AI Integration
Put AI to work inside your product — LLM features, chatbots, RAG and automation.
I integrate AI into real products: LLM-powered features, support chatbots, document Q&A (RAG), content summarisation, classification and workflow automation. The goal is not a demo — it is a feature your users actually rely on, wired into your existing app with sensible cost controls and fallbacks.
I work with the major model providers — OpenAI, Anthropic (Claude) and Google Gemini — and design provider cascades so a feature degrades gracefully instead of failing. Retrieval is grounded in your own data, prompts are versioned, and usage is cached to keep the bill predictable.
What I build
- LLM features inside web and mobile apps
- Support and knowledge-base chatbots
- Document Q&A and retrieval-augmented generation (RAG)
- Summarisation, extraction and classification pipelines
- AI-assisted automation of repetitive workflows
- Provider fallback, caching and cost-control layers
Grounded in your data
RAG over your own documents means answers cite your content instead of hallucinating.
Built to not fail
Provider cascades and timeouts mean a feature degrades gracefully rather than going down with one API.
Cost under control
Caching, model selection and usage limits keep AI features affordable as you scale.
Tech stack
Frequently asked questions
Have a ai integration project?
Tell me what you’re building — I’ll reply with a clear scope and estimate.
varlikbbusiness@gmail.comOther services
Mobile Apps
iOS & Android apps that feel native — built with React Native, Expo and Swift.
Web Apps
Fast, SEO-ready websites and web apps — built with Next.js, React and TypeScript.
iOS & visionOS
Native Apple apps and immersive Apple Vision Pro experiences — Swift, SwiftUI, RealityKit.
Desktop Apps
Cross-platform desktop software for Windows & macOS — POS systems, internal tools, offline-first apps.