AI Agents
AI Support Agents
From answering repetitive customer questions to checking orders and creating tickets; an agent that does more than respond. It connects to your existing systems, uses your organisation's knowledge, and hands the conversation over to a human when necessary.
This is for you if
- Your support team handles a high volume of repetitive customer questions or requests, and you want to automate part of the response process without adding more staff.
- The information needed to answer customers is scattered across company documentation, FAQs, your website, databases, CRM or files, making accurate answers time-consuming to find.
- You want more than a chatbot that only generates responses. The agent should be able to check orders, create requests or tickets, or hand the customer over to a human support agent when needed.
When not to hire me for this
If the goal is simply to add a chatbot to your website that generates general-purpose answers, without examining your actual business knowledge, support processes and existing systems, you probably do not need this service. The goal is not to build a model that can answer questions; it is to build a reliable part of the support process that you can actually depend on.
How it runs
- 1
Needs and process analysis
We first map what customers ask, how support currently responds, which decisions require human involvement, and exactly where the agent should fit into the process. Not every task needs to be handed to a model; we automate where it creates real value.
- 2
Building the knowledge system
The sources used for answering questions — company documentation, FAQs, website content, files, databases and CRM data — are collected and structured. When needed, the agent is connected to these sources through a RAG system so its responses are grounded in your actual business knowledge rather than model assumptions.
- 3
Connecting the agent to operations
The agent does more than generate text. When required, it can check orders, create requests or tickets, retrieve information from existing systems, and return the result within the same conversation. Clear escalation paths are also defined for situations that require human judgement or intervention.
- 4
Testing, deployment and iteration
The agent is tested against real-world scenarios, from common questions to cases where no clear answer exists. When the agent cannot answer with sufficient confidence, the conversation is escalated to a human support agent, while unanswered questions are recorded for review and system improvement. After deployment, the system continues to evolve based on real usage.
What you are left with
A support agent connected to your actual business knowledge and systems; capable of answering customers, finding the information they need, taking defined actions, and handing the conversation to a human whenever it should not make the decision itself.
Does this describe your situation?
Thirty minutes, no pitch. If this is not the right service, I will say so.