AI Chatbots Development
Most chatbot projects fail for the same reason: the bot is judged on whether it answers, when users judge it on whether it helps. A confident wrong answer about a delivery date or a refund policy costs more trust than no bot at all. Zelpex builds assistants grounded in your actual content and systems, with a clear escalation path to a human and honest limits on what they will attempt.
We build customer-facing assistants and internal tools — support triage, order status, document search over policies and manuals — using retrieval over your own content rather than relying on a model's memory. The engineering that decides quality is in the retrieval layer and the evaluation harness, not in the prompt.
- Retrieval grounded in your documentation, catalog and policies
- Integration with order, ticketing and CRM systems for real answers
- Escalation to a human with the conversation context carried across
- Evaluation sets and logging so quality is measured, not assumed
Pick a narrow first job
One high-volume, low-risk question type — order status, opening hours, returns policy — proves the pipeline and earns the right to expand. General-purpose assistants launched broadly are where reputations get damaged.
Ground it in your content
We build the retrieval layer over your real documentation and data, with source attribution, so answers can be traced back and corrected at the source rather than by prompt-tweaking.
Define the refusal behaviour
We decide explicitly what the assistant must not attempt — anything touching payments, medical or legal advice, account changes — and make it hand over rather than guess.
Measure before widening
An evaluation set of real questions with known-good answers runs on every change, so expanding scope is a decision backed by numbers instead of by the last demo.
What teams ask us about AI assistants
Ground it in retrieved content, require citation of the source, and have it refuse when retrieval returns nothing relevant. That combination removes most fabrication. It does not remove all of it, which is why high-risk topics should be routed to a human by design rather than handled carefully by prompt.
Less than most teams expect for text assistants, and it scales with conversation volume and context size. The larger cost is usually maintenance: keeping the content index current, reviewing conversation logs and expanding the evaluation set. Budget for that, not just for the build.
No, and we would be sceptical of anyone promising that. It reliably absorbs the repetitive high-volume questions and gives agents context on the rest. Teams that plan for deflection plus better-informed agents get value; teams that plan for headcount reduction tend to be disappointed.
How we judge a good assistant
By how gracefully it fails, not by how impressive the demo is.
We build these as ordinary software with an AI component: version-controlled, tested, monitored and evaluated. The parts that decide whether it works in production are retrieval quality, escalation design and logging, none of which appear in a demo but all of which appear in the support queue.
Thinking about a project like this?
Tell us what you are building and what is getting in the way. You will get an honest read on scope, approach, and whether we are the right team for it — including when the answer is that you do not need us.
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