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AI agents take on the forms, approvals, invoices and reports a growing team carries between its tools by hand, and keep watch on every job your vans and crews are out doing. They work in the tools you already use and improve them as they go, so more gets done with fewer mistakes, and a person still approves what matters.
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.
A new client’s paperwork rides the firm’s own line.
A wholesaler’s credit limits didn’t come with its accounts software. Neither did an accounting firm’s checklist for a new client, or the way a care agency checks a timesheet.
That knowledge lives in people’s heads and in spreadsheets. Someone carries it from one tool to the next, retyping a client’s details, chasing the page that’s missing and checking an order against a limit by eye.
It takes hours every week, and the mistakes turn up weeks later, as an invoice paid twice or a tax ID typed wrong.
The week beside this is an example. The workshop measures your own.
One week between the tools
Done by AI agentsA person’s callCarried by hand
An example accounting firm of 60 people.
Example
Rules could always move a clean field from one tool to the next. They stopped at a scanned form, an email in a customer’s own words or a blurred photo of a payslip, so a person did those, and the workflow stayed half by hand.
AI agents now read them and check what they read against your rules. So a whole workflow can run the way your business runs it, with a person at the steps that need one. Here are six, from six kinds of business, and then more. After them come the workflows of a business whose work is out on the road.
Onboarding
An accounting firm Example
New clients onboarded
Nothing is set up until every attachment has been read and checked.
Approvals
A wholesaler Example
Orders approved within your limits
Your limits decide what goes straight through, and the person you named sees the account’s history before saying yes.
Invoices and payments
A restaurant group Example
Supplier invoices matched before they’re paid
The AI agent checks each invoice against the order and against what actually arrived, so a short delivery is caught before anyone pays it.
Reports
A veterinary group, four clinics Example
The Monday report, written
Nobody copies numbers into a spreadsheet to make it. They come straight from the three systems, and each one says where it came from.
Compliance checks
A home care agency Example
Timesheets checked before payroll
A mistake is found before it is paid, and the person who decides sees the evidence with it.
HR requests
An engineering consultancy Example
HR requests answered the same day
The AI agent checks each request against your own policy and sends anything outside it to the person who owns it, with the receipt beside it.
More workflows AI agents run Examples, from more kinds of business
Onboarding
Approvals
Invoices and payments
Reports
Compliance checks
HR requests
One business’s day, kept on time: a small town inside an open pocket watch.
Doing the work was never the hard part. Keeping track of it is. For a business with vans, crews or deliveries out every day, quotes, bookings, dispatch and tracking run on phone calls, a spreadsheet and the memory of the one person who knows where everything is. In most growing businesses the first alarm is the customer calling, and by then the problem has cost them an afternoon and cost the business some of that customer’s trust.
Take one example Tuesday at a heating and plumbing company. Van 6 stops at 1:05 with an engine fault, on its way to a boiler repair at 14 Elm St promised for 2:00 to 3:00, so Joe would now arrive at 3:40. Today nobody connects the van to the job until Mrs Okafor rings at 2:25 to ask where everyone is. With AI agents it’s flagged at 1:06 with the options already worked out: send Sam, free after 1:30 and 2 miles away, who arrives at 2:15 with no overtime, or keep Joe and tell her now. Nadia, who runs operations, says yes to Sam at 1:08, and at 1:09 Mrs Okafor reads: “Sam will be with you at 2:15 instead of Joe. Same job, same price.”
Each part of the day gets its own AI agent, working in the tools your team already uses, and each one hands a person anything that costs money or needs judgment. Nine examples of operational jobs, from nine kinds of business.
1Plan the work
A commercial cleaning company
Quotes sent while the customer is still comparing.
The AI agent prices each one from your price list and the jobs you’ve done before, and sends it in minutes. Work you don’t usually do goes to a person before it goes out.
An equipment hire firm
Bookings that fit what’s really in the yard.
The AI agent books against what has actually come back, with time to clean and check it in between, so it catches a clash before anyone promises the same machine twice.
A car repair garage
Parts on the shelf before the car is on the ramp.
Every night the AI agent checks the week’s bookings against what’s on the shelves and orders what’s short from your usual suppliers in time. Anything over the limit you set waits for a person.
2Run the day
A heating and plumbing company
Engineers sent with the right part.
Each job goes to someone with the skills and the part, and the sheet carries what they’d otherwise ring the office for, such as the gate code, the cat and the last visit.
A building supplies merchant
Every delivery watched against its window.
The AI agent checks each truck’s position against the window that site was promised. When one slips, the site manager hears it from you first, with a new time.
A furniture delivery company
Customers told before they have to ask.
Every customer gets their window the evening before and an update when it moves. When someone replies “after 1, please” and a van has room, the AI agent rebooks them there and then. Anything else goes to a person.
3Recover and close
A kitchen fitter
Suppliers chased before the fitters turn up.
The AI agent asks each supplier to confirm what’s due this week and reads the replies. When a delivery slips, it works out a new plan for the fitters and the customer, and a person says yes before anyone’s week moves.
A catering company
Shortages found at 7 am, not at the event.
As each delivery is booked in, the AI agent checks it against what tonight’s menu needs and works out the options. Anything that spends money waits for a person.
A pest control company
Jobs closed the day they’re done.
When the technician marks a job done, everything that follows it goes out the same day: the report, the invoice, the stock and the next visit, so none of it waits for Friday afternoon.
She sets the rules they work to, says yes to anything that costs money and edits what goes to customers. The watching and the ringing round are theirs now, so her day goes on the calls only she can make.
jobs watched against their promise
customer updates sent within her rules
decisions brought to her, with the options worked out
customers who had to ring to ask
Hale Heating’s Tuesday, an example
Trusting an AI agent with every form your customers send takes engineering. Each decision below stops a failure you’d otherwise meet in production. Our forward deployed engineers make them with your team, working inside your business until it’s live, as they do in every Guaranteed AI Outcomes engagement, the way AK AI Factory builds every AI system.
We sit with the people who do the work and write down how they decide: the rules, the exceptions and what must never happen, the part no software came with. Each rule becomes a real case with its right answer, and those cases are the first tests. We start with the steps that cost the most hours. In operations we write down your words too: what counts as late, urgent or a priority customer, per kind of job.
WhyThe process in the manual is rarely the one your team runs.
Known rules and sums stay as ordinary code, such as whether a van will arrive after its window, a part is in stock or an engineer is qualified for the job, and code owns the order of the steps. Left to a model, a rule like that can double-book a job or work out a delay wrong. A model is called where something has to be read or judged, such as a scanned form or an email. The AI agent runs the whole workflow, and chooses its own next step only where the path changes every time.
WhyA rule gets a sum right every time. A model would only be guessing at it.
Documents are read into fields with Amazon Textract and a model on Amazon Bedrock. Every field carries a confidence score, and we set the line on last year’s real forms, where we can see how often a score at that level was wrong.
Below the line you set, the AI agent doesn’t guess. It puts the page and the field in front of a person, who checks it and puts it right. A missing or blurred page is asked for again, in your words, before it holds anything up, and every case like that is kept, so the clear ones become tests.
It reads the live facts when it needs them, such as the job board, where each van is, stock at your suppliers and the customer’s notes and history, because old facts mean an alert about a job that was already moved, or a customer told the wrong time. When two facts disagree, say the tracker puts a van on site and the engineer says not, it asks a person.
WhyA wrong tax ID is never filed with confidence.
A discount, an exception or an order over a credit limit waits for a named person’s yes, with everything the AI agent read on one screen. An approval covers one action, its exact values and an expiry, and no AI agent approves its own work.
Each AI agent has its own identity and a short list of tools, so the one that reschedules can’t order parts. You set what it may do on its own, such as moving a job within the same day and telling the customer the new time, in your words. A well-meant fix can cost more than the problem, so overtime, a paid courier, a refund or another engineer’s afternoon waits for a named person’s yes. To begin with, so does every message to a customer, each on record with what prompted it and who approved it.
WhyA quiet exception never turns into a policy.
AI agents change your systems only through tools you’ve approved, and code checks every value before anything is written. Each action carries an idempotency key, so a retry after a timeout runs once. The run’s state is saved at every step, so it can wait for a person and pick up where it stopped.
It runs in your own AWS account, so your customers’ details stay there and nothing goes to a public chatbot. Every alert and every change is traced: what it saw, which rule fired, what it did, who approved it and what it cost. When a customer asks “Why did it move my two o’clock?”, someone can answer.
WhyA timeout never leaves you with a duplicate client, or an invoice sent twice.
Before we build, you and our lead sign one scorecard: the hours today, the target and what counts as a failure. We test on last year’s real cases, such as the late jobs, shortages and cancellations in operations, with a sealed set nobody tunes to, and grade on what actually changed in your systems.
Once it’s live, every step is traced, and each correction your team makes becomes a test it has to pass from then on. An AI agent gets more authority only after its record has earned it, so the line where a person checks moves only when the evidence says so: weeks of approvals with almost nothing changed. A new version starts on a few cases first, and rolls back on its own if quality drops.
WhyA demo that works tells you nothing about Monday. Your own cases do.
New client · Northgate DentalRead, checked, filed once
Your rules, written down
One run of the AI agent: who does each step
The form, read how sure, out of 100; below 90, a person checks it
Filed once
On record
Example
The workshop takes the workflow that costs your team the most hours, and works it through with the engineers who’d build it. It’s usually client onboarding, document checks, approvals or the weekly report.
AWS funding typically covers the workshop. AWS has funded part of the work for several of our clients, and we’ll tell you whether yours could qualify.
After the workshop every step has a date. In week 4 you make a go-or-stop decision, on a scorecard you sign. If it’s stop, you keep the map, the baseline and the scorecard. If it’s go, we’re paid on the result from then on.
What we’ll tell you not to automate
New client onboarding, automatedLive at week 13
of the work,
at week 13
Example
What a client said
…All clients from [one of our brands] have now been moved from manual tagging to Call Intelligence. This represents about 500 clients. All marketing tracked calls are processed by Call Intelligence at this point.
Dinesh D., CTO, Blue Corona
For government agencies and non-profits
Onboarding a new client took six to seven months of setup by hand, and the company lost deals on the timeline. We built generative AI that reads each client’s forms and checklists and sets them up, with a person’s edit and approval as the last step.
A second piece answers compliance questions in plain English. Billing problems used to surface in payer audits; now ten monitors watch the riskiest billing scenarios, among them overlapping visits, unrealistic travel distances and duplicate claims. Both were fixed-scope, built in five to six weeks and largely funded by AWS.
A hospitality recruitment platform, millions of applications a year
Seasonal hiring rushes brought millions of applications, and recruiters did their tasks by hand. We automated resume parsing and bulk job posting, and wired the workflows into applicant tracking systems, on a rebuilt cloud platform.
Automotive logistics, North America
Dealers used to phone operations to chase delays, which were found after they had cost the customer. We built Glovis America’s first dealer-facing real-time tracking portal, with automatic alerts when a delivery goes off plan.
That case is tracking and alerts. The AI agents in the operations examples above are the next step on the same idea.
1,300+engineers across the UK, US, UAE, India and Canada
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.
No. AI agents work in the tools you already have: the CRM, email, accounts, practice or property software, the job or field service software, the calendar, the van trackers, shared drives and the spreadsheet the day really lives in. Each connection is one of the approved tools in decision 5.
They add what it doesn’t do: read a scanned form, an engineer’s note or a supplier’s email, see what one delay means for the next three jobs, and work out the fix.
There’s nothing to cancel. Your contracts run their course, and only the work between the tools changes. Where a system has no way in, we say so at the workshop. Sometimes the simplest answer is a small piece of ordinary software, or leaving that step with a person.
Not if your week has more work in it than one person can keep track of. What makes it pay is the number of cases or jobs, not the headcount: a few dozen a day, a few vans or crews, and one person who knows where everything is. The workshop works it out on your own numbers, and if it doesn’t pay, the findings report says so.
You don’t need an IT team or AI engineers either. Our engineers build your AI agents with your people, inside your business, until they’re live. Then your team runs them, with the runbooks and the record, or we run them for you.
Finding out what it would save costs you a day, and AWS funding typically covers the workshop. AWS has funded part of the work for several of our clients.
The scorecard counts what each finished case costs, from the model and the review to the rework and the running, so you see the sum before the week 4 decision. After a go at week 4, we’re paid on the result.
The model is chosen by testing several on your own cases for quality, speed and cost, and plain checks stay as code, which costs almost nothing. It runs in your account, so the bill is yours to see.
For clean fields between two tools, a rules tool is the right answer, and we’ll tell you to use one.
The messy part needs more: a model that reads the scan or the email, a confidence line set on your own past cases, approvals that cover one action, and runs that can’t file twice. That’s the engineering in the six decisions above. Your engineers work beside ours, and keep the code, the tests and the runbooks when we leave.
No. They add to it. AI agents take the typing, copying and chasing. Your people review their work, make the decisions, handle the exceptions and talk to your customers, and each of them gets through far more.
In the example above, Rita checks the 2 cases that need her, instead of reading every page of 8 new clients.
She doesn’t have to, to begin with. It starts by bringing her every decision, with the options worked out, and she says yes or no.
It earns more freedom only on evidence: weeks of approvals with almost nothing changed. When she edits a message or overrides a call, that becomes a test it has to pass from then on.
No. Most businesses we meet have trackers on some vans, a job status the engineers update when they remember, and a supplier who emails. It works from the signals you have, and says how sure it is.
Where a signal is missing, a simple check-in from the engineer’s phone often closes the gap. The scorecard measures it on your own data, so you see what it catches before you rely on it.
It stays in your own AWS account, and each AI agent sees only what its step needs. Every step is on record, so you can show a customer exactly what was done with their details.
Or last month’s worst day on the road. Spend a day with the engineers who’d build it. You leave with a findings report, a target architecture and a costed roadmap. AWS funding typically covers the workshop.