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AI agents take the routine work in every department, and your people manage them and get through far more. For growing companies with no AI team of their own, we rebuild one department at a time, in your own AWS account, and your team learns to run it.
The workshop is one day, 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.
Your company as a riverboat, rebuilt one deck at a time while it keeps going.
In most growing companies, AI arrived as subscriptions: a chat tool on every desk, an add-on in the CRM, a pilot in sales and another in support. People write faster, but the work moves the way it always did. An order still arrives by email and gets typed in twice. The same questions still land on the same three people, and month-end still takes days. Ask what AI has done for the business, and nobody can point to it.
This year’s AIAn example company of 80 people
a month, on AI tools
Example. At the workshop we look at your own.
In each department, AI agents take the routine steps from start to finish, from the email that comes in to the update in your systems, in the tools the team already uses. A named person manages them and keeps every call that needs judgment. Six examples, from six kinds of business.
Sales · a home improvement company
Every request answered the same evening
A kitchen inquiry arrives after hours. The AI agent answers from your own prices and calendar, asks what your team would ask, and books the visit.
Maya manages it. Her days go on visits and quotes, not on the evening inbox.
Service · a subscription box company
Questions answered from your own policies
The AI agent answers delivery dates, swaps and skips from your policies, and keeps the source. The hard ones reach Lena with the story so far, so nobody asks twice.
Lena checks a sample of the answers each morning. This week she took the 14 that needed her.
Operations · a property manager
Requests turned into booked work
The AI agent reads the tenant’s message, checks the lease, books the right contractor and tells the tenant. Anything over the limit waits for Priya.
Priya approves the big repairs, and sees where every job stands in one place.
Finance · a landscaping company
The books matched every day
The AI agent matches bank lines to invoices and receipts as they arrive, so month-end starts mostly done. Dana decides the ones that don’t fit.
Dana’s month-end goes on the 18 lines that need her, not the 412 that don’t.
People · a home care agency
Shift gaps filled before anyone calls around
When a caregiver calls in sick, the AI agent offers the shift by text to the right people, in the order your rules set. Overtime waits for Jo’s yes.
Jo says yes to overtime from her phone, and nobody spends Saturday calling round.
Reporting · a dental group with three practices
Plain answers about your own business
The owner asks in plain words and gets the number with where it came from, worked out from one agreed meaning of “no-show”.
Mei, the practice manager, manages it. She owns what “no-show” means, so every answer counts it the same way. She no longer pulls the numbers by hand.
More of the work AI agents take on, department by department. Examples, from several kinds of small business. In each, a person decides what the AI agents may not.
The AI list, rankedAfter one day
Example: 14 ideas from one company. The workshop ranks your own.
Every department arrives with ideas for AI, each with someone championing it, and the same debate comes back every quarter. Capable models exist for almost every job. What decides it is the data, an owner and a number it moves. So before we rebuild anything, we score every idea and rank the list, and the department whose ideas move the most goes first.
Why A score decides which idea goes first, because without one, either the loudest voice in the room wins, or no idea gets picked at all.
For a company that arrives with a long list, the workshop runs as PUSH: choose the right AI use case with maximum ROI. It’s also how every Guaranteed AI Outcomes engagement starts, for a company that would rather pay us on the result after a go at week 4.
Six ideas, six kinds of business, and the verdict each got. Examples. The honest answer was often “not yet” or “no”. Where AI agents took the work on, a person owns it and checks what they do.
An insurance broker, 80 people
It moves the renewals that go out late, counted from last year’s records. The account handlers’ team lead signs every letter, and the data is in the broking system and the policy documents.
A solar installer
It moves the enquiries left a day without a call back. The path is the same every time, so it’s a fixed workflow with AI in two of its steps, on the surveyors’ calendar and the enquiry form. The sales coordinator takes any the AI agent isn’t sure of.
A printing company
It moves the jobs reprinted because of a file error. A wrong flag stops a paying job, so the prepress lead approves every flag for the first month, until the record shows the AI agents can be trusted with more.
A care home group, five homes
It would save the hours the managers spend on the rota, and the operations manager owns it. But availability lives in a group chat and on paper, and an AI agent can’t schedule people from a chat thread. Once it’s one record, the idea comes back to the top.
A travel agency
7 in 10 bookings come from repeat clients who phone their own travel consultant. AI agents draft each trip’s documents for the consultant to check instead, and the reason is in the findings report, so nobody argues for it again next quarter.
A marketing agency
Six people already did it, in a chat tool nobody had signed off. It works and costs little, so it stays, with rules on what client data may go into it.
The gain from AI comes from changing how the work moves and who does what, not from the tools. So when we rebuild a whole department, we build the six parts every AI agent needs, in your own AWS account, and the next department plugs into the same six. Our engineers build them with your people, the way AK AI Factory builds every AI system.
Our engineers sit with the people who do the work and write down the rules, the exceptions and who decides each one. Where two rules conflict, your decision owner settles it before any AI agent acts on either.
Each rule becomes a real case with its right answer, and those cases become the first tests.
WhyAn AI agent can only guess at a rule nobody wrote down.
Every business term gets one agreed meaning, mapped to the exact fields in your systems, so “customer” means the same thing in sales and in finance.
An AI agent reads as the person it’s working for, through Amazon Bedrock AgentCore Identity, so the permissions in your systems still apply.
WhyTwo AI agents asked the same question give the same number.
An AI agent reaches each of your systems through one approved tool, behind one gateway. On every call the gateway checks who is asking and what is allowed, and records it.
Known rules and sums stay as ordinary code. An AI agent is kept for the steps that need reading and judgment. Each action carries a key, so a retry after a timeout runs once.
WhyThe tenth AI agent comes in through the same door as the first, so none is wired into your systems its own way.
Every AI agent gets its own identity and only the tools its job needs. The gateway checks the rules on every call, deny by default, and time and spend per task have hard limits. What goes into a model and what comes out passes Amazon Bedrock Guardrails, and every call is metered.
A refund, a credit or a price change waits for a named person’s yes. No AI agent approves its own work.
WhyA hidden instruction in an email can’t turn into a refund nobody approved.
Before we build, you and our lead agree one scorecard: the outcome, today’s baseline, the target and what counts as a failure.
The tests start as 20 to 50 real failures we read one by one, and grow past 100 from your real cases. A second set stays sealed, so nothing can be tuned to pass it. When your team corrects an AI agent, the correction becomes a test it has to pass from then on.
WhyA demo can work and still fail real customers. You see it pass on cases it has never seen.
Your people run the AI agents from one queue, where finished work sits next to the drafts waiting on a yes and the cases nobody was sure how to call. An AI agent earns more authority only once its record shows it, and your people decide when that happens.
From week 1, your people work alongside ours, then take the work over themselves. We write the runbooks as we build, and the source, the tests and the record live in your account from the first week. Your team also runs a drill without us before we step back, deploying a change, restoring from a backup, and stopping a task halfway through.
WhyA system only its builders can run isn't yours yet. It becomes yours when your team passes that drill without us.
The next department plugs into all six. It still needs rules, meanings and tests of its own. The gateway, the limits, the record an auditor accepts and the habits are already there.
…the thing that stood out for me is the work they did assessing our engineering capability. The report … was stand out in terms of highlighting our key risks (many of which we weren’t aware of) and identifying critical next steps - this guided the next couple of years thinking…
Who runs it, week by weekCustomer service, the first department
run by your team
at week 13
Example
AWS has funded part of the work for several of our clients. At the workshop we’ll tell you whether yours could qualify.
It was very easy to be engaged. I was very impressed because I felt like we were moving forward with very good discussions, very smart people, and a lot of empathy and care…
A U.S. cinema chain
Thousands of customer contacts a week
5 pieces
First we stabilized a rushed contact center migration. Five fixed-scope pieces of work followed. Calls are now handled 30% faster. Three chat workflows run with no person in the loop. A major film release came and went with no emergency hiring.
An executive search firm
C-suite and board search
6 contracts
Six contracts followed, each its own piece: the infrastructure, an AI agent for its recruiters, machine learning operations and analytics among them. When its platform vendor shut down in December 2025, the firm came through with zero disruption, on systems it now owns. Its CEO and board now answer their own questions on revenue, margin and pipeline.
A case management software company
For government agencies and non-profits
80 to 90%
Onboarding a new client took six to seven months of configuration by hand, and it lost deals on the timeline. We built two fixed-scope pieces side by side, in five to six weeks. For form and checklist onboarding, generative AI now does 80 to 90% of the form work of onboarding a new client. A person edits and approves the rest: that’s the human step. The second piece is plain-English analytics, with monitors for the riskiest billing cases, so compliance questions are answered in seconds. AWS largely funded the work.
Ryno Strategic Solutions
Marketing for home services
Under 2 weeks
Its clients are home services companies (heating, plumbing, electrical). Tens of thousands of inbound calls came in, more than 150 people analyzed them by hand every day, and the company’s own attempt at AI had not reached production quality. Our AI call analysis was production-ready in under two weeks of a four-week proof of concept, with a total-cost analysis and a go or no-go framework, and four more proofs of concept followed in parallel. The C-suite committed in the first half of the engagement, and fine-tuning opened a path to a lower cost per call at the same accuracy. The target is to take that manual work off their hands. AWS funded $25,000 of the work.
AWS has checked our work. We’re an AWS Premier Tier Services Partner, validated by AWS for agentic AI consulting.
What owners tell us before they start. Got another? Bring it to the workshop.
Then your people already write and summarize faster, and that’s worth keeping. What a seat can’t do is move the work between your systems. The order still gets typed in twice, and the same questions still reach the same people.
In an AI-native company, the routine work in a department runs through AI agents, inside your own systems, and your people make the decisions. At the workshop we look at the tools you pay for and say where one overlaps what a department’s AI agents will do. You decide what to keep.
The AI your people use that nobody signed off goes on the same list, with the team that uses it and what goes into it. Most of it stays, with rules on what may go in. Anything that touches customer data gets a safer route, such as the same models run inside your own AWS account.
Then bring them. A day of ranking on evidence tells you which pilot to take further and which to stop, and it ends the debate about it. Each one is scored the same way, on value, feasibility and data, with its owner named. A pilot you already have may well be the idea that wins. Then it gets the same scorecard: its number, today’s baseline and its pass mark, signed before anyone builds more.
If it doesn’t win, you hear why before you’ve spent more on it, and what would change the verdict, such as an owner, a data feed or a number to measure. If every idea is dropped, you’ve saved a quarter and its budget.
It’s where most teams start, and it rarely decides anything. Capable models exist for almost every kind of job. What decides whether an idea pays is the data behind it, an owner and a number it moves. Once the test exists, each job gets the smallest model that passes it, and you can change models later without starting again.
Nothing has to be replaced first. AI agents work through the tools you already use, each one reached through one approved tool behind the gateway. So the CRM, the accounts package and the rota stay where they are. If a subscription later turns out to be doing little, that’s a separate decision, made when it renews.
A company of 60 or 160 people gets there the way a large one does: one department at a time, each built on the six parts above, each with a scorecard you can check.
AWS funding typically covers the workshop, so finding out what it would take starts the plan without starting the spend. The workshop also tells you if it isn’t worth doing yet. AWS has funded part of the work for several of our clients, and we’ll tell you whether yours could qualify.
Then the first department has a go-or-stop decision in week 4. If the numbers aren’t there, we stop, and you keep what we found: the map of how the work moves, today’s baseline and the scorecard.
The money you don't spend on an idea that was never going to pay off counts just as much.
AI agents take the routine steps: the typing in, the chasing, the same answer for the twentieth time. Every department keeps a named person who manages its AI agents, approves what matters and handles what doesn’t fit. That person gets through far more of the work only a person can do.
Before we build, we write down with your people what the AI agent does in each step, and what the person checks and decides. Your people learn it beside ours from week 1, and they run it when we step back.
They should run it. Our engineers build it with them inside your business until it's live, so they can. Getting one AI agent working matters less than what comes after: making the second department plug in without starting over, which takes the six parts above, the rules, the meanings, the gateway, the limits, the tests and the drill.
Everything we build stays in your own AWS account: the source, the rules, the tests, the runbooks and the record of every task. Your data stays there too, and each AI agent sees only what its job needs. Any model or tool hosted outside it is your choice, and we list it.
If you’d rather not run it yourselves, we run it for you.
We start with a one-day workshop with the engineers who’d do the work. You leave with a findings report, a target architecture and a costed roadmap. It says which department to rebuild first, the order of the rest, and what each one’s scorecard would measure. AWS funding typically covers the workshop.