AI & Automation
AI Chatbot Development
A useful chatbot is grounded in your specific content — product details, documentation, policies — rather than relying on the model's general knowledge, which is what causes chatbots to confidently make things up. I build these with retrieval-augmented generation, so answers are pulled from your actual data before the model composes a response.
The interface itself is usually the smaller part of the work; the retrieval pipeline and fallback behavior when the bot doesn't know something are where most of the engineering effort goes.
Problems This Solves
- arrow_rightRepetitive customer questions that a support team answers the same way many times over
- arrow_rightDocumentation or FAQ content that's hard to search, where a conversational interface finds the right answer faster
- arrow_rightOff-hours support coverage without staffing a live team around the clock
- arrow_rightGeneric third-party chat widgets that don't actually know anything about your specific product
What's Included
- checkRetrieval pipeline that indexes your content (docs, product data, FAQs) for grounded answers
- checkChat interface integrated into your site's design, not a generic embedded widget
- checkConversation handoff to a human for questions the bot can't answer confidently
- checkResponse logging so you can see what people are actually asking
- checkRate limiting and abuse prevention
How I Work
- Identify the content the bot should draw from and get it into a searchable, chunked format
- Build the retrieval pipeline so relevant content is pulled into context before the model responds
- Design the fallback behavior for questions outside what the bot has grounded knowledge of
- Build the chat interface matched to your site's design
- Test against real questions, not just the obvious ones, before launch
Suitable For
Sites with enough documentation or product content that search alone isn't finding answers efficiently, and support teams fielding a high volume of repetitive, well-documented questions.
Frequently Asked Questions
Will the chatbot make things up?add
Grounding it in your actual content through retrieval-augmented generation significantly reduces this compared to a chatbot relying purely on the model's general training, though no LLM-based system eliminates the risk entirely — which is why a fallback path to a human matters for anything high-stakes.
Can it hand off to a human?add
Yes — when the bot's confidence is low or the question falls outside its grounded knowledge, it can route to a human agent or a contact form rather than guessing.
How is this different from a generic chatbot widget?add
Off-the-shelf widgets are typically either scripted (limited to pre-written flows) or connected to a general model without grounding in your specific content. This is built around your actual product data from the start, and matches your site's design instead of looking like a bolted-on third-party tool.
Does the chatbot need constant updating?add
The retrieval index needs to stay in sync with your content as it changes, which is typically automated as part of the build rather than a manual task.