Custom AI Chatbot Development
Chatbots grounded in your own documentation, with citations, honest escalation to a human, and every conversation logged and measurable.
A chatbot people use twice
Most support bots fail for the same reason: they answer from the model's general knowledge instead of from your content, so they are fluent, confident and wrong. Customers learn within two attempts that the bot cannot help, and the deflection rate collapses.
We build assistants that retrieve from your actual documentation, policies and ticket history before answering, cite what they used, and say plainly when they do not know. Saying "I do not know, here is a human" is a feature, not a failure.
Underneath, it is a retrieval system with a conversational surface. Quality comes from the retrieval and the escalation rules far more than from prompt wording.
Teams that start here often pair it with RAG architecture, vector search and AI integration.
The metric that matters is not how many conversations the bot handled. It is how many customers got the right answer without opening a ticket afterwards.
Why customers stop trusting support bots
Four patterns that turn a launch into a switch-off six weeks later.
The bot draws on general model knowledge rather than your policies, producing plausible answers that contradict what your team would say.
The bot loops instead of handing over, so a customer who needs a human spends five minutes discovering the bot cannot get them one.
Content was indexed once at launch and never again, so the bot confidently scopes a policy that changed two months ago.
Nobody logs which answers led to a follow-up ticket, so there is no way to tell whether the bot is helping or quietly annoying people.
What a chatbot engagement includes
Retrieval, guardrails, escalation and the reporting to prove it is working.
Your help centre, policies, PDFs and past tickets chunked and indexed properly, with a scheduled refresh so answers do not go stale.
Every response is generated from retrieved passages and cites them, so a customer or an agent can verify the claim in one click.
Confidence thresholds and explicit triggers hand the conversation to a human with full context attached rather than looping.
Deployed where your customers already are: website widget, Intercom, Zendesk, Slack or WhatsApp, using your existing agent tooling.
Prompt-injection defences, topic boundaries and refusal behaviour for anything legal, medical or financial that must not be improvised.
Deflection rate, escalation reasons, unanswered questions and follow-up ticket rate, so content gaps become a work queue.
From content audit to live assistant through controlled rollout
Grounded, measured and escalating properly before it meets a customer.
We review what documentation exists, what is out of date and what is missing, because retrieval quality is capped by content quality.
Chunking, embedding and reranking tuned against your real questions, measured on a held-out set before any conversational layer exists.
Tone, refusal behaviour, escalation triggers and the handover format your agents actually want to receive.
Your support team uses it first and grades the answers, which surfaces both content gaps and tone problems cheaply.
Live for a share of traffic with escalation set generously, tightened as the deflection and follow-up numbers hold.
The stack behind a grounded assistant
Retrieval quality first; the conversational layer is the easy part.
Hybrid search with reranking, because pure vector similarity misses exact product names and error codes.
A capable model for answer synthesis, constrained to the retrieved context and required to cite.
Deployed into the tools your customers and agents already use rather than a new silo.
Every conversation logged and classified so content gaps turn into a backlog.
What this looks like in production
Deflecting half the ticket queue without hiding the humans
A SaaS company with a four-person support team was drowning in repeat questions about billing and configuration. An off-the-shelf bot had been trialled and switched off after customers complained it invented refund policies.
We indexed the help centre, billing policies and two years of resolved tickets, then required every answer to cite its source. Anything touching refunds, cancellations or account deletion was configured to escalate immediately regardless of confidence.
The support team ended up treating the unanswered-question report as a content roadmap. Deflection improved as much from writing the missing articles as from anything we changed in the model.
An assistant your support team would defend
We would rather ship a bot that escalates too often in week one than one that guesses confidently and effort you a customer.
Every answer comes from your content and shows where it came from, so nobody has to take the bot on trust.
A working demo and a written note every Friday covering what shipped, what slipped and what it means for the date. No status theatre.
Nothing goes live in one jump. We run in parallel or behind a flag until the numbers say it is safe to switch over.
