AI automation built for your business, not a demo.

Three layers: first, I use API tiers that don't train on your data (Claude, OpenAI, and Gemini all offer zero-retention enterprise tiers). Second, sensitive fields are redacted or tokenized before anything leaves your infrastructure. Third, where it makes sense, I deploy smaller models inside your own environment so data never leaves at all. We'll agree the data boundary in writing before I build anything.

A chatbot answers questions from a script or your documents. An AI agent takes actions: it can check your CRM, draft the email, book the meeting, and update the ticket — then tell you what it did. Chatbots save support time; agents save operations time. Most businesses I work with start with the chatbot and graduate to agents once they see the ROI.

Yes — that's RAG (retrieval-augmented generation). Your PDFs, wikis, and docs get indexed into a searchable knowledge base, and the AI answers strictly from your content, citing sources. It doesn't hallucinate company policy because it isn't guessing — it's reading. I built a working example: an AI ticket classifier that routes support tickets using language models.

A focused build — like a support chatbot trained on your docs — typically runs $1,500 to $5,000. Multi-agent systems with deep integrations run higher. API usage costs are separate but usually modest (most clients spend $20–$200/month on model calls). You'll get a fixed quote after a free scoping call.

APIs, in almost every case — fine-tuning is expensive, slow to iterate, and unnecessary for most business tasks. Good prompt engineering plus RAG over your data beats a fine-tuned model 90% of the time, at a tenth of the cost. If your use case genuinely needs fine-tuning, I'll tell you — and why.