AI & computer vision
We build AI features that survive contact with real users. Image generation, visual search, document reading, classification and assistants, measured on your data and priced per request before you commit.
- Typical length
- 4 weeks to prototype, then 2 to 4 months
- Team
- ML engineer, software engineer, lead
- Starts with
- An AI sprint on your own data
If any of these sound familiar.
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You have a demo that impressed the board and fails on one in five real cases.
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Your model bill grows faster than your revenue.
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Staff still retype data from PDFs and photos.
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You are not sure AI is the right tool and want an honest answer.
What you get.
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Feasibility in two weeks
A working prototype on a sample of your data, with accuracy, speed and cost per request written down.
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Generative image pipelines
Prompt templates, style controls, moderation and storage for products that create images on demand, with the cost of every request tracked.
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Visual search and tagging
Find similar products, detect objects, tag catalogues and flag damaged items from photos.
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Document reading
Pull fields out of invoices, delivery notes and forms, with a human review step for anything the model is unsure about.
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Assistants on your knowledge
Search and answer over your own documents, with sources shown and answers logged for review.
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Evaluation and monitoring
Test sets, regression checks and dashboards so quality does not quietly slip after launch.
How it runs.
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Step 1
Sample
You share a few hundred real examples. We agree what a correct answer looks like.
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Step 2
Prototype
We test two or three approaches side by side and report accuracy and cost per request.
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Step 3
Build
The winning approach goes into your product with logging, fallbacks and a review queue.
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Step 4
Watch
We track quality and cost weekly and retrain or retune when the numbers move.
Tools we reach for
- Python
- PyTorch
- OpenAI API
- Anthropic API
- Stable Diffusion
- pgvector
- AWS Bedrock
We pick the smallest model that meets your accuracy target, and keep the option to run it inside your own cloud account.
Questions about this work.
Do we need a lot of data to start?
Usually a few hundred labelled examples are enough to tell whether an approach will work. We will tell you if it is not enough.
Will our data be used to train public models?
No. We use providers and settings that keep your data out of training, and we can run open models in your own cloud account.
What does it cost to run?
We measure cost per request during the prototype, so you see the monthly bill at your expected volume before the build starts.
Tell us what you are building.
A short call, a clear next step. We reply within one working day.
