What is an AI agent, and how is it different from a chatbot or basic automation?

An AI agent takes a goal, reads context from your systems, decides the next step within rules you set, and acts across your tools. A chatbot mostly answers questions, and basic automation follows fixed if-then paths. An agent can handle variation in the work and knows when to hand a case to a person.

Do we need to replace our CRM, ERP or helpdesk?

No. Agents work inside the systems you already run and connect to them through their existing interfaces. There is no new platform for your team to log in to or manage.

How do you keep agents under control?

You define the rules, permissions and approval steps. Each agent only has access to the tools it needs, and every action is recorded with what the agent saw, decided and changed. Read more about Agent Governance.

Which workflow should we start with?

Start with a repetitive, high-volume workflow that has clear rules and a measurable outcome, such as inbound requests, scheduling, or overdue invoice follow-up. An AI Workflow Audit helps rank your candidates by value and feasibility.

Which systems can agents connect to?

We work with Salesforce, NetSuite, SAP Business One, Shopify, QuickBooks, WooCommerce, Zoho, Zuper, Attio and Twenty, among others. If you run something else, tell us and we will scope the connection.

What happens when an agent is not sure what to do?

It escalates to a person with the full context instead of guessing. You set the thresholds that decide when an agent acts on its own and when it asks for approval.

What are AI agent development services?

AI agent development services cover the strategy, design, build, integration and ongoing management of AI agents that complete tasks on their own inside your business systems. That includes choosing the workflow, selecting the language model, connecting your tools, adding guardrails and approvals, testing, deployment and monitoring.

How much do AI agent development services cost?

Cost depends on the number of workflows, how many systems the agent connects to, the quality of your data, and the level of ongoing support. A focused single-workflow agent costs much less than a multi-agent system across several departments. After a short discovery call we give you a scoped proposal with a realistic range.

How long does it take to build an AI agent?

A focused agent for one workflow can often go through audit, design and pilot in a matter of weeks. Multi-agent systems with several integrations take longer. We start with one workflow, prove it with your team reviewing results, and then extend to the next.

What is a multi-agent system?

A multi-agent system is a set of AI agents that each own a specific role and pass work to each other. For example, one agent reads an incoming invoice, another matches it to the purchase order, and a third updates the schedule. An orchestration layer coordinates the handoffs and keeps a record of every step.

What is RAG, and do AI agents need it?

RAG (retrieval-augmented generation) lets an agent search your documents, policies and records before it responds. Most business agents need it, because it keeps answers based on your own information instead of the model's general knowledge, and it reduces made-up answers.

How do you reduce AI hallucinations in agents?

We combine several controls: retrieval from your approved data, tightly written instructions, output validation, confidence thresholds, limited tool permissions and human approval for sensitive actions. Every run is logged, so errors can be found and fixed quickly.

AI customer support agent development

Support that never clocks out.

Agents that answer, check the order, update the ticket and hand the hard cases to your team, inside the helpdesk and CRM you already run.

Watch a ticket get solved

AI customer support agents

Customer service automation that resolves requests, not just deflects them.

Most support teams are stretched by the same questions asked all day: where is my order, how do I return this, why was I charged twice. Our AI customer support agents read each request, pull the order, account and policy details from your systems, reply in your tone of voice, and update the ticket. When a case needs judgment, such as a refund or an upset customer, the agent hands it to a person with the full history attached.

We build agents on your helpdesk, CRM and order system, grounded in your own knowledge base through retrieval (RAG). You set the rules for what the agent can do alone, what needs approval and what goes straight to your team. Every conversation is logged, so you can see what was said, what was decided and what changed.

A ticket, start to finish

Pick a request and watch the agent work through it.

The chat shows what the customer sees. The panel shows what the agent does behind it, and when it stops and asks for a person.

Customer chat

What the agent is doing

One agent, every channel

Customers write in anywhere. Your systems stay up to date.

One agent handles the conversation and writes the outcome back to the tools your team already works in. Hover a channel or system to follow its path.

Where customers reach you

Web chatInstant replies on your site

EmailShared support inbox

Customer portalLogged-in account questions

VoiceSee voice agents

Support agent Reads, decides, acts

Where the outcome is recorded

HelpdeskTickets, tags and status

CRMContact history and notes

Order system & ERPOrders, returns, invoices

Knowledge baseApproved answers and policies

What agents take on

The requests that fill your queue, handled at the first reply.

Each request type has its own rules for what the agent does and where a person steps in.

Order and delivery status

Looks up the order, checks the carrier status and replies with the answer and tracking details.

Agent: Answers and logs the reply on the ticket
Person: Steps in for lost or delayed parcels

Returns and refunds

Checks the return window, collects the reason and starts a return within the limits you define.

Agent: Starts standard returns and sends the label
Person: Approves refunds above your limit

Account and billing questions

Finds invoices, explains charges and updates contact details after the customer is verified.

Agent: Sends invoice copies and explains line items
Person: Handles disputed charges

Product help and troubleshooting

Searches your knowledge base and walks the customer through the fix, step by step.

Agent: Guides using approved help articles only
Person: Takes over when the steps do not work

Ticket triage and routing

Classifies each conversation, sets priority and sends it to the right team with a short summary.

Agent: Tags, prioritises and assigns
Person: Reviews anything marked urgent

You write the rules

Switch a rule on and watch requests move to your team.

Escalation rules decide what the agent resolves alone and what goes to a person. Change them any time, without rebuilding the agent. See Agent Governance.

Refund requests Upset or repeat customers VIP accounts Privacy and legal

Agent resolves 0

Goes to your team 0

Built for your support queue

Faster first replies. A team that focuses on the hard cases.

Start with one channel or one request type, review real conversations with your team, then extend.

Talk to us about support agents

Answers grounded in your knowledge base
Handover with full context
Connected to your CRM, ERP and helpdesk

Questions

Answers before you ask.

What is an AI customer support agent, and how is it different from a chatbot?

An AI customer support agent reads a customer's request, gathers context from your order, CRM and helpdesk systems, and takes the next step within rules you set, such as answering, updating a ticket or starting a return. A chatbot mostly follows a script or returns generic answers. An agent works with your live data and knows when to pass a case to a person.

Which channels and systems can it work with?

Agents can work across web chat, email, customer portals and voice, and connect to your helpdesk, CRM, order system and knowledge base. We work with tools such as Salesforce, Zoho, Shopify, WooCommerce and NetSuite, among others. If you run something else, tell us and we will scope the connection.

When does the agent hand a conversation to a person?

You set the rules. Common triggers are refund requests, upset or repeat customers, VIP accounts, privacy or legal topics, and any case where the agent is not confident. The person receives the full conversation, a short summary and the agent's suggested reply, so the customer never has to repeat themselves.

How do you stop the agent from giving wrong answers?

We ground answers in your approved knowledge base and policies using retrieval (RAG), write tight instructions, validate replies before they go out, and use confidence thresholds. If the agent cannot find a reliable answer, it escalates instead of guessing. Every conversation is logged so errors can be found and fixed.

What can the agent actually do, beyond answering questions?

Within the permissions you grant, an agent can look up orders and accounts, update or close tickets, tag and route conversations, send tracking or invoice details, and start returns up to limits you define. Sensitive actions can require a person's approval.

Will customers know they are talking to AI, and can they reach a person?

You decide how the agent introduces itself. Customers can ask for a person at any point, and the request is routed to your team with the conversation attached.

How much do customer support agents cost, and how long do they take to launch?

Cost depends on conversation volume, the channels and systems involved, the quality of your knowledge base and the level of ongoing support. A focused agent for one channel or request type can often be piloted in a matter of weeks. After a short discovery call we give you a scoped proposal with a realistic range.

How do we measure whether the agent is working?

We agree the measures before launch, typically resolution without a person, time to first reply, handover rate, and customer satisfaction on agent-handled conversations. Your team reviews real conversations during the pilot, and we tune the rules and knowledge base from what we see.