AI integrations
AI in a product is not "plug in ChatGPT" — it is making it work reliably, fast and cheaply. I built a Ukrainian-language voice assistant with under half a second of latency and a system that picks the model per request on its own.
What's included
- LLMs in your product: chat, document search, text generation and parsing
- Voice assistants: speech recognition (Whisper), noise suppression, spoken answers
- Document processing: extracting data from invoices, contracts and forms into a structured shape
- Model routing: a cheap model for the simple, a strong one for the hard — automatically
- Conversation context and memory, spend limits, answer quality monitoring
How I work
- 1
Scoping
You describe what you need; I ask questions and come back with a plan: stages, timeline, what I need from you.
- 2
Prototype
A working skeleton first, not a slide deck. You see how it works before everything is built.
- 3
Development
Short stages with visible results. Code in a repository, deployed to your server or mine.
- 4
Launch & support
Deployment, monitoring, documentation. Then support by agreement or a hand-over to your team.
Related cases
Real projects from the portfolio closest to this topic.
FAQ
Is Ukrainian a problem for AI?
For some models — yes, especially with background noise. That is why the assistant has VAD noise suppression and picks the model by audio quality.
How much does model usage cost?
It depends on volume. Routing usually cuts costs several times over: the strong model is called only where it is needed.
Will data leave our premises?
You can work via an API with a no-training data policy, or run a local model if requirements are strict.
Have a task?
Describe it in a few sentences and I'll tell you how to do it and how long it takes.