AI Integration & Chatbot Development

RAG grounds responses in approved documentation, while guardrails, confidence thresholds, testing, and human escalation reduce errors. It does not eliminate hallucinations, so high-impact answers should still be reviewed or routed to a person.

Yes. We build integrations with HubSpot, Salesforce, Zendesk, Intercom, and custom CRM platforms — so AI interactions are logged, routed, and escalated through your existing workflows.

RAG — retrieval-augmented generation — means the AI answers from your actual content rather than its general training data. Your documentation, product pages, and policies are indexed, and when a user asks a question the system retrieves the relevant passages and has the model answer from them. This is what separates a chatbot that accurately reflects your business from one that invents plausible-sounding answers, which is the single biggest risk in deploying AI on a customer-facing site.

Costs fall into build and running. The build is a one-off project cost driven by integration complexity and how much content needs indexing. Running costs are usage-based API charges from the model provider, which scale with conversation volume and typically land in the tens of dollars per month for a small site rather than the thousands people often assume. We model expected running costs during scoping so there are no surprises after launch.

Grounding the model in your own content through RAG is the primary control, since the model answers from retrieved passages rather than memory. Beyond that we set explicit scope boundaries so the assistant declines questions outside its remit rather than improvising, and we implement escalation to a human for anything it cannot answer confidently. No system is perfect, and we will tell you plainly which question types warrant a human handoff rather than overselling the automation.

Yes. We build AI integrations for both WordPress and Next.js, and connect them to the systems behind your site — CRM, helpdesk, order management, or internal knowledge bases — through their APIs. This is usually where the real value sits: an assistant that can look up an actual order status is substantially more useful than one that can only answer general questions.

We work with Claude, GPT, and Gemini, and select based on the task rather than brand preference — they differ meaningfully in reasoning depth, context handling, latency, and cost per token. We also build so the provider can be swapped without rewriting your application, which matters in a field where the capability and pricing landscape shifts every few months.