Custom AI for problems off-the-shelf tools do not solve
Purpose-built AI for problems that off-the-shelf tools do not solve, such as forecasting, document understanding, vision and recommendations.
Typical balance
40 human : 60 AI
Hybrid. Varies by engagement.
Your people keep framing the problem and the measure of success, domain expertise from your team and acceptance testing.
AI takes on model selection, tuning and evaluation, processing data at scale and ongoing performance monitoring.
What is custom AI solutions?
A custom AI solution is AI built for a specific problem that off-the-shelf products do not solve, such as demand forecasting, document understanding, visual inspection or recommendations, using your own data and expertise. It is worth building when your advantage depends on knowledge no vendor product has.
Also known as custom AI development, bespoke AI models, machine learning development and AI software development
Who needs custom AI solutions?
Companies with valuable proprietary data or specialist know-how, and a problem generic tools handle poorly.
A good fit when: your advantage depends on data or know-how that no vendor product understands.
How custom AI solutions works
Feasibility
We check whether the data and the problem support a reliable solution before anything is built.
Build and evaluate
Models are selected, tuned and tested against a success measure agreed with you.
Integrate
The solution is connected to the systems where people will use it.
Monitor
Performance is watched over time and the model is retrained when it drifts.
What you get
- Feasibility study
- Working model with evaluation results
- Integration into your systems
- Monitoring and retraining plan
How long does custom AI solutions take?
A feasibility study in two to four weeks; a production solution typically in two to four months.
Common questions about custom AI solutions
Should we build custom AI or buy a product?
Buy when a product solves the problem well enough. Build when the problem is specific to your business or your data is the advantage. A feasibility study answers this before you spend on either.
How much data do we need for custom AI?
It depends on the problem. Some tasks work well with a few hundred good examples on top of a modern model; others need much more. Feasibility work establishes this.
Who owns a custom AI model you build?
You do, including the code, the trained model and the evaluation results, unless we agree otherwise in writing.
