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For a business of 30 or 300 people, AI agents take on the work that grows with every customer: the inquiry at 9 pm, the order that arrives by email, the invoice nobody has chased. Each one runs in your own AWS account, with the rights of the person who asked and only the tools you approve, so your people get back to the work only people can do.
A one-day workshop with the engineers who’d do the work. AWS funding typically covers the workshop.
An AWS Premier Tier partner, one of about 300 among more than 130,000, validated by AWS for agentic AI consulting.
A square of small businesses, each with AI agents on the team.
The CRM doesn’t talk to the accounts, orders arrive by email, and the answer a customer wants is in someone’s head. So people fill the gaps: they retype, check, chase and reply.
Every new customer adds a little more of it, and growing comes to mean hiring. The people hired for their judgment spend hours a day being glue.
TuesdayAn office manager’s day
spent being glue
Example
Each job gets its own AI agent, working in the tools your team already uses, and it hands a person anything that needs judgment. The person who manages it checks its work, and gets through far more of their own. Here are six, from six kinds of business.
A real estate office
Inquiries answered and qualified
Answered within 5 minutes at any hour, with your own answers. It asks the questions your team would ask, and passes the hot ones to a person straight away.
Maya reviews last night’s conversations at 8:30 and calls the hot leads first.
A wholesaler
Orders typed in from emails
Read from the customer’s email, checked against your price list and stock, and entered once. Anything odd goes to a person.
Tom in sales checks the orders it flags, and spends the morning with customers instead of at the keyboard.
A clinic
Appointments booked and reminded
Booked from a message, with the reminder sent and a canceled slot offered to the waiting list.
The front desk confirms the day’s bookings and keeps the phone for patients who need a person.
A design agency
Invoices sent and chased
Sent when the work is signed off and chased politely on schedule. A dispute goes to a person, with the history.
Dana takes the disputes, with every reminder and reply in front of her.
An online shop
Customer questions answered from your policies
Answered from your own policies and order records, with the source kept. When the answer isn’t there, your team gets the question.
The owner reads a sample of its answers each Friday, and a wrong one becomes a test.
An accountant
Documents collected and checked
Chased, read and checked, so a missing or blurred page is asked for again before it holds up the work.
Priya opens each return with every document checked, and starts on the advice.
More jobs owners hand to AI agents first. Examples, from eight more kinds of business:
A demo takes an afternoon. Trusting an AI agent with your customers and your systems takes six decisions, and each one stops a failure that would otherwise show up in production. Our engineers make them with you, for the job it will do, the way AK AI Factory builds every AI system.
It reads your documents, price list and customer records when it needs them, and keeps the source. It sees only what the person it’s working for may see. When your facts don’t hold the answer, it says so and asks a person.
WhyAn AI agent never makes up a price or a policy.
Known rules and sums stay as ordinary code, and the AI agent only does what needs reading and judgment, such as an email, a customer’s question or a messy form.
It connects to what you already have, like the CRM, email, calendar, accounts and documents, and changes your systems only through tools you’ve approved. Each action carries a key, so a retry after a timeout runs once. Where a system has no way in, we say so at the workshop, and sometimes the simplest answer is a small piece of ordinary software instead.
WhyNo invoice goes out twice, and no total is worked out wrong.
Each AI agent has its own identity and a short list of tools, so the one that books appointments can’t issue refunds. Each conversation runs in a session of its own, so one customer’s chat never sees another’s.
Acting for a person, it gets that person’s rights and no more. No password or key ever reaches the model, and each tool call gets a short-lived pass for that call alone. What it may touch is a rule at the gateway, deny by default and kept by whoever owns your security rather than written into the AI agent’s code, so tightening a rule needs no new release.
Time and spend per task have hard limits. An offer, a refund or a price change waits for a named person’s yes, and no AI agent approves its own work. None of them holds private data, reads untrusted text and can send data out all at once, because that mix is how data gets stolen.
WhyOne hidden instruction in an email can’t steer it into data it was never meant to touch.
Before we build, you and our lead agree one scorecard: the outcome, today’s baseline, the target and what counts as a failure, with the cost of each finished task beside the result. We test it on last year’s real cases and keep a second set sealed, so nobody can tune it to pass, and a pass has to hold on every run. If the numbers aren’t there by week 4, we stop, and you keep what we found.
We choose the model by testing several on your own cases for quality, speed and cost, and the same tests check the next model before it’s swapped in. So the six decisions hold when the models change.
WhyYou see it work on your real cases before a real customer meets it.
It runs in your own AWS account, so your customers’ data stays there and trains nobody else’s AI, and the bill is yours to see. We trace every task, recording who asked, which version ran, which tools it called, what it cost and how it ended. A new version reaches a few customers first, and rolls back on its own if quality drops.
WhyYou always know what it did, and what it cost.
When your team corrects it, the correction becomes a test it has to pass from then on. It starts with a person approving what it does, and earns more freedom only on evidence: weeks of approvals with almost nothing changed.
WhyA mistake your team corrected once doesn’t come back.
Armakuni helped us build agentic AI capabilities that work inside our content pipeline. The orchestration layer sits in our AWS account, governed by our IAM, audited by our team. We own every piece of it.
AWS funding typically covers the workshop, and AWS has funded part of the work for several of our clients. At the workshop we’ll tell you whether yours could qualify.
What we’ll tell you not to do
Inquiries answered within 5 minutesLive at week 13
of inquiries,
at week 13
Example
Ryno Strategic Solutions
Marketing for home services
Under 2 weeks
More than 150 people analyzed tens of thousands of inbound calls by hand every day. Our AI call analysis was production-ready in under two weeks of a four-week proof of concept. The target is to take that manual work off their hands. AWS funded $25,000 of the work.
A case management software company
For government agencies and non-profits
80 to 90%
Onboarding a new client took six months of setup by hand. Generative AI now does 80 to 90% of the form work, a person edits and approves the rest, and onboarding takes weeks. Built in five to six weeks, largely funded by AWS.
An executive search firm
C-suite and board search
Zero disruption
Its platform’s vendor shut down in December 2025, and the firm came through it with zero disruption, on systems it now owns. An AI agent returns a ranked, reasoned shortlist from thousands of candidate profiles, enriched from recruiters’ calls and emails and written back to the CRM. All the business data stays inside AWS.
A U.S. cinema chain
More than 40,000 calls a day
30% faster
Calls are handled 30% faster, three chat workflows run with no person in the loop, and a major film release came and went with no emergency hiring.
On AWS: Amazon Connect, Amazon Bedrock and AgentCore Gateway.
1,300+engineers
500+customer launches on AWS
70%of the proofs of concept we scope reach production
Checked by AWS. An AWS Premier Tier Services Partner, validated by AWS for agentic AI consulting.
Got another? Bring it to the workshop.
Not for one job. The first AI agent needs the documents and systems of its own job, usually a handful, not all of your data.
Where your facts aren’t written down, writing them down is part of the first weeks, and the scorecard shows whether it was enough. If the data can’t support the job yet, the workshop says so, and says what would.
AWS funding typically covers the workshop, and AWS has funded part of the work for several of our clients. At the workshop we’ll tell you whether yours could qualify.
The work stops at week 4 if the numbers aren’t there, and you keep the map, the baseline and the scorecard.
They can build the demo in an afternoon. The six decisions are what make it safe to give it real work.
Our engineers build it with them, inside your business, until it’s live, so you need no AI engineers on your side. Then your team runs it, with the runbooks and the record, or we run it for you.
A chatbot answers from what it was given, in its own window. An AI agent built this way works inside your systems, with your rules, your tests and your record. It’s built in your own account, so you keep all of it.
It stays in your own cloud account. Each AI agent sees only what its job needs, and what the person it’s working for may see. Nothing goes to a public chatbot, and nothing trains anyone else’s AI.
Every task is on record, so you can show a customer exactly what was done with their details.
Spend one day with the engineers who’d do the work. You leave knowing which job an AI agent should take first, how it would be kept to that job, what its scorecard would measure and whether it’s worth doing. AWS funding typically covers the workshop.