An AI consultant
for small business
that builds it, too.
You wrote the plan with AI. We make it run.
If you're like the owners who call us, you've already done the hard thinking. There's a long spec, a stack of chats, maybe a first version built on a weekend. What's missing is the part that makes it real: plugging it into the software your business runs on, deciding who can see what, and checking the work every day. That's our AI implementation work, and it starts with a small first phase you can watch run.
What owners bring to an
AI consultant, and what's next.
Here's a real one. A business owner brought us a detailed spec they wrote with AI. It was good. It named problems most briefs miss until six weeks into a build, like the same file showing up twice. They'd also built a quick first version themselves and hit the wall right where most people do.
That's a great place to start from. Three things we took away from pitching work like that:
- The spec is a draft, and a good one. AI writes a thorough document. Some of it is real daily pain, and some of it is the model being thorough. Our first job is sorting the two and picking what you need first.
- Reading documents is the easy half. Summarizing a PDF makes a nice demo. The real job is putting that information into your system correctly, and then telling you exactly what changed.
- Your first try taught you something. Hitting the wall yourself means you know what's possible and where it gets hard. If you built it, there's usually nothing to unpick. We pick up where you stopped and keep what's worth keeping.
You stay in charge. We work alongside you, with you holding the keys. Everything we build is yours and documented in plain English. No month-long disappearance and no black box at the end.
Top 5 things owners underestimate
about AI implementation.
None of these show up in a chat window. All of them show up the first week a tool touches real customers and real records.
- 1
Where the truth lives
Every business has one place that holds the real answer: the job, the order, the customer record. Your AI has to read and write there, in the shape your data already has. So we map your actual setup before we build a thing, and we build against that instead of the version on the whiteboard.
- 2
Writing into your system
Reading is safe. Writing is where things break. The same document sent twice can't create the record twice. A revised version can't erase the history. Some software even has an update method that quietly wipes every field you didn't mention. We read the vendor's documentation for traps like that before we promise anything.
- 3
Who gets to see what
You can't keep a secret by asking an AI nicely. Instructions can be talked around. If the accounting numbers are for you and your bookkeeper only, the system has to check who's asking before it fetches anything. Decide that once, at the start, and a customer view or a new role later is just a setting. Skip it, and it's a rebuild.
- 4
The edge cases
Half an order arriving today and the rest next week. A charge that's wrong, and a charge nobody can check yet, which are two different problems. These odd cases are most of the real work, and they're where your team's know-how matters most.
- 5
Who keeps it running
Connected software changes. Access keys expire, usage limits get hit, and somebody adds a new field on a Tuesday. A tool nobody watches slowly stops working. We plan the upkeep from day one and put it in writing.
Small first phase. Real proof.
Then the next step.
Big AI builds fail the way any big project fails: months of work before anyone sees it run. So we break it up, and you see something working early.
The foundation plus one loop
Sign-in and permissions built in, your real setup mapped, and one everyday task working end to end, with a plain report of every change it makes. Small on purpose.
Priced once you've seen it run
Each phase is priced once the one before it is running. You're never guessing at the cost of step four while step one is still a drawing.
Added when you want them
The nice-to-haves, like a customer view or accounting access, wait until you ask. Because permissions came first, adding them later is simple.
Two habits that keep the estimate honest:
- We ask for five or six real examples. The emails, PDFs and forms your business actually receives tell us more than any spec, and they're the one thing most likely to move the number.
- Good, fast, cheap: pick two. When an owner tells us good matters more than fast, we pace the work to be right, and phase one still gets you something useful early.
The five pillars behind our
AI integration services.
Most businesses already have plenty of AI. What they need is for it to work every day without someone babysitting it. That takes five things, built in from the start.
Structure
Clear jobs, clear schedules, and one home for the work and its history.
Memory and rules
Your business context and your rules written down once, so every session starts with them.
Security
Each job gets its own limited access. If a job should get a price and never place the order, we build it without the ordering part at all.
Checks
Every job reads back what actually changed. A green light only says it ran, so we look at the result.
Human approval
Anything that reaches a customer, moves money or goes public waits for a yes from a person.
The same system that runs
an agency, pointed at your project.
We run our own marketing agency on these pillars every day: connected to live systems, working on a schedule, held to written rules, and remembering what it learned. Every number here is a count, taken on September 26, 2026, and every one of them is checkable.
And the work no longer stops when the laptop closes. Claude Cloud lets scheduled AI jobs run on Anthropic's machines, on the subscription you already have, as often as once an hour. We've set up a locked-down account for our first one. Here's the plain-English version: Top 5 Reasons Claude Cloud Is a Big Deal.
What still takes a human
AI makes the work run on time. Getting it right still takes people who know the business. Five jobs stay with a person, every time.
- Choosing what to build first. AI can write ten features into a spec. You know which one eats the most hours every week.
- Knowing your business. The odd customer, the unwritten price, the exception everyone just knows. We ask, and we write it down.
- Saying yes. Customer messages, money and anything public wait for a person.
- Deciding access. Who sees what is a business decision. We build it, and you make the call.
- Reading the result. A job that finished isn't always a job that worked. Someone looks.
Four steps, and you keep
control at every one.
Discovery is its own piece of work with its own flat fee, agreed before anything starts. The build, if there is one, is quoted separately once we both know what is actually there. Nobody is guessing at a number.
We agree the discovery
A flat fee for the discovery itself, agreed up front. That covers reading your project, scoring it, and handing you the plan. Nothing beyond it is assumed.
We sign a mutual NDA
Before anyone looks at anything. It cuts both ways: it spells out exactly what our systems see and touch, and everything of yours stays yours.
We read it, read-only
Your project, your spec and the software it has to work with get read and scored without being changed. We also check that your software can actually do what the plan needs. At this stage it's diagnosis only.
You get the plan, and the number
A written handoff: what you have, what is missing, a small phase one to build first, and what the build would cost. It is yours whether or not you build it with us.
What to send
Whatever exists. Half-finished is normal and genuinely fine. A spec written with AI is welcome, and so is the rough version you built yourself.
- Your spec or plan, even if AI wrote most of it
- The chat threads where the idea took shape
- Any first version you built, however rough
- Five or six real examples of what it will handle: the emails, PDFs or forms you actually get
- The software it needs to read from and write into
- Who should see what, and what it must never do
- Where it stopped, and what you think stopped it
AI consultant questions, answered.
The questions owners ask us most before an AI build. Short answers first, the way we'd give them on the phone.
What does an AI automation consultant actually do?
We turn an AI idea into something that runs inside your business. That means mapping the software you already use, connecting the AI to it with only the access it needs, building checks that prove each job worked, and keeping a person in charge of anything that reaches a customer or moves money.
I already wrote a detailed spec with AI. Is that useful?
Yes, very. A spec written with AI is a strong starting point. We treat it as a draft: we confirm which parts solve real daily pain, check that your software can actually do what it asks, and pick the one piece to build first.
Why start with a small first phase?
So you see it working before you commit to the whole thing. Phase one builds the foundation plus one everyday task, end to end. Each later phase is priced once the one before it is running, so every estimate rests on something we've both seen work.
Can AI keep private information away from the wrong people?
Only when the system enforces it. Telling an AI to keep something private isn't enough, because instructions can be talked around. We build the check into sign-in, so the system confirms who is asking before it fetches anything.
Can the work run when I'm not at my computer?
Yes. Scheduled automations run on their own, and Claude Cloud now lets scheduled AI jobs run on Anthropic's machines as often as once an hour with your laptop closed. Anything that reaches a customer or moves money still waits for a person to approve it.
Who keeps it running after launch?
Somebody has to, and the plan says who. Connected software changes, access keys expire and usage limits get hit, so a tool nobody watches slowly stops working. Your written plan spells out the upkeep the build needs.
Do you take over my project?
No. Everything we build is yours, documented in plain English. Discovery is read-only, and you approve each step after it, so you stay in control the whole way.
Tell us where it stalled.
One message with what you have and where it stopped is enough to start. Send the spec, the rough version, whatever exists. If it is not a fit, we will say so and tell you what we would do instead.