AI Chatbot Development That Survives Contact With Real Users

Anyone can wire a chat box to an LLM API in an afternoon. The demo dazzles. Then a real customer asks a question your training data never covered, the bot invents a refund policy that does not exist, and now you have a support escalation instead of a deflection. The gap between a weekend prototype and a chatbot you can put in front of paying customers is almost entirely the unglamorous parts — grounding, guardrails, evaluation, and observability.

Production AI chatbot development is what closes that gap. Answers grounded in your actual documents with citations. Guardrails that keep the bot on-topic and refuse what it should refuse. An eval suite that catches a regression before it ships, not after a customer screenshots it. Logging that shows you exactly what the bot said, why it said it, and which source it pulled from. We build the boring parts so the impressive part actually holds up.

AI work starts with an eval set, not a prompt. Discovery produces a representative set of real questions and the answers a good bot should give. That eval set becomes the contract: every prompt change, model swap, and retrieval tweak is measured against it, so we are improving a number instead of arguing about vibes. The retrieval pipeline is built and tuned before the conversational layer, because a confident answer grounded in the wrong document is worse than no answer.

Two-week discovery → eval-set design and retrieval build → phased agent build (4 to 12 weeks typical) → guarded rollout with transcript review. You own the source code, the prompts, the eval suite, and the deployment, and you bring your own model API keys.

The chatbots that pay for themselves are not the ones that try to do everything. They are scoped to a job: deflecting tier-one support tickets with cited answers, helping a sales team draft proposals, letting staff query an internal knowledge base in plain English, or guiding a customer through onboarding. We scope the job first and build the narrowest agent that does it well, then expand.

Every chatbot we ship is grounded, evaluated, and observable, so when a stakeholder asks "why did it say that?" there is an answer in the logs. That auditability is what lets you put an AI in a regulated workflow without inheriting a compliance problem.

AI chatbot development served from Macon, GA, with clients across Atlanta, New York, San Francisco, and the rest of the US.

Fixed-fee per scope. Typical ranges:

• Single-purpose RAG support bot over your help center: $10k – $25k
• Internal knowledge-base assistant with auth and access control: $18k – $45k
• Tool-calling agent that takes actions through your API: $30k – $70k
• Multi-channel deployment (web + Slack + widget) with handoff: $35k – $90k
• Discovery sprint with eval set and retrieval prototype: $3,500 flat

Model API usage is billed to your own provider account at cost — no per-message markup. 30-day post-launch support included, with an optional retainer for prompt tuning and new tools.