Startups and scale-ups
Fast iteration on AI-powered products, from MVP to production-ready. Often working alongside founders or small engineering teams.
Startups and scale-ups need to move fast - validate ideas, build MVPs, and get to production without getting stuck in complexity. I work alongside founders and small engineering teams to ship AI-powered products.
The work ranges from greenfield MVPs to adding AI to existing products. I focus on getting something working that you can put in front of users, then iterating based on feedback. For early-stage teams, that might mean building the first version yourself so you can learn what works. For scale-ups, it might mean accelerating a specific AI initiative or filling a gap until you hire. The goal is momentum - shipped software that creates value, not perfect architecture that never ships.
Example AI integrations
AI services and tools I've integrated for businesses include:
Vercel AI SDK
Ship AI features fast with streaming and React integration. For startups, it speeds up AI feature development with minimal boilerplate.
Replicate
Deploy open-source AI models via API without infra. For startups, it runs open models without managing infrastructure.
Together AI
Fast inference for open models when iterating on AI products. For startups, it provides fast inference for rapid iteration.
Groq
Fast LLM inference API for AI product development. For startups, it delivers low-latency inference for real-time AI.
Hugging Face
Open models, datasets, and inference for AI development. For startups, it provides models and datasets for experimentation.
Mistral AI
Open and frontier LLMs for building AI products. For startups, it offers cost-effective frontier models for products.
Try these free tools
AI product integrations
- Search and retrieval - Semantic search over your documents, products, or knowledge base so users find what they need without exact keywords.
- Computer vision and image AI - Image classification, object detection, and visual inspection for quality control, compliance, and automation.
- Workflow automation - AI agents that triage tickets, draft replies, route requests, and trigger follow-ups. Integrate with your existing tools.
Frequently asked questions
- A focused MVP with one core AI feature typically takes two to four weeks to build and deploy. I prioritise getting something working in front of users fast, then iterate based on feedback. Speed to learning matters more than perfection at this stage.
- Yes. I slot in alongside founders and small engineering teams, filling the AI gap until you hire or upskill. I write clean, documented code that your team can own and extend after the engagement.
- I recommend a lean stack: Next.js or your existing framework, Vercel AI SDK for streaming, and API-based models (OpenAI, Anthropic, or open-source via Replicate/Together). This keeps infrastructure simple and costs proportional to usage. No GPU clusters needed to start.
- Yes. I do short validation sprints where we assess whether AI adds genuine value to your product, what the technical feasibility looks like, and what it would cost to build and run. Better to learn this in a week than after months of development.
How quickly can you build an AI-powered MVP?
Do you work alongside existing engineering teams?
What AI stack do you recommend for startups?
Can you help us figure out whether AI is the right approach for our product idea?
Want to discuss AI for your business?
I help businesses integrate AI into their workflows. Get in touch to talk through your specific situation.