Most businesses are already experimenting with AI — but very few are using it the right way.
A generic chatbot is not enough. What actually drives value is a custom AI agent built around your workflows, capable of calling APIs, automating decisions, and integrating directly with your systems. This is where AI stops being a demo and starts becoming infrastructure.
With the right architecture, your AI can handle operations, reduce manual work, and scale processes without increasing costs — from customer support to internal tools, data processing, and beyond.
If you're thinking about building a real AI solution tailored to your business, let’s talk.
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My own AI assistant for company documents and SQL data. Users ask in plain language and get answers from their files (RAG with LangChain and FAISS) or from their databases (questions turned into SQL queries). Django web app, Python/Flask microservices, OpenAI, Docker.
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WhatsApp AI assistant for an energy company's employees. It answers questions from company documents (RAG with Azure OpenAI and Cognitive Search), adds source links, verifies who is asking before answering and understands voice messages. Python/Flask, Twilio, PostgreSQL, Docker.