Book a call

Your instincts about your business are good. Imagine backing them with data.

Most retail teams are deciding on numbers that are a week old, from systems that do not talk to each other. We bring your ERP, warehouse and storefront data together on AWS, so what you see is what is happening now.

We're an AWS Advanced Tier Partner with the Digital Workplace and Generative AI competencies, working with retail and consumer brands.

Book a 20-minute call
Trusted by brands you know, built on AWS

The data exists. It just arrives too late to act on.

Sales sit in one system, stock in another, the storefront in a third. By the time someone has reconciled them in a spreadsheet, the week you were reporting on is over.

A week in the life of a merchandising decision
Monday
Last week's numbers get pulled from three systems
Two days of prep
Owner your data teamConfidence reasonable
Wednesday
The report lands, and a best seller has been out of stock since Friday
Five days late
Lost sales already goneReorder starts now
The same week, on a unified data layer
One source, refreshed continuously, same numbers for everyone
Same day
Prep time removedStockout caught Friday
Illustrative, but the shape of it is what most mid-market retail teams describe.

Four reasons retail teams call us

Most arrive with one of these. They tend to be the same four.

One set of numbers

ERP, warehouse and storefront data unified on AWS, so merchandising, finance and operations stop arguing about whose spreadsheet is right.

Reporting that refreshes itself

Stockouts you catch early

Know which best sellers are running down before the reorder point passes, rather than reading about it in next week's report.

The costliest thing in retail is empty shelf on a winner

Infrastructure that handles the spike

Promotions and seasonal peaks scale up and back down again, instead of buying capacity for the busiest hour of the year and paying for it all year.

Pay for the peak, not the year

A foundation AI can actually use

Demand forecasting, personalised recommendations and inventory prediction all need clean, connected history. This is the work that has to happen first.

The unglamorous part nobody sells you

What the work actually involves

A data project, not a dashboard project. The dashboard is the easy part.

  • Source connectionsPulling from the systems you already run, including the ERP that was never designed to be queried by anything else.
  • A data lake on AWSOne place the data lands, with history kept properly rather than overwritten each night.
  • Transformation and modellingTurning raw system exports into tables a human can reason about: product, store, day, channel.
  • Reporting that runs itselfThe weekly ritual of assembling numbers by hand disappears, and everyone reads the same figures.
  • Scale for peakAutoscaling and caching so promotions and seasonal traffic do not take the storefront down.
  • Cost controlRight-sized from the start, and reviewed, so the platform does not quietly become the second biggest line item.
  • The AI groundworkClean, connected, historical data is what forecasting and recommendation models need. Most projects stall here, before any model is chosen.

Short version: we make your own data usable, current, and cheap enough to keep.

Every retail team solves this somehow

Three ways to get to one set of numbers.

All three are real choices. Two of them are what most companies are doing right now.

Keep assembling it by hand

Someone competent exports, reconciles and rebuilds the report every week. It works, and it costs you that person's week, every week.

No project cost · a standing tax on your best analyst · numbers always a week behind

Buy a reporting tool

A BI tool on top of systems that still do not talk to each other. Prettier charts, same underlying problem, plus a licence.

Quick to buy · does not fix the data · often shelved within a year

Fix the layer underneath

Unify the sources on AWS once, then put whatever reporting or model you like on top. Slower to start, and the only one that compounds.

A real project · reporting stops being manual · the foundation AI work needs

If you are weighing AI projects, this is the honest sequence: the data has to be ready before a model is worth choosing. We would rather tell you that now than six months in.

Whether this is worth your time

Being straight about the fit costs us a few calls and saves everyone a wasted quarter.

Worth a conversation

  • Sales, stock and storefront data living in systems that do not talk
  • Someone spending real hours each week assembling the same report
  • Stockouts or overstock you found out about later than you would like
  • An AI or forecasting idea you suspect the data is not ready for

Probably not us

  • A single platform that already holds everything and reports well
  • Looking for someone to run merchandising or marketing rather than the data behind it
  • A data team already doing this work, with no capacity problem
  • No decision path beyond a proof of concept

Twenty minutes, and you'll know if this fits

A conversation with an engineer who has built these, not a discovery call with a salesperson. Here is what happens on it:

  • Which systems hold your sales, stock and customer data today
  • What the weekly reporting routine currently costs you in hours
  • Where the data would need work before it could support forecasting
  • The rough shape of a first phase, and what it would change
  • Whether you are better off leaving it as it is, which we will say if it is true

Not ready for a call? Email sales@cloudlife.io and we'll answer in writing.

20 minutes · with an engineer

Pick a time that works

Open calendar, real availability this week. Bring the names of the systems you run if you have them, and if you don't, that's fine too.

Book a 20-minute call
AWS Advanced Tier Services PartnerAWS Digital Workplace CompetencyAWS Generative AI Competency

Cloud Life Consulting · AWS Advanced Tier Partner · Digital Workplace Competency · Generative AI Competency

Consent Preferences