Chatbot interface build
A widget or interface matched to your site's design, not a default chat bubble bolted on.
What's included
Grounded in your content, with a real fallback plan
Retrieval pipeline over your content
Answers grounded in your actual documentation or knowledge base, not the model's general training.
Integration into your site or app
Embedded where your users actually are, whether that's a support widget or an internal tool.
Ongoing tuning
Answer quality reviewed and improved after launch as real questions come in, not left static.
Overview
The difference is what it's actually trained on
A generic chatbot widget answers from whatever the underlying model already knows, which means it'll confidently make things up about your specific product or policies. A chatbot built properly uses retrieval-augmented generation: it searches your actual documentation, help articles or knowledge base for relevant content, then answers grounded in what it found, with a lot less room to invent an answer.
We build the retrieval pipeline over your real content, connect it to whichever model fits the use case, and design the fallback behavior for when it genuinely doesn't know something, handing off to a human rather than guessing. This works for customer-facing support widgets and for internal tools helping your own team find answers in scattered documentation.
Tools & platforms we use
- OpenAI API
- Anthropic API
- Pinecone
- pgvector
Questions
Common questions about AI chatbots
4 questions
Is this different from just installing a generic chatbot widget?
Significantly. A generic widget answers from general training data and will confidently invent details about your product. What we build searches your actual content first and answers based on what it finds, which is a meaningfully more reliable approach for anything customer-facing.
Can it be trained only on our own content?
That's the point of the retrieval approach: it pulls from your documentation, help center or knowledge base rather than relying on what the underlying model happens to already know, which keeps answers specific to your product.
Does this replace our support team?
For most teams, no, it handles the repetitive, well-documented questions and frees up your support team for the harder ones. We build in a clear handoff to a human for anything the assistant isn't confident about.
How do you keep answers accurate as our content changes?
The retrieval pipeline pulls from your live content source, so as your documentation updates, the assistant's answers update with it, rather than needing to be manually retrained.
Quick question?
Ask us about AI Chatbots & Knowledge Assistants
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Tell us what your support team gets asked most.
Send an example of your documentation or knowledge base and we'll come back with a fixed estimate for a chatbot built around it.
If the first milestone doesn't match the brief, we'll revise it at no extra cost.
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