Chatbots that answer from your knowledge, and know when to stop.
The reason people distrust support bots is not that the answers are wrong. It is that the bot will not let them out. A good assistant resolves what it can and hands over cleanly the moment it cannot.
We build assistants grounded in your actual material — help docs, past tickets, product data, policies — so answers come with a source rather than a plausible-sounding invention. When the knowledge base does not cover a question, the assistant says so and routes to a person with the conversation already summarised.
Deployment matters as much as the model. On WhatsApp, that means template approval, session windows, and opt-in handling done properly. On the web, it means a widget that loads without dragging your page speed down and works on a phone.
What we build
Support assistants
Trained on your help content and resolved tickets, answering with citations and escalating anything outside what it can evidence.
Sales and qualification assistants
Conversational qualification that asks what your sales team would ask, then writes a structured lead into the CRM rather than a transcript nobody reads.
WhatsApp deployment
Business API setup, template approval, session-window handling, and opt-in compliance — the operational detail that decides whether a WhatsApp bot survives contact with real traffic.
Human handoff
A clean route to a person, with the conversation summarised and context attached, triggered by intent, sentiment, or an explicit request.
Answer quality review
A log of what was asked, what was answered, and what escalated, so gaps in your knowledge base become a visible list instead of a vague sense that the bot is not great.
How the engagement runs
- 01
Audit the knowledge
What you have, what is stale, and what the bot will not be able to answer.
- 02
Ground and constrain
Retrieval over your sources, with refusal behaviour when evidence is thin.
- 03
Deploy on one channel
Web or WhatsApp first, measured, before adding the second.
- 04
Review and close gaps
Escalation logs drive knowledge-base fixes, which raise resolution over time.
This is a good fit if
- A recognisable set of repeat questions and somewhere they are already documented
- Support volume high enough that deflection is worth measuring
- WhatsApp-first customer bases in India, UAE, and similar markets
- A willingness to keep the knowledge base current — the bot is only as good as its sources
We’d turn this down
- No documentation of any kind — there is nothing to ground answers in, and a bot that invents them is worse than no bot
- Highly regulated advice where every response needs individual sign-off
- Using a bot to make it harder to reach a human. That is a decision about your customers, and we would rather not build it
Questions we get about this
- Will it make things up?
- It is built to answer only from retrieved sources and to say it does not know otherwise. That behaviour is tested before launch and monitored after. Grounding reduces invention sharply; the escalation path is what covers the remainder.
- Can it work on WhatsApp?
- Yes, on the WhatsApp Business API, including template approval and session-window rules. Most of the work in a WhatsApp deployment is operational rather than conversational, and it is where these projects usually stall.
- What does it cost to run?
- Model inference is usually a small share of the total; the meaningful costs are the messaging platform fees and keeping the knowledge base current. We size the running cost against your actual volume during scoping so it is not a surprise later.
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