cost and deployment requirements.
Useful before you start
Questions worth exploring.
RAG or fine-tuning for company knowledge?
Finding current information and adapting model behaviour solve different problems. Identify which one your application actually has.
How to choose your first AI project
Start with work you can observe, data you can access and an outcome you can measure.
From AI pilot to production: what changes?
A working demonstration is a starting point. A released service also needs integration, ownership and a dependable response to failure.
A useful next step
Bring us the work
that should work better.
One process, one product idea, or a question about where to start.