Conversations that understand your business.
A chatbot is only worth deploying if it knows things the visitor could not find on their own — and admits when it does not.
Yes — JANNEX builds AI chatbots. They are retrieval-based assistants grounded in your documents, product data and policies, with citations, CRM and helpdesk integration, human handoff, conversation history and analytics. They can be deployed on a website, inside a product, or as an internal assistant for staff.
Why most business chatbots get switched off
The first generation answered from a script and failed on anything unusual. The current generation answers everything, including things that are not true. Both erode trust in about a week.
- Answers invented when the knowledge base has a gap
- No route to a human, or a route that loses the conversation
- Access rules ignored — internal pricing surfaced to the wrong audience
- No record of what was asked, so nobody learns what is missing
- A separate silo from the CRM, so a qualified lead evaporates
How we build a chatbot worth keeping
Ground it, show its working, give it a clean exit, and read the transcripts.
Source of truth
Your content is indexed with its permissions intact. Answers quote it and link back to it.
Honest limits
Below a confidence threshold the assistant says so and offers the next step, rather than improvising.
Human handoff
Transcript, page context and customer record pass to the person who takes over. No re-explaining.
Feedback loop
Unanswered and low-confidence questions become a queue. Content gets fixed; quality compounds.
What this covers
Deployment surfaces
- Website assistant
- In-product assistant
- Internal staff assistant
- Helpdesk co-pilot
- WhatsApp and messaging channels
Understanding
- RAG over private knowledge
- Document Q&A
- Product and pricing lookup
- Multilingual conversation
- Intent and entity capture
Business integration
- CRM record creation and lookup
- Lead qualification
- Ticket creation and status
- Booking and scheduling hooks
- Human handoff with context
Operations
- Conversation history
- Answer quality analytics
- Gap reporting
- Access-controlled knowledge
- Audit logging
A demonstration, not a mock-up.
This assistant is scripted for the page and answers only about how we build chatbots — a real deployment retrieves from your content instead. What it demonstrates is the behaviour that matters: a cited answer, an honest limit, and a route to a person.
Labelled as a demonstration because it is one. We do not present scripted responses as a live system.
Technology
The working set for this capability. Choices are made per engagement, against your constraints and your team's skills.
Where it is typically applied
Patterns we see repeatedly, described generically. Your version will differ in the details, and the details are the work.
Pre-sales qualification
Answers specification and pricing-model questions, captures requirement and contact, writes the lead to CRM with the transcript attached.
Customer support
Resolves account, policy and how-to questions from the knowledge base; opens a ticket with full context for anything else.
Employee assistant
HR policy, IT procedure and internal process answered from documents the employee is permitted to see.
Document Q&A
A contract, manual or report uploaded and interrogated, with every answer pointing to a page.
How the engagement runs
The same seven stages, scoped to the size of the problem.
Understand
We start with the constraint, not the feature list. What breaks today, who it affects, what it costs.
Define
A written scope with the trade-offs made explicit — what is in, what is deferred, what we will measure.
Design
Interfaces, data models and system boundaries designed together, because they constrain each other.
Build
Short cycles against a working environment. Reviewed code, tests where they earn their keep.
Launch
Staged rollout with monitoring in place before traffic, not after the first incident.
Learn
Instrumented usage read against the thing we said we would measure at Define.
Scale
Performance, cost and operations tuned once real load has told us where the pressure is.
Questions we are asked
Can it use our existing documents?
That is the normal case. PDFs, web pages, wikis, spreadsheets and helpdesk articles are ingested, chunked and indexed. Retrieval respects the permissions attached to the source.
What happens when it does not know?
It says so and offers a route — a human, a form, or a phone number. That behaviour is configured, tested and monitored, not left to chance.
Can it hand over to our team?
Yes. Handoff passes the transcript, the page the visitor was on and any captured details into your helpdesk or CRM so the conversation continues rather than restarting.
Which languages?
The underlying models handle major languages well. We test the specific languages you need against your own content rather than assuming parity.
How is it kept accurate over time?
Re-indexing on a schedule or on content change, plus a report of questions the assistant could not answer. Those become content updates.
Related reading
AI chatbots vs AI agents: what businesses actually need
The two are engineered differently, fail differently and cost differently. Choosing the wrong one is the most common way an AI project stalls.
Read AI Chatbots · 8 min readHow to build an AI chatbot that understands your business
The model is not the hard part. Getting your own knowledge into a state it can be retrieved from is.
Read AI · 6 min readRAG vs fine-tuning: which approach makes sense?
They solve different problems. Most business cases need retrieval; a minority genuinely benefit from fine-tuning, and a few need both.
ReadHave something worth building?
Tell us the constraint you are working against. If we are not the right people for it, we will say so.
Or write to connect@jannex.in