Author: AP

Every trading business has a back office that nobody quite has time for. Prices need reviewing every week. Product pages drift out of date. Somebody should know which products will run out before the next delivery lands. And what your suppliers have agreed is usually sitting in someone’s inbox.

We automated five of those jobs inside a real UK equipment supplier with a team of 20 to 30 people. The AI does the reading, checking and number crunching. A person approves every change. Here’s what we did, step by step, so you can try it yourself.

Start with one list: the products that matter

Before anything else, we built a monthly ranking of every stocked product by gross profit over the last 12 full months, in bands: Top 50, Top 100, Top 200 and Top 500. Spare parts, drop ship lines and anything not active are left out. A product only moves band after two months in a row on the new side of the line, so the list doesn’t flicker about.

The other jobs use it: the stock forecast reports on the Top 50, 200 and 500, and the supplier log uses it to decide which products are key. Before anyone relied on it, it had to match the list the business had signed off by hand. It matched line for line.

“Here is 12 months of completed sales by product with gross profit, plus our stock list. Leave out spare parts, drop ship lines, inactive products and anything with zero or negative gross profit. Rank the rest by gross profit in pounds and band them Top 50, Top 100, Top 200 and Top 500. Flag any products within £1 of each other at a band line, and tell me what you left out and why.”

A weekly price review you approve, not build

Every Wednesday the AI reviews every product’s sales speed, margin, stock and cost against written rules. Slow sellers come down 5%. Fast sellers and thin-margin lines go up 5%. Nothing moves more than 5% a week, nothing moves twice within six days, and clearance stock steps down each week it doesn’t sell, but never below a hard floor.

It then writes an import file for the ERP and emails the director a summary, including what the month’s blended margin will look like before and after the changes. He checks it and uploads it himself. The AI never touches live prices. The first two batches were approved and uploaded within a day of each other.

A Friday check of every product page

Product pages collect mistakes: warranty text that doesn’t match the real warranty, “free delivery” on products that aren’t, blank dimensions, gross weights where net weights belong. Every Friday the AI checks every stocked listing against the business’s own reference data. Each issue gets a priority and a confidence. Confident fixes are proposed for approval, and everything else becomes a question for a person.

The first full audit raised 12 questions, which were answered the same day. The approved fixes went live the next morning and every change was logged with the old value, so any of it can be rolled back. A recheck the day after found the fixes had held.

Fix purchase order dates before you forecast stock

A stock forecast is only as good as the dates on your purchase orders. So every other Friday the AI first compares the ERP’s order dates with the buyer’s own tracker, and lists what needs changing. A person changes the dates and replies “done”. Only then does it forecast how many key products will be in stock now, and in one, three and six months, and which will run out before the next order arrives.

The first run found 24 orders that needed re-dating. It also found that the open orders report everyone relied on was missing 37 live orders, because its filter only showed orders marked “Open”. That one check fixed a report the whole forecast depended on.

“Here are our open purchase order lines from the ERP and the buyer’s tracker. Read dates as UK format. First list any typos in the tracker, without guessing them. Then list every order where the ERP date differs from the tracker’s arrival date: order, supplier, current date, new date and units. Don’t change anything.”

One place for what suppliers have agreed

Each week the AI reads the sourcing agent’s updates and logs them into one supplier workbook: payment terms, quality issues, quotes and price cuts. It takes a dated backup first, never overwrites history, and logs unconfirmed terms without counting them. It then works out how much cash the agreed terms free up, clearly labelled as an annual estimate.

Your starter checklist

1. Find the one report or workbook that already pulls sales, stock, costs and orders out of your ERP.
2. Build your product ranking first and check it against the list you use today.
3. Write your pricing rules down, with a reason for each, before you automate anything.
4. Agree which source wins when product data disagrees: manual, supplier, ERP or website.
5. Check your open purchase order report includes orders awaiting approval and orders not yet sent.
6. Back up every workbook before the AI changes it, and log every change.
7. Make sure a person approves every price, listing and order date change.
8. Write a short handover for each job so anyone can run it.

One safety point: use a business account for your AI assistant and check its data settings before sharing customer, supplier or financial details. Claude, Microsoft Copilot and ChatGPT all offer business plans.

Build it yourself, or have us build it

If you’d like to build this yourself, our guide The Trading Back Office covers all five automations in detail: the rules and why they exist, every check, the problems we hit, 21 copy-ready prompts and the procedures to adapt.

If you’d rather we did it, our Trading Back Office Setup builds it inside your business, connected to your own ERP, shop and supplier data so nothing is keyed in. It’s £2,495 + VAT, fixed price, done in three weeks.

We’re not IT people. We’ve sat in the MD’s chair and built the systems behind it, and we’ll talk to you in plain business terms. Not sure where to start? Take our free Growing Pains Check to see where your business is under the most strain.

See how the workshops work →

Or book a free 30-minute call with us →

Keying a retailer’s orders into your own system. Sending shipping and invoice details back through their portal. Setting up a new trade customer online with the right discount. We automated two of these jobs inside a real UK equipment supplier that fulfils orders for a national retailer, using an AI assistant working through the same screens a person would. Here’s how, so you can try it.

What we automated

1. Daily dropship orders. A national retailer that sells some of the business’s products online sends its orders through the retailer’s supplier portal. Every day someone had to raise each order in the ERP, ship it, then go back to the portal with the carrier, tracking number and invoice number. Now one line, “complete the daily run”, does all three phases. On its first clean run it shipped two orders, invoiced two and raised two new ones in 16 minutes, across both systems. The team reads a summary and deals with anything flagged.

2. New trade accounts. When a trade customer wants to order online at trade prices, a sales colleague emails the director. Now the director types “set up [customer]”. The AI reads the email thread, sets up the account and discount on the online shop platform, sends the login invite, checks it’s live, and leaves a reply as a draft in Outlook. A person reads it and sends it. Eight trade accounts have been set up this way so far.

No code was written for the order run. The AI works in the business’s own browser after a person signs in. It never signs in itself.

Step 1: learn it live, with the person who owns the job

Don’t write the process up front. Sit the AI next to the person who does the job and run a real case, one step at a time, with them watching and correcting. On the very first day, the owner caught two things no written guide would have: a standard description field that was missing, and a button the AI must never press, because a separate system does that job overnight.

Try this:

“I’ve signed in to [portal] and [system] in this browser. Take the first new order only. Before each step, tell me what you’re about to do and which value you’ll use, and wait for me to say OK. Never sign in, never press [risky button], never guess a value. At the end, list anything in our notes that was wrong or missing.”

Step 2: write every correction down, straight away

Keep one plain-English process document: the steps, the rules, the price list, the odd behaviour of the screens, and worked examples. The AI reads it at the start of every run. If a fix isn’t written in, it gets made again next time. Over about five weeks, the order run went from supervised to running a full day unaided.

Give every rule a reason. “Delivery charge = delivery price × quantity, because delivery is priced per unit” is much harder for someone to delete by accident than a bare rule.

Step 3: tell it what to do when it’s unsure

The most important rule in both automations is never guess. If a value isn’t on the order, in the price list or in the system itself, the AI stops on that item, flags it and moves on to the next. One problem order never holds up the rest. In the first five weeks this caught a duplicate order, an order with no shipment behind it, a gap between our price and the retailer’s total, and three stock shortfalls. All went to a person.

It also caught something you might not expect: on the first clean run, the system’s own default prices on both new orders were well above the agreed list price. Because the process names one price list as the only source of truth, both were corrected.

Step 4: save it so one line runs it

Most AI assistants, including Claude, Microsoft Copilot and ChatGPT, let you save a set of instructions so one short phrase runs the whole procedure the same way every time. Point them at your process document so you update one place.

For the trade accounts, the setup on the shop platform did a lot of the heavy lifting. The platform only allows four automatic discounts to be active at once, so most customers share one discount at the standard trade rate, applied to a product group that automatically leaves out delivery, warranties, service and dropship lines.

“Search my inbox for the latest email from [colleague] about [customer]. Read the whole thread, including attachments. Give me the customer name, contact email, rate and any special terms, and anything unclear. Don’t change anything yet.”

Step 5: keep a person on the last step

The AI handles the routine. People make the decisions. Duplicates, stock shortfalls and price gaps go to the process owner. Customer records are only ever added to, never overwritten. Every email is a draft that a person reads before it goes.

Your checklist

1. Pick one job that’s frequent, rule-based and spread across two systems.
2. Time it by hand for a week, so you know your “before”.
3. Run three real cases with the owner watching.
4. Write every correction into one process document, with a reason.
5. Write the risky actions as hard “never” rules.
6. Tell the AI to flag and move on whenever it’s unsure.
7. Name one source of truth for prices.
8. A person signs in to every system. The AI never does.
9. Drafts, never sends.
10. Save a report and a log line after every run.

One safety point before you start: use a business account for your AI assistant and check its data settings before sharing customer or financial details.

Build it yourself, or have us build it

If you want to build it yourself, our guide Orders and Accounts Without the Admin has every step, rule and check from both automations, the problems we hit and how we fixed them, 20 copy-ready prompts and the two procedures we actually use, ready to adapt.

If you’d rather have it done for you, our Order Admin Setup builds it inside your business and connects it to your own systems, so nobody re-keys data. £1,495 + VAT, fixed price, done in three weeks.

We’re not IT people. We’ve sat in the MD’s chair and built the systems behind it, and we’ll talk to you in plain business terms. Not sure where admin is costing you most? Take the free Growing Pains Check.

See how the workshops work →

Or book a free 30-minute call with us →

Every Monday morning, in a lot of small businesses, someone opens the help desk, counts last week’s tickets, goes through the review sites, works out an average and types it all into an email. Then the same figures get typed again into the leadership scorecard. It’s slow and easy to get wrong.

We automated both of those reports inside a real UK equipment business with a busy customer service inbox. Here is what we did, so you can do the same.

What runs now, every Monday

Just after 8am, an AI assistant pulls last week’s figures from the help desk: replies sent and tickets resolved by each person, how long new customers waited for a first reply, and how many customers are waiting on us right now. It emails a summary to the customer service lead and checks in two KPIs on the leadership scorecard.

At about 9am, it reads the live review sites, counts new reviews by star rating, checks every figure against a second source, and writes the weekly reviews email in the layout the team already knew. Then it logs two review KPIs on the scorecard.

Nobody has to do anything, unless a check fails.

Step 1: agree what every number means

This was the most important step, and it had nothing to do with AI. “Last week” and “response time” can each be worked out several ways. The help desk’s built-in “last week” ran Sunday to Saturday, for example.

So we wrote the rules down. Last week means Monday 00:00 to Sunday 23:59, UK time. First response time is measured on tickets created last week, in clock hours, because a customer’s wait doesn’t stop at 5pm on a Friday. The AI agent that already answered some tickets is kept out of the team totals, because it replies in seconds and would flatter everyone.

Here is a prompt you can use to do the same for your own report:

“I want to automate a weekly report on [topic]. Interview me one question at a time until every figure in it has a written rule: what exactly counts, what is left out and why, which dates last week covers, which system it comes from, and how it should be shown. Then write a one-page definitions sheet in plain English, with a reason next to each rule.”

Step 2: read live, from two sources where you can

Early on, one week’s reviews email repeated the previous week’s figures. The page that had been read was an old cached copy. That one mistake shaped the whole design. Now every figure must come from the live sites during the run, never from search results, old emails or memory. The public review page is cross-checked against the review site’s business app, review by review.

Step 3: check before anything is sent

Before an email goes, the totals must add up, each person’s row must match the raw data, and the two sources must agree. Then a separate checker reads the finished email against the raw data. This is the prompt for that second check:

“You are checking a report written by someone else. Here is the draft email [paste] and the raw data it was built from [attach]. Compare every number, name and date in the email with the raw data. List anything that doesn’t match exactly, or anything the data doesn’t support. Do not rewrite the email. Reply ALL CHECKS PASSED only if you find nothing.”

Step 4: draft first, send later

Both reports ran as drafts until a person had checked a full week line by line. Only then were they allowed to send on their own, and only when every check passes. If anything is off, the email is saved as a draft with “Please check” at the top, and one short note explains why.

Step 5: log it to the scorecard without treading on anyone

Straight after each email, the run checks in that week’s KPIs on the leadership scorecard. It never overwrites a figure a person has already entered: it leaves it and reports both numbers. After checking in, it reads the value back to make sure it landed in the right week.

What the first week showed

The reviews report’s first live run went from start to finished draft in about five minutes, with no corrections to the figures. Both review sources matched on every review, and it turned up four follow-ups for the team, including two older one-star reviews that still needed attention.

The customer service summary showed a team that replies quickly on most tickets, but with a large backlog of customers waiting, much of it parked on the AI agent’s own account, plus open tickets with no owner. These are specific, fixable things, which is exactly what a weekly report is for.

Your checklist

1. Pick one weekly report that someone does by hand.
2. Write a rule for every figure in it, with the reason.
3. Connect the AI to your help desk and email, and sign it in to anything it needs to read.
4. Make sure it only reads: it never replies to, changes or closes anything.
5. Add checks: totals add up, sources agree, a second checker reads the email.
6. Run it as a draft until a full week needs no corrections.
7. Let it send only when every check passes, and hold a draft when they don’t.
8. Log the figures to your scorecard, without overwriting anyone.
9. Keep the instructions and a run log where the team can see them.

One safety point: use a business account for your AI assistant and check its data settings before sharing customer or financial details. Claude, Microsoft Copilot and ChatGPT can all do this kind of work; we used Claude.

Build it yourself, or have us build it

If you want to build your own, our guide Customer Service on Autopilot has every rule, every check and what happens when it fails, the problems we hit, 20 copy-ready prompts, and the full scheduled instructions for both reports.

If you’d rather have it done for you, our Customer Service Reporting Setup builds both reports inside your business, connected to your own help desk and review sites, and logged to your scorecard. It’s £795 + VAT, fixed price, done in two weeks.

We’re not IT people. We’ve sat in the MD’s chair and built the systems behind it, and we’ll talk to you in plain business terms. Not sure where to start? Take our free Growing Pains Check.

See how the workshops work →

Or book a free 30-minute call with us →

If you sell online and to trade customers, money leaks in the gaps between your systems. A refund done in the online shop but never recorded in the accounts. A customer who paid twice and is still waiting. A return nobody chased. Each system looks fine on its own. The problem is that nothing joins them up except people’s memory.

We recently automated three of the jobs that catch those leaks inside a real UK equipment supplier selling online and to trade: the monthly debtors review, the refund tracker and the returns process. Here’s what we did, and the steps you can copy.

What we found when we checked properly

The first time the AI tied the debtors report back to the accounts system, it found 9 customers had dropped out of the hand-built analysis. Nobody knew, because the total had never been checked against the source.

It also found old refunds marked as “bank transfer” in the online shop that had never reached the bank refund run. Ticking “bank transfer” only meant someone had marked it, not that money had left the bank. Those customers were most likely still owed. And it found the opposite problem too: refunds already paid but still showing as open in the accounts, which is exactly how businesses end up paying the same customer twice.

On the refund tracker, comparing every shop refund and every credit note with the spreadsheet turned up more than 200 missing refunds, 16 wrong amounts and 36 duplicates. On returns, the first week of runs found 7 jobs that had already been done and skipped them, instead of doing them twice.

Step 1: write the process down

Before any AI, write down how the job works today: who does each step, which systems they open, what they look for and what they must never do. The AI follows that document, not memory. Every time you correct something, add it to the document the same day. In our returns process, that one habit meant the same mistake never happened twice.

Step 2: check against the source, not the spreadsheet

A spreadsheet that has only ever been checked against itself will always look right. The real gaps only show up when you compare it line by line with the systems the money actually moved through: your online shop and your accounts system. Start with your debtors. Does the report total match the debtors control account to the penny? If not, find out why before doing anything else.

You can start with an AI assistant such as Claude, Microsoft Copilot or ChatGPT and a couple of exports. Try this:

“Here is our aged debtors report as at [date] and our trial balance for the same date. Check the report total equals the debtors control account. If it doesn’t, find the difference: customers missing, documents posted after the report was run, or unposted batches. Don’t do anything else until we agree it ties.”

Step 3: sort every line by rule, then cross-check

Most lines on a debtors report need nothing: store credits that show in both systems, trade invoices still within terms, web orders paid today. Agree those rules with whoever owns the debtors, and let the AI sort every line into “needs action” or “no action”. Then, for each action item, check the online shop (was it sent, was it refunded, how was the refund paid?) and your bank refund sheet (paid, waiting for the next run, or never added?).

Finally, ask for a suggested fix for every line, grouped by type, so all the “raise a credit note” items can be done together. The most important group is the first one: refunds already done somewhere else. Record them, and do not pay again.

“For each web order on the action list, check the online shop: sent or not, refunded or not, and how the refund was paid. For any refund marked as a bank transfer, find it on our refund sheet by order number, name and amount, and say whether it’s paid, waiting, or missing. Read only: don’t change anything.”

Step 4: keep the people steps

In all three automations, a person signs in to each system, a person clicks Refund, and nothing in the accounts is changed until the finance lead approves it. The AI never types a password. It reads everything, checks everything and suggests the fix. That’s what keeps the risk low and makes the team comfortable trusting it.

Step 5: check before every action, and leave a note

Has this already been refunded? Has this job already been done? Did we email this approver today? Every run should check before it acts. Take a dated backup before every save, and leave a short carry-over note at the end of each run so the next one starts exactly where the last one stopped. On our refund tracker, someone saved an old copy over the updated file eight minutes after the AI finished. The backup is the only reason nothing was lost.

Your quick checklist

1. Does your debtors total tie to the control account to the penny?
2. Is every refund in your online shop on your refund tracker, once, at the right amount?
3. Are refunds marked “bank transfer” actually on your bank refund run?
4. Is anything open in the accounts that has already been refunded?
5. Has every customer who paid twice been refunded?
6. Does every return have a note saying who was asked and when?
7. Has the stock from every refunded return been booked back in?
8. Is there a backup before every change to your shared spreadsheets?

One safety point: use a business account for your AI assistant and check its data settings before sharing customer or financial details.

Build it yourself, or have us build it

If you want to build it yourself, our guide Get Paid and Stop Refund Leaks walks through all three automations: every rule, every check, the problems we hit, 21 copy-ready prompts and the full run procedures.

If you’d rather have it done for you, our Cash and Refunds Setup builds the debtors review, the refund tracker check and the returns run inside your business, connected to your own systems, with your team trained to run it. It’s £1,495 + VAT, fixed price, done in three weeks.

We’re not IT people. We’ve sat in the MD’s chair and built the systems behind it, and we’ll talk to you in plain business terms. Not sure where to start? Take the free Growing Pains Check.

See how the workshops work →

Or book a free 30-minute call with us →

Most owner-managed businesses know their sales by channel. Very few know their profit by channel, let alone by customer or by salesperson. Which channel really pays its way once delivery, refunds and advertising are counted? Which customers cost you money? Is each salesperson covering their own cost?

We built exactly that inside a multi-million-pound UK trading business. It pulls straight from the ERP and the accounts system, refreshes in one line when the month closes, and comes with a planner that prices every “what if?” in pounds. Here’s how it works, and what it changed.

Turnover lies: look at profit in four layers

A channel with the most sales can make the least money. You only see it when you peel profit back in layers:

1. GP1: sales less cost of goods. Are we pricing right?
2. GP2: less credits, refunds and delivery. Do we keep the margin once orders go out?
3. Contribution: less the costs a channel causes, like advertising for the website or the sales team for trade. Does the channel pay its way?
4. Net profit: less a fair share of overheads. After everything, is it worth it?

Prove it before anyone trusts it

A colleague had built the first version by hand for eight months. It answered the right questions, but had to be rebuilt every month. We wrote the method down first, then set one rule: the automated version had to reproduce those eight months line for line before anyone used a number. It did, to within about £1, and that match did more for trust than any presentation. Every refresh now runs 45 checks that must all come out at £0.00.

Plugged into the ERP, so nobody keys data

The ledger comes straight from the accounts system and the order data from the ERP, which already refreshed a reporting workbook every morning. A person signs in and approves the downloads. Nothing is typed in by hand. When the month closes, someone types one line: “refresh the profit by channel for October”.

The column that changes the conversation about customers

Share every cost out and some customers look loss-making. But fixed costs, like the sales team and the warehouse, don’t leave with a customer. So next to each customer we show the profit you’d actually lose each month if they left. Some customers at the bottom of the table are still paying towards the rent. The right question becomes “what would make them profitable?”: price, minimum order or delivery charges.

“For each customer in this P&L, split their costs into those that would disappear if they left and those that would stay. Add a column for profit lost per month if they left, and rank customers by it.”

The meeting where it paid for itself

We replaced six scenario spreadsheets with one planner that starts from actual results and prices every lever in pounds a month. The surprise: a 10% rise in sales was worth less than a one-point cut in credits, because advertising grew with web sales. Improving return on ad spend was worth more than either. The conversation moved from “sell more” to margin and efficiency, and the leadership team agreed a set of cost changes and a break-even plan with four measurable targets in a single meeting.

“Using the planner, work out what each of these is worth to net profit per month on its own: margin up 1 point, credits down 1 point, sales up 10% at the same mix, return on ad spend up 1, fixed costs down 10%, and losing each of our five biggest customers. Rank them.”

Use a business account for your AI assistant and check its data settings before sharing financial details. Agree your method with your accountant.

Build it yourself, or have us build it

Profit You Can See is our full playbook on how we built it: the method, every check, the problems we hit and 22 copy-ready prompts. It comes with the Cooper Vance Profit Planner, an Excel workbook with profit by channel, customer and salesperson, a scenario planner (lose a customer, raise margin, cut direct costs) and break-even and target sales, loaded with an example business so you can try it straight away.

Rather have it done for you? Our Profit by Channel Setup is £2,495 + VAT, fixed price, done in two weeks: we connect it to your ERP and accounts system so no data is keyed in, prove it against months you’ve already reported, set up the planner and a one-line refresh you run on demand, and train your team.

We’re not IT people. We’ve sat in the MD’s chair and built the systems behind it, and we’ll talk to you in plain business terms. If profit is a symptom of a bigger growing pain, take the free Growing Pains Check or look at our Growing Pains Diagnostic.

See how the workshops work →

Or book a free 30-minute call with us →

In most growing businesses, the Monday sales report is somebody’s job. They pull an export, paste it into a spreadsheet, fix the bits that look odd and email it round. It takes a couple of hours, it depends on one person, and when it’s late or wrong, nobody finds out until a decision has already been made on it.

We’ve automated exactly this inside a multi-channel UK business selling online and to trade. The report now builds itself every Monday and lands as a draft email for a person to check and send. Here’s how it works, and the one thing that matters more than the AI.

Start with the rules, not the tool

Before anything was automated, we wrote down in plain English what “sales” meant. Which orders count, which date they count on, what’s left out, VAT, how channels are split and who owns each customer. It sounds dull. It’s the most valuable hour in the whole project.

In our case, writing the rules down showed that an existing report everyone trusted had been missing a clean-up step. Fifteen customers who had moved to ordering online were still being counted as trade customers: a five-figure amount of sales credited to the wrong team. No AI found that. Writing the rules down did.

“I’m going to explain how we work out our weekly sales figures. Ask me one question at a time until you can write a one-page rules table: what counts as a sale, which date we use, what’s excluded, VAT, how channels are split, who owns each customer, and what must always add up. Flag anything I’ve left vague.”

If you haven’t yet got everyone working from one set of numbers, start with our guide to getting one set of numbers you trust.

The five checks that stop bad numbers going out

Automation without checks just gets you wrong numbers faster. Our weekly report won’t produce a draft unless every one of these passes:

1. The data is fresh. If it doesn’t reach last Sunday, it waits and tries again later in the day, then tells the owner rather than sending old numbers.
2. One source only. It reads one named file, never an old copy.
3. The sums add up. Channels add to the total and salespeople add to trade sales, to zero difference.
4. It’s a draft. A person always reads it and presses send.
5. It logs every run, so you can see what was reported and when.

Before we trusted it, we recounted a week independently. The totals matched to within a penny across hundreds of orders.

What it costs you today

Typical example: one person spending 4 hours a week pulling, checking and sending sales reports, at £20 an hour, is about £4,160 a year, before counting the time spent arguing about which figure is right. Swap in your own numbers.

Use a business account for your AI assistant and check its data settings before sharing customer or financial details.

Get the full playbook

This is one part of our new playbook, Numbers You Trust Every Monday. It covers the weekly sales report, a daily sales file for your team, weekly KPIs with commentary and monthly profit by channel, with 16 copy-ready prompts, every check we built in and a 30-day rollout plan. All of it is running in a real business today.

We’re not IT people. We’ve sat in the MD’s chair and built the systems behind it, and we’ll talk to you in plain business terms.

Rather have it built for you? Our Sales Report Setup does it on your own data in about two weeks. And if the numbers are a symptom of a bigger growing pain, take the free Growing Pains Check or look at our Growing Pains Diagnostic.

See how the workshops work →

Or book a free 30-minute call with us →