AI Chatbot Development Services

A chatbot widget wraps a generic model in a chat window and answers from whatever the model already knows, while a production AI chatbot is engineered around your business: it retrieves answers from your documents and systems, follows your rules, escalates to a human when it should, and is tested against evals before launch. The widget takes an afternoon to install and fails on the first real question. The engineered bot takes weeks to build and keeps resolving conversations for years.

RAG (retrieval augmented generation) means the chatbot searches your actual content (help docs, policies, product data, past tickets) before it answers, then generates a response grounded in what it found, which is how you get answers specific to your business instead of confident guesses from a general model. We handle the unglamorous parts that make RAG work in production: chunking your content well, reranking results, keeping the index fresh as your docs change, and citing sources so answers can be trusted and audited.

We build on both Anthropic's Claude and OpenAI's GPT models, and we pick per project based on the task: reasoning quality, instruction following, context length, latency, and cost per conversation all differ, so we benchmark candidate models against your real queries during the pilot and let the eval results decide. We also keep the retrieval and integration layer model-agnostic, so switching providers later is a configuration change rather than a rebuild.

We reduce hallucination with grounding and guardrails: the bot answers only from retrieved sources, cites where each answer came from, declines when retrieval finds nothing relevant, and every release is run against an eval suite of real questions with known correct answers before it reaches your customers. No system makes hallucination impossible, so we also design the failure mode: an uncertain bot hands off to a human instead of bluffing.

A production chatbot typically takes 4 to 8 weeks from kickoff to launch: the first two weeks cover the data audit and a working pilot on your real content, the middle weeks add integrations, handoff, and guardrails, and the final stretch is eval testing and a supervised soft launch. Scope drives the range. A single-purpose support bot lands near the short end, while multi-system integrations with complex escalation logic take longer.

Yes, human handoff is part of every chatbot we ship: the bot detects when a conversation needs a person (frustration, complexity, high-value intent, or an explicit request), transfers with full context to live chat, email, or your ticketing system, and logs the exchange so your team is not starting cold. Handoff is where most off-the-shelf bots fail, and it is the difference between a bot that deflects customers and one that serves them.

Your data stays under your control: we use API access where providers do not train on your inputs by default, keep your knowledge base in your own infrastructure, redact sensitive fields before they reach the model, and scope the bot's read access to only what it needs. We document the full data flow during discovery, so your team (and your customers' legal departments) can see exactly what leaves your systems and where it goes.

After launch we monitor conversations, track answer quality against the eval suite, refresh retrieval as your content changes, and ship improvements on a monthly cadence, either inside a support window included with the build or as an ongoing retainer through our care plans. Chatbots degrade quietly when content drifts, so the maintenance loop matters as much as the initial build.