What our AI agent development services include
- Lead qualification agents. The agent reads each enquiry, scores it against the rules you give us, asks one follow-up question if something is missing and passes the good leads to sales with a summary.
- Inbox and reply agents. The agent sorts incoming email, drafts replies in your tone from your own documents and leaves them for a person to approve, or sends the routine ones on its own.
- Research and data agents. Give it a company name or a list. It gathers the facts you need from the web and your systems, then writes them into a sheet or CRM record.
- Internal knowledge agents. Staff ask a question in Slack and get an answer taken from your policies, price lists and past tickets, with a link to the source.
- Reporting agents. Each week the agent reads your sales, support or ad data, writes a short plain-English summary and sends it to the people who need it.
Example: a lead qualification agent
A property agency receives enquiries from its website, Facebook and two listing portals. Before automation, a coordinator copied each one into the CRM and replied when she had time, which could be the next morning.
The agent now reads every enquiry as it arrives. It checks budget, area and timeline against the agency’s rules, replies within a minute with matching listings, and books a viewing if the lead is ready. Leads that are unclear get one clarifying question. Everything is written to the CRM, and an agent is alerted in Slack only when a person should step in.
For measured results from a client project, read the real estate lead follow-up case study.
How the agents are built
We build agents on n8n and LangChain, with models reached through OpenRouter so you are not tied to one AI provider. The agent only gets the access it needs: it can read the inbox and write to the CRM, for example, but it cannot delete records. Every decision it makes is logged, so you can see why it did what it did.
Is this right for you?
| A good fit when | Probably not worth it when |
|---|---|
| You get the same type of request many times a day | The task happens a few times a month |
| The rules for handling it can be written down | Every case needs a senior person’s judgement |
| A slow reply costs you sales or goodwill | The data the agent would need is not stored anywhere yet |
How a project runs
- Map the decisions (days 1–2). We sit with the person who does the job today and write down every decision they make, including the awkward exceptions. That list becomes the agent’s rules.
- Build and test on real history (days 3–10). We build the agent and run it against past requests from your own inbox or CRM. You see where it agrees with your team and where it does not before it touches a live customer.
- Launch with a person in the loop (days 11–14). The agent starts in draft mode, where a person approves each action. Once you trust it, we switch the routine cases to automatic and keep the rest for review.
What it costs
Most single-agent projects fall in our Growth package at $800–$1,500, which also covers the workflows the agent needs around it and 30 days of support. Systems with several agents working together, or agents that need custom integrations, start at $2,000.
You also pay the AI provider for usage. For a small business handling a few hundred requests a month this is usually a few dollars to a few tens of dollars, and we estimate it for you before you commit.
See what each package includes on the AI automation pricing page, or book a free audit for a fixed quote.