Are you going to replace my team?

That's neither the aim nor what works. We automate the repetitive part and the team keeps the work that needs judgement: negotiation, product, pricing decisions and customer relationships.

Which models do you work with?

Whichever fits the case, with the integration layer decoupled so it can be swapped without rebuilding the project. Choosing a model is a cost and quality decision, not an article of faith.

What happens to my data?

We define what leaves and what doesn't before a line of code is written: minimum data, no unnecessary personal information, an agreed retention period, and deployment options if your policy demands them.

What if the agent makes something up?

It answers only from your catalogue and documentation, with the source shown. What isn't there, it says so and escalates. On top of that there's a test suite and a log of every conversation so it can be audited.

How long does a project take?

A specific case in production is a matter of weeks. What takes time is the data: if the catalogue is messy, it gets cleaned first, and we say so from the start.

That's what we recommend: one process, measured, with a proven return. Expanding is a decision made with the numbers from the first one on the table.

Applied artificial intelligence

AI that executes. And a person who answers for the result.

We build agents and automation on your real catalogue and data: listings, support, forecasting and pricing. With traceability, defined limits and a human strategist signing off the outcome.

Free audit

Let's talk

9Deliverables across agents, catalogue, forecasting and training

3 levelsApprove, delegate or govern: you decide how much it executes

TraceableEvery answer and every change is logged with its source

The problem

Three signs AI is costing you more than it saves

Pilots that never reach production

One-off experiments in a spreadsheet, with no catalogue integration, no permissions and nobody accountable when it breaks.

The catalogue gets translated by hand

Thousands of SKUs, several languages and incomplete attributes. Every new market becomes a manual project nobody wants to start.

A chatbot that annoys your customers

It answers with something it made up or a two-year-old PDF, doesn't know whether there's stock, and can't hand the conversation to a person.

Deliverables

What we build

09 What's included

Support agent grounded in your catalogue

It answers with real stock, lead times, compatibilities and returns policy, cites its source, and escalates to a person when it should.

Bulk listing generation and translation

Titles, bullets and descriptions per market, in your tone, within each channel's limits and reviewed by a human before publishing.

Catalogue enrichment and normalisation

Assisted PIM work: complete vertical attributes, consistent units, duplicates flagged and gaps ranked by visibility impact.

Semantic search and recommendations

Search that understands intent rather than the exact word, with recommendations by behaviour and by product compatibility.

Demand and stock forecasting

A model built on your history, seasonality and promotional calendar, with stockout and overstock alerts per SKU.

Rule-based dynamic pricing

Pricing inside bands you set, with a margin floor, competitor monitoring, and every change logged with its reason.

Review and ticket triage and replies

Sorting by reason and urgency, draft replies, and product issues spotted before they turn into returns.

Automated reporting and alerts

The warnings nobody sees today arrive by email or Slack with the number, the reason and the suggested action.

Rollout and team training

Use cases, templates, usage limits and a data policy, so AI doesn't depend on the one person who knows how to use it.

We decide what goes in, and in what order, during the audit.

How we work

How it gets rolled out without breaking anything

01Step
Process and data mapping
What repeats, how many hours it costs and what data it needs. Three or four cases with a clear return come out, and we drop the rest without regret.
02Step
Prototype on real data
Built on your catalogue, not a demo dataset. Within a couple of weeks you either see it working or we drop it.
03Step
Evaluation and limits
A test suite, accuracy measurement, an explicit list of what the agent must not do, and the escalation route to a human.
04Step
Integration and go-live
Connected to the store, the PIM, the ERP and the support channel, with permissions, activity logging and a rollback plan.
05Step
Governance and continuous improvement
Error review, instruction tuning, cost per operation, and more autonomy only once the data supports it.

Metrics

What we answer for

Hours freed per month: Team time that stops going into repetitive tasks. Measured before and after, with the process actually timed.

Resolution rate without a human: Conversations or tickets the agent closes properly on its own. Audited monthly against a hand-reviewed sample.

Accuracy and escalation rate: The share of correct answers and the share handed to a person. A healthy escalation rate is judgement, not failure.

Catalogue attribute coverage: The share of fields filled and normalised. It's what decides whether you get found and whether you get recommended.

Forecast error (MAPE): Average deviation between forecast and actual demand. It tells you whether the forecast is good enough to buy on.

Cost per operation: What each answer, listing or report costs to generate. Without that number, AI is spending with no ceiling.

These are the metrics you'll see every month. Your starting point sets how far each one goes.

Questions

What people ask us before starting

That's neither the aim nor what works. We automate the repetitive part and the team keeps the work that needs judgement: negotiation, product, pricing decisions and customer relationships.

Keep going

What it pairs with

Custom platforms

For when off-the-shelf software runs out of road.

View service

Amazon and marketplaces

Account, catalogue, advertising and logistics run as one system.