Custom chatbots built on Google Gemini, OpenAI GPT, and Anthropic Claude — for lead capture, customer support, and internal knowledge. The chatbot on this page is one of ours: the same architecture, security, and lead-capture flow ships for your domain.
We work across the three model families that matter for chatbots in 2026: Google Gemini, OpenAI GPT, and Anthropic Claude. We pick per workload — fast cheap turns vs. long-context reasoning vs. tool-using agents — and we will tell you when a non-LLM fallback (FAQ search, rules) is the right answer for part of the flow.
Powering our own production chatbot
Gemini 2.5 Flash for fast, low-cost conversational responses; Gemini Pro when reasoning depth matters. Long context windows make Gemini a strong default for knowledge bots that draw on internal documentation.
Strong general-purpose conversational AI
GPT-4o and the latest GPT family for nuanced conversation, function-calling, and structured outputs. Mature tooling, strong streaming, and predictable behaviour for sales and support flows.
For careful reasoning and long-form work
Claude 4 family for chatbots that handle policy-heavy domains, long context, or careful reasoning. Strong at staying within instructions, ideal where tone and safety bounds matter.
Six chatbot patterns we ship today. The first one — lead-qualification — is what is running on this site, so you can see it in action before we quote anything. The rest reuse the same engineering core, adapted to your channel and domain.
Inbound chat that asks the questions a salesperson would ask, decides when there is a real opportunity, and emails your team a curated brief. The bot on this page is the reference implementation — same pattern, your domain.
First-line support over your help-centre articles, product docs, and FAQs. RAG-grounded answers with sources, escalation to a human when confidence is low, and full transcripts for your CS team to review.
Chatbots that search your internal documentation, runbooks, or policy library and answer in natural language. Built with retrieval-augmented generation on pgvector or Qdrant, scoped to roles where appropriate.
Conversational agents that guide a prospect or new customer through a structured flow — capturing requirements, scheduling demos, or completing onboarding tasks — with tool-calls into your CRM or backend.
Same conversational core, deployed across channels: embeddable web widget, WhatsApp Business, Slack workspace bot, or a Telegram/Messenger surface. Channel adapters around a shared chatbot API.
Chatbots scoped to a vertical — e-commerce product Q&A, real-estate listing search, healthcare intake (where clinical advice is explicitly out of scope). Brand voice via prompt design and few-shot examples.
Most "AI chatbot dev" gigs end at a working prompt and a model API call. Ours start there. The list below is what we operate on our own production chatbot — security, rate limiting, content moderation, session management, lead capture, observability — and what you inherit on day one of your engagement.
Swap between Gemini, OpenAI GPT, and Claude without rewriting the bot. We choose per workload and keep the door open if pricing or capabilities shift.
Whitelist-based origin validation so the chatbot API only answers requests from approved domains. No widget-key leak turns into a credit-card story.
Configurable request limits per IP and per session, plus a cap on concurrent sessions. Abuse and runaway loops cost a fraction of an unprotected key.
Pattern-based filtering for harmful content, URL injection, and PII leakage in user input. Tunable per use case — strict for public widgets, relaxed inside guided lead-capture flows.
Secure session IDs with timeouts, conversation history threaded into each turn, and IP tracking for auditability. No accidental context bleed between users.
When a user expresses intent, the bot switches into a guided flow: name, email, optional phone, requested service, short brief. Submits to your contact endpoint or CRM and ends the conversation cleanly.
JSON logs with request IDs, response latency, model, and token usage. Drop them into Datadog, Loki, or CloudWatch — your existing observability stack just works.
Shallow and deep health endpoints (with provider reachability), Docker-ready, behind your load balancer, HTTPS terminated upstream. Boring, in the way production should be.
Six steps. Discovery starts with a feasibility check — a few questions about your use case, your knowledge base, and what success looks like. We will tell you in that call if a chatbot is not the right tool for the problem (sometimes it is not), and we always recommend evaluation passes before anything goes live to real users.
Send a brief through the contact form. We reply within one business day to set up a discovery call. You will leave the call with a model recommendation, a scope, and a sense of what success looks like — or a clear no, if a chatbot is not the right answer.
