AI product development and generative AI integration
AI product development
Generative AI designed and built into real products: voice assistants, chat, document reading and automation. I work with the model and platform that suits your organisation and take features from first prototype to a reliable production service.
AI features people can actually trust
Adding AI to a product is easy to demo and hard to get right. Answers need to be reliable, costs need to stay predictable and, most importantly, people need to understand what the AI did and be able to correct it. I design and build AI features with those questions answered from the start.
I work across the whole feature: deciding where AI genuinely helps, designing the interface and the moments of trust, building the prompts, data flows and integrations, and putting guardrails, logging and usage controls in place. I am not tied to one provider. I choose the model and platform that fit your organisation, whether that is OpenAI, Microsoft Azure, AWS or Google, and design so you can change provider later.
This is hands-on experience, not theory. In Brillama.app I built a voice assistant, a chat assistant and document reading and categorisation into a live product used by small businesses. In the Nightshift concept I explored how people decide whether to trust an AI that acted on their behalf, and how correcting it can feel like teaching rather than fighting.
What my AI product development service includes
AI opportunity workshops
Finding the places where AI saves real time or money, and where it does not.
LLM integration
Connecting OpenAI, Azure OpenAI, AWS Bedrock or Google models to your product and data.
Voice and conversational interfaces
Assistants people can talk or type to, designed around real tasks.
Document reading and automation
Extracting, checking and categorising information from receipts, forms and files.
Prototype to production
Moving a promising proof of concept into a reliable, monitored feature.
Trust, transparency and correction
Interfaces that show what the AI did and let people fix it easily.
Guardrails and data protection
Controls that keep personal data safe and outputs within sensible limits.
Cost and usage control
Monitoring and limits, so AI costs stay predictable as usage grows.
How a AI product development project works
Explore
Workshops to find where AI can help and agree how success will be measured.
Prototype
A working proof of concept tested with real data and real users.
Build
Production-ready integration with guardrails, logging and a clear interface.
Improve
Monitoring of quality and cost, with improvements based on how people use it.
Who this service is for
Teams with a generative AI idea that needs to become a proof of concept, products ready to move an AI prototype into a reliable production feature, and organisations that want AI to save staff time without putting customers or data at risk.
Related work
AI product development questions
Straight answers before we talk. Anything else, just ask.
I am not tied to one provider. I work with OpenAI, Microsoft Azure OpenAI, AWS Bedrock and Google models, and choose based on your data, budget, security needs and the platform your organisation already uses.
Data protection is designed in from the start. That includes choosing providers and settings that do not train on your data, limiting what is sent to the model and keeping personal data handling in line with GDPR.
No model is perfect, so I design for that. Answers are grounded in your own data where possible, checked by rules where it matters, and the interface makes it easy for people to see and correct what the AI did.
Yes, and I usually recommend it. A short proof of concept with real data shows whether the idea works before you invest in a full build.