The proprietary-data revenue platform

Your catalogues
are a revenue channel.

We turn the data only your business owns into pages, content and funnels — then measure every visit through to leads and attributed revenue. Not pageviews. Revenue.

See it working → How it works
▲ 29× organic clicks · 6 months 2,683 pages published $12.7k attributed orders 74% of clicks via owned data
Live pipeline

Watch data become revenue

Every record flows through five stages. Click a stage — or a node — to see what happens inside, with real samples from our clients.

What this looked like for our clients
Wellness education
Auction house
Food-allergy app

Organic clicks, one client, six months

▲ 29× growth source: Google Search Console
0growth in monthly Google clicks in 6 months — blog engine, wellness platform
0of an auction house's organic clicks now arrive through its own data archive
0verified articles in six months — ~30/day with no quality decay
$0orders from buyers who read an article first — measured as a floor, not a claim

Every number re-runnable by the client on their own analytics — GA4, Search Console, raw BigQuery events.

One engine, three businesses

Same playbook. Very different data.

Online certification platform

100+ course curricula → a content engine per course

Curricula, syllabuses and scientific sources became 2,683 articles across the buyer journey, localized into 10+ languages — wired to raw analytics and measured to the purchase.

Read the case study →
29×monthly Google clicks, Feb → Aug
686kcumulative search impressions
$12.7kattributed orders from article readers
Specialist auction house · UK

30 years of catalogues → the front door to the brand

Three decades of auction and maker records published as a structured public archive — now cited by Google and AI assistants, and connected to valuation enquiries.

Read the case study →
74%of all organic search clicks arrive via the archive
55%of all site visits enter through an archive page
+112%archive traffic growth in six months
Food-allergy startup · Lisbon

Allergen data → the pages allergic diners search for

Dish-level allergen data from Lisbon restaurants becoming local pages: safe dishes per restaurant, restaurants per allergen — with measurement wired from day one.

Read the case study →
Day 1attribution designed in, not bolted on
Per-dishstructured data nobody else holds
In buildresults published when re-runnable
Case studies

The work, measured

Principles

Why clients stay

01

Staffed, not unattended

Engineers run the systems, strategists pick targets, editors own quality. Automation does the repetition; named humans sign off.

02

Manual first, then engine

We do the job by hand before automating it — so the engine encodes real craft, not shortcuts.

03

Floors, not hype

Attribution is a conservative range. A number you can't re-run on your own data is a number we don't report.

Sitting on data nobody's using?

Bring us one dataset — a catalogue, an archive, a price list. We'll tell you plainly whether it can become a growth channel, and what we'd measure to prove it.

Start the conversation →
Method

Five steps, in plain language

No jargon. This is exactly what happens when we work with you, in order.

1

Discover your proprietary data

We come to your business and look for the records only you have: course catalogues, decades of auction results, restaurant menus, price lists, customer questions. Most businesses treat this as a reference library. We treat it as a lead engine waiting to be switched on.

↳ workshops · data audits · export & scraping of your own systems

2

Arrange it into a structured library

Raw data is messy. We clean it, connect it and organize it so every record can become a page — and so the library keeps growing as your business produces more data.

↳ databases · BigQuery · pipelines that update themselves

3

Publish pages people search for

Every record becomes a page matched to a real search: one per maker, per course topic, per restaurant, per allergen. We write by hand first to learn what works, then industrialize production behind verification gates — with a named human reviewer before anything ships.

↳ content engines · editorial review · localization into 10+ languages

4

Distribute across search, social & AI

The pages rank on Google, get cited by AI assistants like ChatGPT, and feed social content — clips, posts and threads cut from the same data library. One asset, many channels.

↳ SEO · social content production · AI-engine visibility

5

Measure to leads and revenue — not pageviews

Traffic is easy to claim. We wire the full funnel to raw analytics and follow every visitor to whatever they did next: read, clicked, enquired, bought. You see what the content actually earns, and every number can be re-run on your own data.

↳ GA4 · Search Console · raw BigQuery events · GTM · attribution dashboards

The three-stage rule

We never arrive with a ready-made tool. Every engagement runs the same way: first we do the work manually ourselves inside your real workflow, then we automate the repetitive pieces around the content, and only then do we build an engine — with a human review gate at the end. Slower to start, much better at scale, because the engine encodes how the work is actually done well.

How we report numbers

Purchase counts are conservative floors. Attribution is a range, not a single flattering number. Bot-heavy traffic is excluded from reach claims. If you can't reproduce a figure from your own analytics, we don't publish it.

See it applied → Talk to us
Case studies

The work, measured

Client names are withheld until each partner approves publication — the numbers are real and verifiable from their analytics.

Industries we serve

If your business collects data, it can sell with it

The playbook is the same everywhere — what changes is the data. Here's what each industry is usually sitting on.

Auction houses & marketplaces

Decades of lots, results, maker and provenance records — the richest datasets in their fields, usually dormant.

catalogues · results · maker records

Education & certification

Curricula, syllabuses, scientific sources and student questions — every course is three search audiences.

curricula · syllabuses · course catalogs

Food & hospitality

Menus, dishes, allergens, locations — structured, this answers the exact questions diners search every day.

menus · allergens · locations

E-commerce & D2C

Product specs, reviews, comparisons, price histories — content engines that outlast any ad budget.

specs · reviews · price data

Health & wellness practices

Treatments, practitioner expertise, patient FAQs — the trust content people search for before they book.

treatments · expertise · FAQs

Communities & memberships

Applications, member knowledge, events — operations and growth engines built on what the community already produces.

applications · member data · events
Ask about your industry →
← All case studies

The Blog Engine

An online wellness-education platform was buying every customer with ads at ~$80 each — near break-even on a ~$97 course. Six months later it owns an organic channel measured all the way to the purchase.

The data

The platform sells 100+ certification courses. Each course carries its own proprietary data: curriculum, syllabus, scientific sources, buyer personas. Our starting insight: one course is not one search audience but three — someone learning about the field, someone weighing a career move, someone ready to enroll. Each course justified its own small content engine.

What we did

Stage 0 — by hand. We wrote the first 49 articles ourselves, learning what structure ranks and how scientific citations must be sourced.

Stage 1 — the engine. A pipeline from the course catalog and search data to long-form drafts with verified citations, editorial review, imagery and automated publishing — roughly 30 articles a day, plus 4,978 translated versions across 10+ languages. Staffed, not unattended: engineer, strategist, operator, editorial reviewer.

Stage 2 — measured to the purchase. Search Console, GA4 and the raw BigQuery event export, following every blog visitor to whatever they did next.

Results

0monthly Google clicks in 6 months: 95 → 2,751 (Feb–Aug 2026)
0articles in ~6 months; 90% earn Google impressions within weeks
0cumulative search impressions
$0orders from 66 buyers who read an article before purchasing

The reader funnel · June 1 – Aug 6

landed on article22,966
genuinely read5,910 · ~2 min active
visited a course495
added to cart38
bought (full journey)66 buyers · ~$12.7k

The economics

A blog-connected buyer currently costs $95–115 in article spend versus ~$80 from ads. The difference is structural: an ad is paid again for every customer; an article is paid once and keeps earning. The blog's cost per buyer falls every month as the library compounds. The program is now testing 40 articles a day.

Reported honestly: same-device purchases are a floor, attribution is a range, and bot-heavy "direct" traffic is excluded. The most-clicked links in articles are the scientific citations — this audience checks sources.

← All case studies

The Archive Engine

A respected specialist auction house was sitting on three decades of catalogue and maker records — one of the richest datasets in its field — doing almost nothing commercially. The archive is now the front door to the brand.

The data

Thirty years of auction catalogues, lot results and maker records. To leadership it looked like a reference library. Enquiries complete off-site — WhatsApp, phone, valuation forms — so traffic was visible but never connected to revenue. We treated the archive as a lead engine.

What we did

We published the archive as structured public pages — makers, lots, results — built to be found by collectors searching for specific instruments and to be cited by AI assistants answering their questions. Then we started wiring visits to actual enquiries.

Results · first half of 2026

0of organic clicks
57,920of 78,573 organic clicks arrive through the archive
55%of all site visits enter through an archive page (June)
+112%archive growth in six months — vs 74% for the rest of the site

Traffic is turning into enquiries

Enquiry tracking is new and still being built out, but the early picture is encouraging: a meaningful share of valuation enquiries begin with an archive visit — including high-intent visitors who arrive through AI assistants like ChatGPT.

Why it matters: paid ads are rented — the moment spend stops, leads stop. An archive engine is owned. For a business built on proprietary catalogue data, it is the most defensible growth asset available, and almost no one in the industry is using it.

← All case studies

The Course Engine

The same wellness-education platform needed to grow its catalogue faster than a manual team could write. We turned course production itself into a system — with a human sign-off on every course.

What we did

Stage 0 — we wrote courses ourselves, inside the client's real workflow: research, curriculum, writing, imagery, sales page, publishing. Doing the job by hand mapped which steps repeat mechanically and which need human judgment.

Stage 1 — we automated the pieces around the content: image generation, sales and FAQ pages, multi-platform publishing, localization — while people still wrote the courses. Every batch reviewed; corrections fed back in.

Stage 2 — the engine. The manual playbook became internal software that produces curriculum and content following the same steps our writers refined by hand. Nothing ships without a named human reviewer verifying the course first.

Results

0English-language courses live, produced with the engine and human review
0languages served from the same engine
Per-coursepricing — the client buys outcomes, not hours; every engine improvement is shared

The catalogue this engine built created the next problem worth solving — matching demand to supply — which became the Blog Engine.

← All case studies

The Local Pages Engine

A food-allergy startup in Lisbon holds data most of the food industry doesn't: which dishes at which restaurants are safe, per allergen. We're publishing it as the pages allergic diners actually search for.

The data

Allergen information collected from restaurant menus across Lisbon — structured per dish, per restaurant, per allergen. For someone with a nut or gluten allergy searching "where can I eat safely near me", this data is the answer, and nobody else has it.

What we're building

Local pages generated from the data library: safe dishes per restaurant, restaurants per allergen, neighbourhood guides. The same engine pattern as our archive and blog work — structured data in, indexable pages out, measurement wired from day one rather than added later.

Engagement in progress — results will be published here the same way as the others: numbers the client can re-run on their own analytics, floors not hype.

← All case studies

The Admissions Engine

A global education community ran admissions on forms, spreadsheets and reviewer memory. We rebuilt it in three stages into an AI-assisted platform — keeping human judgment in charge.

What we did

Stage 0 — document the manual process. ~10 applications a day, 30–60 minutes of reviewer time daily, criteria held in reviewers' heads, a week in the queue. We mapped every criterion and edge case into a clear decision tree.

Stage 1 — automate the admin. Status-driven flows for acceptance, rejection and clarification emails, plus automatic onboarding into community channels, groups and local chapters. Ran in production for a year; humans still made every decision.

Stage 2 — AI-assisted evaluation. The validated decision tree was tested against 100 historical applications before launch. A custom platform now enriches applications with permitted public information, recommends accept / reject / clarify, and drafts personalized clarification emails. Reviewers confirm or override in bulk.

Results

~0daily review time, down from 30–60 minutes
0of AI "accept" recommendations agreed with by human officers since the initial adjustments
Same-daycommunication and onboarding, replacing a week-long queue

Why it's here: the same discipline behind our data engines — document the manual craft, automate around it, introduce AI last, keep a human accountable — applied to operations instead of content.

Contact

Bring us one dataset

A catalogue, an archive, a price list, a decade of records nobody looks at. We'll tell you — plainly — whether it can become a growth channel, and what we'd measure to prove it.

Start the conversation

One message is enough. Tell us what your business collects, and we'll reply with an honest read — usually the same day.

Message us on WhatsApp → +971 54 331 3938 →