AI Automation & LLM Integration Developer for Your Product

In 2026, a basic chatbot or single AI feature typically runs $5,000–$15,000, a RAG assistant over your own docs $15,000–$40,000, and multi-step agents or automation $40,000+. Ongoing API usage is usually $100–$2,000/month depending on traffic and model. We quote fixed-scope, and design for caching and cheaper models where they fit, so the running cost stays predictable.

A focused chatbot or single LLM feature usually ships in 2–4 weeks; a RAG assistant grounded in your documents in 4–8 weeks; a multi-tool agent workflow in 8–12 weeks. We deliver in weekly milestones with a working demo, so you can test answer quality on your real data early instead of waiting for a final handover.

RAG (retrieval-augmented generation) feeds the model your own docs at query time, so answers cite current, private data without retraining — it's cheaper, faster to update, and far less prone to hallucination than fine-tuning for most use cases. In 2026 we reach for RAG first, and only fine-tune when you need a fixed tone or format at high volume. We build the embeddings, vector store, and retrieval end to end.

We ground responses in your data with RAG, add guardrails and input/output validation, and defend against prompt injection. Before launch we build an eval set — typically 50–200 graded test cases — so every prompt change is measured, not guessed. Agents get tool restrictions, retries, and human-in-the-loop checkpoints on risky actions, so the system fails safe rather than confidently wrong.

Both are excellent in 2026 — Claude tends to shine on long documents, careful reasoning, and safety-sensitive work, while OpenAI has a broad tooling and ecosystem. We build on both and pick per use case, and we structure the code behind a model-agnostic layer so you can switch or route between them as prices and capabilities shift, with no lock-in.