AI that connects questions to your business knowledge
An example of source-backed answers, knowledge retrieval and continuity of support.
Explore the workHow to automate customer support with AI, in order
The five steps in order, why step one is not the chatbot, and what to measure so you know whether it worked.
Most support automation projects start by choosing a chatbot. That is step four. Starting there is why so many of them get switched off within a quarter.
1. Read your own tickets first
Take the last 200 conversations and group them. You will usually find that a handful of question types cover most of the volume, and that a good share of them are not questions at all: they are people asking for the status of something. Those two groups need different solutions, and one of them is not a chatbot.
2. Fix the documentation before automating it
An assistant grounded in stale documentation repeats your errors very confidently. If the answer to a frequent question is not written down anywhere correct, writing it is the actual work — and it improves your human support too.
3. Decide what it may DO, not only answer
Checking an order status is different from changing it. Read-only actions are safe; anything with a side effect gets bounded in code and, where it has consequences, goes through human approval. Never bound it in the prompt alone.
4. Then build the assistant
Grounded in your documentation, citing the source, and with an explicit "I do not have that" when retrieval comes back empty. Silence is better than invention.
5. Design the escalation as carefully as the answers
A person should receive the conversation and a summary, not a bare notification. The most common failure of support automation is not a wrong answer: it is trapping someone who needed a human three messages ago.
What to measure
- Share of conversations resolved without a person, and the satisfaction of exactly those conversations. One number without the other is misleading.
- Escalation rate and how long the handoff takes.
- Questions the assistant could not support: that list is your documentation backlog.
- Model usage per conversation, so the bill is not a surprise.
Start with one channel
Web chat, WhatsApp and email each bring their own expectations and handoff rules. Launch on the channel where most of your repetitive questions already arrive, measure it, and only then add the next one.
Privacy and data
Decide what the assistant may see, what it stores and for how long. Conversation logs are useful for improving answers, but they contain customer data: limit who can read them and do not send personal details to analytics.
What we will not promise
A deflection percentage. It depends on your ticket mix and your documentation, and any figure quoted before reading your tickets is invented.
The demo shows the awkward cases
A valid answer with sources, a question with insufficient support, and a handoff to a person.
Frequently asked questions
Will an AI assistant replace our support team?
No. It takes the repetitive questions and status checks so your team can focus on exceptions, complaints and anything that needs judgement. The handoff to a person is part of the design, not a failure.
What do you need from us to start?
A sample of recent conversations or tickets, your current help content, and the systems the assistant should check, such as order status. Read-only access is enough for a first version.
How do we know it is working?
Track resolution without a person together with the satisfaction of those same conversations, the escalation rate and the questions it could not answer. One number on its own is misleading.
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Start with your tickets, not with a chatbot
Send us the question types you see most. We will tell you which are worth automating and which need a process fix instead.