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AI Integration into Your Products
LLMs, RAG, agents and automation pipelines — integrated where they create real value, not just to add the feature.
Who is this for?
- 01
You have an existing product and want to add intelligence without rebuilding everything.
- 02
You're launching something new and want AI as a differentiator from day one.
- 03
Your no-code experiments have hit their ceiling — you need a solid, maintainable integration.
What I deliver
LLM feature integrated into your application — generation, summarization, classification, structured extraction.
RAG pipeline on your proprietary data with configurable source of truth.
Agent or automated workflow tailored to your business process.
Monitoring and observability on LLM calls: latency, cost, output quality.
Technical documentation and knowledge transfer to your team.
Stack
FAQ
- Does the whole codebase need to be rebuilt to integrate AI?
- No. In the vast majority of cases, the integration is added to an existing codebase without restructuring it. I design integrations to minimize coupling with existing code and keep future maintenance manageable.
- How do you keep LLM API costs under control?
- Through aggressive caching on frequent requests, picking the right model for the task — GPT-4o for complexity, a cheaper model for volume — and cost monitoring from day one. I always set up anomaly alerts on consumption.
- Does user data get sent to external APIs?
- That's an architecture decision to make upfront. Alternatives exist: locally hosted models, on-premise processing, anonymization before sending. I document the data flow explicitly before starting.