Outcomes
Handle customer support with AI
Answer the repeated questions fast, and hand the rest to a person.
Most questions reaching support have already been answered hundreds of times. Automating those gives the team back the time for the ones that need judgement — and that is where support stops being a cost centre.
What separates this from a 2010s chatbot is knowing when to stop. An agent that insists on answering what it does not know costs more than not existing: you lose the customer and you lose trust in the whole channel. Work done properly spends more time defining limits and escalation than writing answers.
What can be delivered
- Analysis of your ticket history, to learn what is repeated and what is not
- A structured knowledge base — serving the agent and also the people who prefer to look it up
- An agent connected to your systems, able to look up a real order rather than only chat
- Explicit escalation rules, with the handover to a human carrying the conversation context
- A defined, tested tone of voice, so the agent sounds like your company and not like software
- Measurement of resolution, satisfaction, and how often the agent should have handed over and did not
The skills this mobilises
How long it takes
Four to eight weeks to answering real customers, with a shadow phase in between — the agent answers and a human reviews before it goes out — which is the part nobody should cut.
When this is the right outcome
Choose this when the volume of repeated tickets already forces a choice between slow replies and more headcount. Do not choose it to paper over a product problem: automating the reply to a recurring complaint is paying to repeat it faster.
Other outcomes
Tell us the problem. We'll tell you who solves it.
Half an hour of conversation. If it isn't work for us, we say so in that conversation.