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Tools like Chatbase and Intercom Fin answer from their training data, or at best from a document you uploaded and they chunked generically. A custom-built RAG chatbot answers exclusively from your documents, structured the way your queries actually work, with an evaluation suite that measures accuracy before the bot talks to a single user.
The difference shows up in the answers. A generic bot says something plausible. A custom bot says the right thing, cites the source, and says “I don't know” when it genuinely doesn't, because we build that behavior in deliberately.
We integrate with your authentication system, your CRM, and your support ticketing workflow. The bot is not a standalone widget; it is a component in your product.
Tell us what you're building.
Four categories of chatbot we scope and build regularly. Each has a different retrieval architecture and a different definition of “accurate.”
RAG-powered chatbots grounded in your product documentation, help center, and knowledge base. The bot answers only from your content. It does not guess, invent, or drift into off-topic territory.
Conversational bots that qualify inbound leads by asking your defined questions, scoring responses, and writing structured records to your CRM. Runs 24/7 without SDR time.
Employee-facing bots that surface information from internal wikis, SOPs, HR documents, and engineering runbooks. Cut the Slack messages to “does anyone know where…” by 80%.
Step-by-step guidance bots that walk new users through your product's setup flow, answer configuration questions in context, and escalate to a human when the user is stuck.
Four phases, each with a defined output. We don't proceed to the next phase until the current one passes its quality gate.
01
We map your source documents, define the question domains the bot must handle, agree on what out-of-scope queries should return, and set measurable accuracy targets before writing a line of code.
02
Every RAG chatbot lives or dies by its chunking strategy. We test multiple approaches (fixed-size, semantic, hierarchical) measure retrieval recall on your actual queries, and pick the one that performs best.
03
We integrate the retrieval layer with the generation model, build an evaluation set from real user questions, and measure hallucination rate and answer accuracy before considering the bot shippable.
04
We deploy to your infrastructure, connect observability tooling so you can see every query and answer, and set up alerts for confidence-score drops that flag when the bot is struggling.
Plenty of chatbots launch and quietly get disabled a few months later. Retrieval quality is part of it, but the failures that actually end deployments are about behavior, not embeddings. Four of them account for most of the graveyard.
It is confident when it should be unsure. A bot that answers every question, including the ones it has no basis for, trains users to distrust all of its answers. We build a real “I don't have that” path tied to retrieval confidence, so the bot declines instead of improvising. Users forgive a bot that admits a gap; they abandon one that makes things up.
It cannot hand off cleanly. The moment a user needs a human and the bot traps them in a loop, trust is gone. We design escalation as a first-class path: the bot recognises when it is out of its depth, routes to the right human or ticket queue, and passes the full conversation so the user never has to start over.
It forgets the conversation. A bot that asks for the same account detail three times, or loses the thread between turns, feels broken even when each individual answer is correct. We manage conversation state and entity memory across the full session so the exchange feels like one conversation rather than a series of disconnected lookups.
Nobody learns from what it failed. Every question the bot handled badly is free product feedback, and most deployments throw it away. We log every query, surface the low-confidence and escalated ones, and turn them into a queue of documentation gaps to fill, so the bot measurably improves instead of silently decaying.
We'd rather tell you now than take your money on the wrong kind of project.
Teams who need a simple FAQ widget
If you have 20 static FAQs and a help center that already ranks, you need Intercom or a $49/month no-code tool, not a custom build. Come to us when accuracy and integration matter.
Teams who want a no-code setup
We write code. The output is a production codebase your developers can extend. If you want a drag-and-drop chatbot builder, Botpress and Voiceflow exist for that purpose.
Teams without source documents
A RAG chatbot is only as good as the documents it retrieves from. If your product knowledge lives only in the heads of your support team, we need to document it before we can build a chatbot on top of it. We can scope that work separately.
Tell us about your documents, your users, and what accurate looks like for your use case. We'll reply within one business day with a rough scope and price range.