Define the job
Specify the user outcome and the evidence needed to judge model quality.
Service / 07
AI product development and integration
Custom AI products, retrieval systems, multimodal experiences and model integrations designed for production reliability rather than a convincing demo.
Discuss this projectA good fit when
- AI is a core product capability
- The product needs grounded search or retrieval
- Multiple models, tools or data sources must work together
What I handle
From useful scope to finished system.
- 01
AI product and model strategy
- 02
Retrieval and knowledge systems
- 03
Agentic product workflows
- 04
Provider and API integration
- 05
Evaluation and output validation
- 06
Cost, latency and reliability controls
Process
Four clear stages.
Choose the system
Select models, retrieval, tools and hosting based on capability, privacy, latency and cost.
Build + evaluate
Develop the product path and test it against real representative cases.
Productionise
Add validation, monitoring, fallbacks and operating controls before launch.
Useful answers
Before you send the brief.
Which model providers do you work with?
OpenAI, Anthropic, Google, open-source and local model stacks. Provider choice follows the task rather than habit.
Can AI be reliable enough for production?
For well-defined jobs, yes—but only with evaluation, validation, fallbacks and sensible human control. Reliability is designed, not assumed.
Can this integrate with an existing product?
Yes. A focused technical review establishes where AI belongs, what data is available and how to introduce it without making the product brittle.
Project enquiry / AI development
Send what you know.
Rough notes are fine. I will reply with the questions, scope and next step that make sense for this kind of work.