What is an AI agent, in plain terms?

An AI agent is software that carries out a task for you rather than just answering a question. A chatbot responds when spoken to. An agent works through a job: it looks at your CRM, works out which leads have gone quiet, drafts a follow-up for each one that reflects the last conversation, and puts them in front of you to send. The difference is that it takes steps and uses your tools, which is also why it needs proper guardrails.

Can an AI agent handle my sales follow-up and keep leads warm?

Yes, and it is one of the clearest fits we see. The work is high-volume, repetitive, and mostly gets skipped when the team is busy, which is exactly the shape that pays back. An agent can watch for leads going cold, pull in the context of your last conversation, draft the follow-up in your voice, and either queue it for approval or send it once you trust it. Most businesses start with draft-and-approve and move to sending later, which we would recommend.

Will it send emails without me seeing them first?

Only if you decide it should, and we would not start there. Anything that leaves your business and reaches a customer is hard to take back, so by default we build those actions behind an approval step: the agent does all the work and you press send. Once you have watched it for a few weeks and trust the output on a given type of message, we can move specific categories to automatic. That sequencing is deliberate, because the fastest way to lose faith in an agent is for it to email a customer something wrong.

Will it sound like a robot wrote it?

That is the main thing that makes or breaks this kind of project, so it gets real attention. The agent works from your actual past messages, your tone, and the specific history with that customer, rather than from a generic template. It should read like a slightly more organized version of you. We tune this against real examples before anything goes out, and if it cannot hit the bar, that is worth knowing before you have paid for a build.

Does it work with the tools we already use?

Usually yes. Agents connect to CRMs, email, calendars, shared drives, helpdesks, spreadsheets and most systems with an API. The practical question is not whether a connection is possible but whether your data is in good enough shape for the agent to make sensible decisions from it. We check that early, because messy data is the most common reason these projects cost more than expected.

How is this different from Zapier or the automation we already have?

Rule-based automation is excellent when the rules are the same every time: when this happens, do that. It struggles when the right action depends on judgment, like what to say to this particular customer given what they asked three weeks ago. An agent handles the judgment part. Plenty of good systems use both, with automation moving data around and an agent handling the decisions and the writing. If your problem is genuinely rule-shaped, we will tell you to use automation and save your money.

There are two numbers: building it, and running it. The running cost is the one people forget, and it grows with use, so a successful agent costs more to run than an ignored one. Both depend heavily on how narrow the job is, which is why we scope one workflow first rather than quoting for a platform. We will give you the run-rate before you commit to the build, not after.

How long until it is doing real work?

It depends far more on your systems and data than on the AI. One clearly defined workflow, with clean data and one or two connections, moves quickly. Something touching several internal systems with approval requirements takes longer, and most of that time goes into integration and testing rather than the AI itself. We would rather scope this honestly up front than give you a number that ignores your setup.

What if it does not work?

You should find that out cheaply and early, not after a big build. We define what success looks like in numbers before starting, and we measure against it. If a workflow turns out to be a poor fit, the right outcome is a small bill and a clear answer, rather than a large bill and a system nobody uses.

Why Claude specifically?

Claude is Anthropic's AI, and it is where our depth is. It writes unusually well, which matters when the output goes to a customer, and the tooling for building agents around it is mature. That is a genuine specialization rather than a claim that nothing else works. If your requirements point somewhere else, we will say so.

Claude Agent Development

AI Agents That Do the Work You Keep Putting Off

Built on Claude, connected to your systems, and run after launch

Following up with leads. Answering the same customer questions. Chasing information between systems. We build AI agents that take on the work your team never quite gets to, and we stay to run them.

Tell Us the Workflow What agents can actually do

What can an AI agent do for my business?

An AI agent carries out a task rather than just answering a question. In practice that means the recurring work that matters but keeps slipping: following up with leads who have gone quiet, drafting replies to common customer questions, pulling information out of documents, chasing data between your systems, and preparing the things someone on your team currently does by hand. It connects to the tools you already use, works from your real history and tone, and, for anything that reaches a customer, hands you the draft to approve rather than acting alone.

Key Facts:

Sales follow-up and keeping leads warm without anyone remembering to
First-draft replies to the questions your team answers over and over
Pulling data out of documents, invoices, and forms
Preparing reports and briefings from systems that do not talk to each other
Anything customer-facing goes out for approval first, until you trust it

Where Businesses Actually Start

The workflows that pay back first, in roughly the order people bring them to us.

Sales follow-up and lead nurture

The agent watches for leads going quiet, pulls up what was actually discussed, and drafts a follow-up that references it. No generic sequences, no leads forgotten because the week got busy. You approve, it sends.

Customer questions and first replies

Most inbound questions are variations on a handful of themes. An agent drafts the reply from your documentation and past answers, so your team edits and sends rather than writing from scratch each time.

Documents and data entry

Invoices, forms, contracts, applications. Work that resists conventional automation because every one is slightly different is exactly where an agent earns its cost.

Reporting and briefings

Pulling numbers from systems that do not talk to each other and turning them into the summary someone currently assembles by hand every Monday morning.

CRM hygiene and chasing

Keeping records current, spotting the deals with no next step, flagging what has gone stale. Unglamorous, constantly skipped, and quietly expensive when it slips.

Research and preparation

Background on a prospect before a call, competitor checks, pulling together what your team needs to walk into a meeting prepared instead of scrambling.

What This Looks Like in Practice

A sales follow-up agent, start to finish.

Say the problem is the familiar one: leads come in, someone has a good conversation, and then things go quiet because the week fills up. Three weeks later nobody can remember who was promising.

The agent watches your CRM for deals with no activity for a set period. For each one it reads the history: what they asked about, what was promised, what stage things reached. It drafts a follow-up that actually refers to that conversation, in your tone, not a mail-merge template with a first name dropped in. The drafts land in one place for you to skim. You edit two, delete one, approve the rest, and they go.

That is the whole thing. It is not glamorous and it does not replace anyone. It means the follow-up happens on the week it should have happened, every week, which for most businesses is worth considerably more than anything clever.

What we would not do on day one

We would not have it sending unsupervised in week one. Not because it cannot, but because you have no reason to trust it yet, and one badly judged email to a good prospect costs more than the whole build saves.

The pattern that works is earning the automation: draft-and-approve first, then once you have watched a few hundred drafts and you are approving nearly all of them unchanged, move that specific category to automatic and keep the rest supervised. Teams that skip this step are the ones who switch the agent off after a bad week.

The goal is not an agent that does everything. It is an agent that reliably does one thing you currently do not.

Which of Your Workflows Is Worth Automating

A rough scoring guide. The more of these a workflow hits, the better a first candidate it is.

Signal	Good candidate	Poor candidate
How often it happens	Many times a week	A few times a year
How similar each time	Broadly the same shape, with variation in the details	Every instance is genuinely different
Cost of an occasional mistake	Low, or easy to catch on review	High and hard to reverse
Where the information lives	In systems you can reach	In people's heads
How you would measure it	A number you could check, like hours or response time	You would just know
What happens today	It gets skipped when things are busy	It always gets done properly

If your workflow lands mostly in the right-hand column, an agent is probably the wrong tool and we will say so. The most useful outcome of a scoping conversation is sometimes finding out you do not need to build anything.

Why Most AI Projects Do Not Pay Off

Sourced industry figures, not our own claims. Primary sources only.

95%

Of generative-AI pilots showed no measurable return. Preliminary and contested research, but consistent with what we see: the failures are almost never the technology.

Source: MIT NANDA (2025, preliminary)

40%+

Of agentic-AI projects Gartner expects to be canceled by the end of 2027, on runaway cost and unclear value. Both are scoping problems.

Source: Gartner (2025)

$2,068

Expected AI spend per employee in 2026, up 50% year over year. Real money is being committed, which makes picking the wrong first project expensive.

Source: Federal Reserve Bank of Atlanta

76%

Of enterprise AI use cases are now bought rather than built. Knowing when an off-the-shelf tool will do is part of the job.

Source: Menlo Ventures

How We Work

One workflow at a time, measured, and honest about what we find.

One

Tell us the workflow

Describe the job that keeps slipping. We tell you whether it is a good fit, whether an off-the-shelf tool would do it cheaper, and what it would take.

Two

Agree what good looks like

Decide the number that proves it worked, and measure where you are now. This takes an afternoon and you cannot reconstruct it later.

Three

Build it narrow

One workflow, connected to your real systems, with approval steps on anything that reaches a customer. Not a platform.

Four

Run it supervised

You watch the drafts before they go. We tune the tone and the judgment against what you actually approve and edit.

Five

Earn the automation, then expand

Once you trust a category, let it run. Then take the next workflow. We stay to run it, and your team learns it as we go.

What We Will Tell You That Others Might Not

A lot of what gets sold as AI agent work does not need an agent. If your process is genuinely rule-based, if this then that, ordinary automation does it more cheaply and more predictably, and we will point you there. If a good off-the-shelf tool already covers your use case, buying it beats building, and most enterprise AI use cases are now bought rather than built for exactly that reason.

We would also rather scope one workflow than sell you a platform. Broad AI programmes are where budgets go to die: they take longer, prove nothing for months, and are hard to switch off gracefully when something is not working. One workflow that pays back earns you the credibility and the budget for the next one.

And the failure mode nobody mentions is adoption. An agent your team quietly stops using returns nothing, however good it is. That is why we build in the open with your people, keep humans in the loop early, and hand over something your team understands rather than something only we can maintain.

If the honest answer is that you should buy a tool, use automation, or do nothing yet, that is the answer you will get.

AI Agents for Business, FAQ

The questions we get asked most often before a first project.

What keeps slipping?

Tell us the job your team never quite gets to. We will tell you straight whether an agent is the right answer, roughly what it takes, and what it costs to run.

Tell Us the Workflow AI agents for sales follow-up

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