REFERENCE SCENARIO · ECOMMERCE & RETAIL
Reference scenario - composite, not a named client. A composite build illustrating our method. Figures are modeled and the model is shown.
This ecommerce ai automation case study is a reference scenario. No retailer sits behind it. It shows how a catalog of 40,000 items and the ad account spending against it can be worked by agents, and how each figure here is calculated.
What this page is not
There is no client here. We have no consented retailer story to publish, and a page implying otherwise would be a legal problem rather than a marketing one. So the composite is stated up front.
The workflow is real in the sense that we build against this shape. The catalog size, the completeness rate and the minutes per task are inputs chosen to make the arithmetic checkable. None of them is a measurement.
The situation this build answers
A catalog of 40,000 items, fed by suppliers who each describe products their own way. Some arrive with dimensions and no material. Some arrive with a description written for a trade sheet. A few arrive with nothing but a code and a price.
Downstream, that mess becomes a merchandising problem. Filters miss items, search returns nothing, and paid ads run against listings a buyer cannot evaluate.
Where the time actually goes
- Enrichment is per item, and per item is where a large catalog defeats a small team.
- The backlog never clears, because new and changed items arrive faster than anyone works the old ones.
- Ad feeds break quietly. A disapproved item keeps spending against a live campaign until someone checks.
- The weekly ad review is a fixed ritual, so the worst case is always a full week of drift.
- Nobody can say which listings are costing money, because the two systems are read separately.
The hive, agent by agent
SEO Content Agent
Drafts the missing attributes and product copy from supplier data, existing listings and category conventions, and flags the items where it is guessing.
Ad Ops Agent
Checks feed health and campaign structure daily, drafts the changes it would make, and batches them for one human approval.
The orchestrator
Sequences the work. Enrichment runs against the backlog and the daily delta, and an item is not eligible for a campaign change until its attributes pass validation.
The two halves are joined on purpose. An ad agent optimizing spend against a broken listing is optimizing the wrong thing. The service pages behind this build are AI marketing agents and data engineering for AI.
What the agents may not do
Autonomy boundary
Agents draft attributes, copy and campaign changes. They never change a price, never alter stock, and never raise a budget.
Approval gate
Every catalog write and every campaign change is approved by a person in a batch. The agent proposes, a merchandiser accepts.
Uncertainty is declared
Where an attribute is inferred rather than sourced, the draft says so and the reviewer sees it first.
What stays human
Pricing, promotions, brand voice decisions and anything a supplier contract governs.
Logging
Every draft, source and approval is recorded with a timestamp. Read our governance approach for how that is held.
How the build would run
| Phase | Typical | What happens |
|---|---|---|
| Audit | 3-10 days | The catalog is scored for completeness by category, and the ad account is read against it. This produces the real incompleteness rate the model below assumes. |
| Pilot | 2-3 weeks | One category. Agents draft, merchandisers approve, and the review time per item is measured rather than guessed. |
| Backlog | 4-8 weeks | The enrichment queue is worked category by category, newest-selling first. |
| Steady state | ongoing | Daily delta plus daily ad checks. The backlog phase does not repeat. |
How to read this ecommerce ai automation case study
Every figure below is arithmetic on stated inputs. We show the multiplication so you can disagree with an assumption rather than with a claim.
| Input | Assumed value |
|---|---|
| Catalog size | 40,000 items |
| Items with an incomplete attribute set | 18% |
| Manual enrichment time | 9 minutes per item |
| Review time on an agent draft | 90 seconds per item |
| New or changed items | 600 per month |
| Ad review today | 4 hours, once a week |
| Ad approval after | 1 hour, once a week |
The modeled outcome, with the arithmetic
| Figure | The arithmetic | Label |
|---|---|---|
7,200 items to enrich | 40,000 × 18% | Modeled |
1,080 h to clear it by hand | 7,200 × 9 min = 64,800 min | Modeled |
27 weeks of one person | 1,080 h ÷ 40 h a week | Modeled |
180 h to clear it by review | 7,200 × 90 s = 10,800 min | Modeled |
4.5 weeks of one person | 180 h ÷ 40 h a week | Modeled |
~83% less enrichment time | 1 − (180 ÷ 1,080) | Modeled |
75 h returned per month, ongoing | (600 × 9 min) − (600 × 90 s) = 4,500 min | Modeled |
13 h returned per month, ad ops | (4 h − 1 h) × 52 ÷ 12 | Modeled |
1 day worst-case feed detection lag | Daily check, against 7 days on a weekly one | Modeled |
100% of writes approved by a person | Design rule, not a rate | Target |
What we would measure, and what we will not claim
We publish no outcome range for this build. We have no consented retailer result, and a modeled figure is not a result.
What we would change next time
Sequence the backlog by revenue, not by emptiness. The instinct is to fix the worst listings first. The listings that sell are worth fixing first, and they are usually not the same items.
Measure review time in the pilot before committing to it. Ninety seconds per item is the input this whole model rests on, and it is the one most likely to be wrong in your catalog.
The parts this build is made of
AI marketing agents
The agents that brief, draft, watch and report.
Data engineering for AI
Pipelines and validation, because catalog quality is a data problem before it is a model one.
SEO Content Agent
Attribute and listing copy drafted against your own conventions.
Ad Ops Agent
Daily feed and campaign checks with a batched approval.
AI agents for ecommerce and retail
Where else agents pay off in this sector.
