Not an app. An end-to-end partner
built on our own data.

On top of our own connected data and strong infrastructure, we don't just hand you the data. We present and prepare it properly, personalized, in the format you need. Always in collaboration with you.

Four pillars, one engine.

Each item is expanded on its own slide.

Never blind anymore.

Who sells your brand, where and at what price: the real price after discounts, vouchers and bundles. Not the sticker price, but the one the customer actually clicks through to.

This is the foundation, not the whole product. The value begins with reading the data (Pillar 2, Analytics).

The same bottle, 8 prices.

One exact SKU: 1 Million Eau de Toilette 100 ml, boxed and new (no Parfum, Elixir, Lucky, other sizes, sets or testers). Every offer on each portal counted once per seller on 15 September 2026, item price without shipping, FX ECB. Seven offers whose own shop page contradicts the portal (price, stock, tester) or shows counterfeit signals are left out. Median, not average, so a single outlier does not move the line.

The sticker price is not the selling price.

In every market, the cheapest seller of the same 100 ml bottle today, next to the recommended retail price the key retailers display for it.

Cheapest seller = cheapest offer for exactly this bottle across all sellers on the price-comparison portal, checked against the seller's own page (slide before) · RRP = the lowest RRP a key retailer displays for the 100 ml, 24 of 30 key retailers with a captured price in 8 markets · page capture on file for every price.

Germany: 42 sellers, one bottle.

€57 to €128 · 40 of 42 below the €119 RRP (douglas.de) · 22 cheaper than the €75 median

Every seller with a new, in-stock 100 ml offer on idealo.de, cheapest first · bold = key retailer · bars from €40 · offers the seller's own page contradicts left out.

It's not that we have data.
It's that we read it for you.

Temperature check = one score per product and market, combining three views into a single thermometer:

The market, or just me?

Part of Analytics, read for sales teams and management.

We see who is driving the price spiral down.

Not a single snapshot: a pattern over time. And we distinguish two types of players you can't tell apart today:

No hunting through reports.
You get a to-do: fix this.

Cherry-picking with concrete, explicit examples: sellers and countries across all of Europe. Not an aggregate, not a spreadsheet to decode.

You pick from the mass
what is suspicious to you.

Discovery and Analytics (Pillars 1–2) give the reading to everyone. The Workspace is for those who actually work with brand protection, and curate what to watch more closely.

Primarily for brand-protection people (partly legal). Not for management, they have monitoring.

Cherry-picked sellers as live cases,
not dashboard filters.

I watch specific shops: do they actually stop dumping? No need to remember, click through a dashboard, and re-apply filters. I have it as an assigned case / a real task.

It feeds fresh data to each case, always current, never a snapshot that goes stale.

When you go on the attack,
we collect the evidence for you.

The moment you switch a case to "active attack", the Workspace starts automatically building evidence for Pillar 4 (Enforcement):

From a warning to court.

It stands on evidence built in Pillar 3 (screenshots, timestamps, the case file). The client chooses how far to go.

Partner law firms across all of Europe.

It's not one selected country. We're building a network of partner law firms across European jurisdictions. The warning and the dispute proceed correctly under local law, wherever the seller is.

One engine powers all four pillars.

The same self-healing engine feeds Discovery (it sees), Analytics (it reads), Workspace (it processes) and Enforcement (it acts). The technical foundation beneath the consulting, not a box you buy. Running in production, today.

AI does the heavy lifting under the hood. The point is the outcome: all four pillars stay live.

The European map is built.
The engine is switched on.

We map the market first, then put the shops that sell products like yours on a price schedule in waves (see next steps). Adding a market or a category is a setting, not a rebuild. 5 months of price history, 131 M price points. Figures as of 15 September 2026.

In 60 days, full monitoring
of one brand's portfolio.

Pick one brand. In 60 days you get its complete portfolio monitored across markets, Pillars 1–3, live. Enforcement (Pillar 4) stays out of the pilot; this is about proving the intelligence first.

No Pillar 4 in the pilot. Enforcement comes once you've seen the intelligence and decided to act.

From the pilot to full coverage, in waves.

No big bang. Each wave adds the markets your brands actually sell in, and each wave ships with the same evidence layer, so nothing you see is a one-off.

No prices and no dates here on purpose: pricing is the next conversation, once the pilot has shown you the data.

Build vs. buy, the arithmetic.

Volume

300 SKUs × 7 countries × 25 sellers × daily × 1.15 discovery = 60,400 pages a day; 175 seller × country pairs

Fetching

Firecrawl $0.60 per 1,000 (markdown), Bright Data Scraping Browser $9 per 1,000 (blocked shops, 32 % of pages), Apify $3 per 1,000 (marketplaces); residential proxy $8/GB

Reading

25k tokens per page × $1 per million (Claude Haiku 4.5); parsers per shop, LLM on 10 % of pages

People

Senior data engineer FR/DE €95k gross + 32 % on-cost; data QA €55k; product owner €66k; 140 productive hours a month

Shops

4 to 8 engineer-hours to connect a shop; 0.5 h a month to keep it alive

Result

Run €19,600 to €30,400 a month; build €205,800 incl. connecting the shops; year one €388,400

Sources

Vendor list prices and salary data verified 2026-09-15; tokens measured on 14 shops 2026-09-15; ECB USD reference rate