Do I need to change the tools I already use?

No. We connect to whatever you're already running: Shopify, Amazon Seller Central, Xero, HubSpot, Gmail, Slack, Klaviyo, Google Ads, Meta. We don't replace your tools. We wire them together and put AI on top.

How is this different from Zapier or Make?

Zapier moves data between apps. We build systems that think. Your AI generates branded marketing reports from live campaign data, checks health claims against EFSA regulations, and builds product concept documents from a brief. It's not just automation. It's intelligence.

We're a small team. Is this overkill?

Small teams benefit the most. You don't have spare headcount to throw at admin and reporting. These systems give you the operational capacity of a much bigger team without the payroll. Most of our clients are teams of 3 to 20.

How much does it cost?

Every engagement is different, so we scope and price after the audit call, not before. Most engagements start from £5,000 for the initial build, with monthly retainers from £750. You'll get a fixed-price proposal with no surprises before any work begins.

How long until I see results?

Most brands are live within 2 to 4 weeks. You'll see significant time savings in the first month. By month three, the system is handling tasks you forgot you were still doing manually.

Can you help us get better visibility on our manufacturer?

Yes, this is one of our strongest areas. We've worked inside contract manufacturing and understand the data flows, delays, and communication gaps. We can build you real-time visibility on orders, batches, and timelines without your manufacturer needing to change how they work.

What if I want to cancel?

Cancel anytime with 30 days notice after the initial build. Everything keeps running, you just won't get ongoing optimisation. But most clients stay because the system keeps getting more valuable.

How a UK Supplement Brand Cut Reporting Time by 80%

This is based on a real engagement with a UK-based supplement brand. Details have been anonymised, but the numbers and the process are genuine.

The Brand

A sports nutrition and wellness brand. 35 SKUs across capsules, powders, and ready-to-drink formats. Three sales channels: Shopify DTC, Amazon FBA, and a growing wholesale account list of about 15 retailers and gym chains. Annual revenue in the low seven figures. Team of six, including the founder who was still involved in day-to-day operations.

The Problem

Reporting was eating the team alive. The operations manager spent roughly 12 hours per week on data tasks. Here is what that looked like.

•Monday morning: 2 hours pulling Shopify and Amazon data into a master spreadsheet. Manually reconciling orders, returns, and fees.
•Tuesday: 1.5 hours updating stock levels across all channels. Cross-referencing FBA inventory with 3PL stock and any incoming production runs.
•Wednesday: 1 hour preparing a mid-week sales summary for the founder.
•Thursday: 2 hours on wholesale account management. Checking reorder patterns, updating a separate spreadsheet with account status.
•Friday: 1.5 hours compiling weekly performance data. SKU-level sales, channel comparison, ad spend summary.
•Ad hoc: 4 hours across the week responding to "can you pull the numbers on..." requests from the founder and marketing.

The spreadsheets were complex. Multiple tabs, manual formulas, copy-paste from different data sources. Errors crept in regularly. The ops manager once discovered a formula error that had been understating Amazon revenue by 8% for three months.

What We Built

The solution was a custom reporting system with three components.

Automated Data Pipeline

Shopify and Amazon data pulled automatically via API every night. Orders, returns, inventory levels, and advertising data all flowed into a central database. Wholesale orders were ingested from a simple upload form, replacing the manual spreadsheet updates.

Unified Dashboard

A single dashboard showing all three channels side by side. Revenue, units, and margin at the brand level and the SKU level. Stock position across FBA, 3PL, and in-production. Week-over-week and month-over-month trends. The dashboard updated automatically. No manual refresh needed.

Automated Reports and Alerts

A Monday morning email with the weekly summary, formatted and ready to share. Automatic alerts when stock for any SKU dropped below the reorder threshold (calculated from rolling 30-day velocity and lead time). A monthly report for the founder covering P&L by channel, top and bottom performing SKUs, and stock health.

The Results

The impact was immediate and measurable.

•Reporting time dropped from 12 hours per week to under 2.5 hours. The remaining time was spent reviewing and adding commentary, not pulling data.
•The formula errors disappeared. Automated data pipelines do not fat-finger a VLOOKUP.
•The founder got the information he needed without asking. The Monday email and the live dashboard replaced most of the ad-hoc requests.
•Stock decisions improved. The reorder alerts caught two potential stockouts in the first month that would have been missed under the old system.
•The ops manager redirected roughly 10 hours per week into supplier negotiation, wholesale account development, and NPD support.

What Surprised Them

Two things the team did not expect.

First, the data quality improvement was more valuable than the time savings. When you pull data manually, small errors accumulate. A missed return here, a wrong date filter there. Over time, decisions get made on slightly wrong numbers. Automated systems pull the same data the same way every time. The team started trusting their numbers again.

Second, visibility changed behaviour. When everyone could see SKU-level performance in real time, conversations shifted. The marketing team started looking at which products had margin headroom for promotions. The founder spotted a wholesale account that had quietly stopped reordering. The ops manager identified two SKUs that were consistently over-ordered and adjusted the reorder logic.

The biggest win was not saving 10 hours a week. It was making better decisions because the data was always available, always accurate, and always current.

Timeline and Investment

The system was built and deployed in three weeks. Week one: data pipeline setup and testing. Week two: dashboard build and report configuration. Week three: team onboarding, refinement, and parallel running alongside the old spreadsheets.

The investment was less than the annual cost of two mid-tier SaaS tools the brand had been paying for but barely using. Those subscriptions were cancelled.

If your team is spending more time compiling data than acting on it, the maths on automation is straightforward. The question is not whether it is worth it. It is how much longer you can afford to wait.