AI product development that works in production, not just in a demo.
A demo that calls an LLM takes an afternoon. A product people rely on needs much more: the right model calls behind your own API, failure handling, cost control, and a backend that turns model output into something useful. I've shipped AI products across finance, analytics, voice and location data, and I build the engineering around the model, not just the prompt.
#who-it's-for
- Founders building an AI-first product from scratch
- SaaS teams adding AI features to an existing product
- Businesses automating document-heavy or repetitive work
#what-you-get
OpenAI and multi-LLM integrations behind your own API, with keys never exposed to the client
Document and OCR pipelines that turn files into structured data
AI reporting, analysis and conversational features
Async AI jobs with queues, retries and status tracking
Cost and failure handling: rate limits, fallbacks and usage tracking
#how-it-works
Find the real use case
What the AI should do, what 'good' looks like, and where it must not fail.
Prototype the core loop
Prove the model can do the job on your real data before building around it.
Productionize
Backend, queues, cost controls, monitoring and the UI around the AI.
Improve
Tune prompts, models and flows based on how people actually use it.
#related-work
- Boekie: Automated accounting system with AI-driven insights
- Nero Budget: AI-driven financial management and budgeting tool
- BluntlyAI: Multi-LLM AI data analyst producing automated reports
- Somiqora: AI Text-to-Speech platform for natural voiceovers
- ZipcodeAI: AI-powered tool for location-based insights and analytics
#faq
Can you add AI to my existing product?
Yes. Most AI work is integration work: a dedicated backend module that calls the model, handles failures and controls cost, plus the UI that presents the result. I can add that to an existing Node.js, NestJS or Next.js codebase.
Which AI models do you work with?
OpenAI is what I've shipped most, and I design multi-LLM setups where different models handle different tasks (BluntlyAI is one example). On AWS I also work with services like Amazon Transcribe, and I hold the AWS Certified AI Practitioner certification.
How do you keep AI costs under control?
Model calls go through your backend, never directly from the browser. That makes it possible to cache, rate-limit, pick the cheapest model that does the job, and track usage per customer.
#further-reading
- Should Your MVP Include AI Features?
- What AI-Native Engineering Actually Means
- How to Integrate OpenAI into a SaaS Product Properly
Planning a build like this?
A 30-minute call is enough to tell whether I'm the right fit.
Other services