AI that does a specific job.
Assistants, automation and generative pipelines integrated into your products and workflows — scoped to a task with a measurable outcome.
The useful AI projects we take on tend to look narrow on paper. A drafting step that takes four hours down to twenty minutes. A classifier that routes an inbox. A retrieval layer so staff stop searching three systems to answer one question.
The unsuccessful ones are usually broad: a general assistant with no defined task and no way to tell whether it is working. We would rather help you pick the narrow one.
What we build
Assistants on your own content
Retrieval over your documents, policies and archive, with citations back to the source so an answer can be checked rather than trusted.
Generative production pipelines
Models drafting shots, boards, copy or motion, with your team finishing them — the pipeline behind our AI generation and animation work.
Automation and classification
Routing, tagging, extraction and summarising for the repetitive work that currently consumes a person.
Evaluation
A test set and a score before rollout, so you know whether the thing is good enough and can tell when a model change breaks it.
How we work
Pick a task with an owner
One workflow, one person who feels the pain, one number that should move. Without that, there is nothing to judge the result against.
Prototype against real data
Your documents, your edge cases, your messy inputs. Demos on clean data tell you almost nothing about production.
Integrate where the work happens
Inside the tool your team already uses. A separate AI portal is a portal nobody opens.
What you get
- Scoped use case with a success measure
- Working prototype on your own data
- Production integration and access controls
- Evaluation set and quality baseline
- Cost model per request, so running it is predictable
Before you ask
- Where does our data go?
- Wherever you require. We can run against providers with no-training guarantees, keep retrieval inside your own infrastructure, or use self-hosted models where the data cannot leave.
- What if the model gets it wrong?
- We design for that. Citations, confidence thresholds, human review on anything consequential, and logging so a bad answer can be traced.
The rest of the stack
Tell us what you're building.
Send the brief — or the problem, if the brief doesn't exist yet. We come back within one business day with an honest view of how we can help.
Start a project