AI Chatbot Development Company
Chatbots that know when to answer and when to hand off to a person.
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AI chatbot development is building a conversational interface — for customer support, lead qualification, or internal use — powered by a large language model connected to your actual data. As an AI chatbot development company, DuCodes scopes every bot around what it should answer directly, what it should escalate, and how it fails safely when it doesn't know.
Most "AI chatbot" disappointment comes from one root cause: a bot that answers confidently instead of accurately. We build chatbots that are grounded in your own documentation, product data, or support history through retrieval, so answers come from your actual content rather than the model's general training — and we design explicit escalation paths for anything outside that scope.
This covers customer-facing support widgets, lead-qualification bots for a marketing site, and internal chatbots that let a team query internal documentation or systems without digging through a wiki or ticketing tool.
Why work with DuCodes on this
Grounded answers, not guesses
Retrieval against your real documents/data means the bot answers from what you actually publish, not from whatever the underlying model assumes.
A defined escalation path
Every chatbot we build has an explicit "I'm not sure, let me connect you to a person" behavior — we scope this before writing a single prompt.
Built into your existing stack
The chatbot lives inside your actual website or app and can read from (and where appropriate, write to) your CRM or support system, instead of being a bolted-on third-party widget.
Honest about limits
We'll tell you directly if a rules-based flow or a better help-center search would solve your actual problem more reliably than a chatbot.
How we work
Scope the conversation
What should the bot handle end-to-end, what should it escalate, and what does a good vs. bad answer look like?
Connect your data
Documentation, product catalog, or support history indexed for retrieval so answers are grounded in what you actually publish.
Prototype against real questions
Testing with real (or realistic) customer questions before committing to a full build.
Build with guardrails
Rate limiting, confidence thresholds, and monitoring so a bad answer gets caught, not just shipped.
Launch and tune
Real usage surfaces gaps in the knowledge base and prompts — we iterate on both after launch, not just before it.
Technology we use
Frequently asked questions
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