Answer a market question in days, not a quarter
Assortment, positioning and sentiment for a whole category — assembled from public sources on demand, and summarised in language your stakeholders will read.
Entering a market, launching a line or sizing a competitor usually means weeks of manual collection followed by a deck nobody trusts. Here the collection is automated and the reading is done by models, so the analysis starts on day one.
What makes market research slow
These are the reasons a market question takes a quarter to answer. Each one is handled by the stack below.
The data is scattered across the web
Shops, marketplaces, review sites and comparison portals each hold part of the picture, in a different structure. Assembling them by hand is most of the project.
By the time it is ready, it is old
A study that takes six weeks describes a market that has moved. Refreshing it means repeating the whole exercise.
Assortment is hard to compare
Competitors organise categories differently and bundle differently, so “who sells what” is a judgement call before it is a number.
Reviews are qualitative, and there are thousands
The signal about what customers value sits in free text nobody has time to read at volume.
Every market reads differently
Assortment, pricing norms and language differ per country. A single study built on one market does not transfer.
The output has to be readable
Stakeholders act on a narrative with evidence attached, not on a raw table of scraped rows.
What changes when research is a pipeline
A market question becomes a query you can re-run, rather than a project you have to fund again.
Collection runs unattended and the summary is generated, so the first draft of an answer exists before the kickoff meeting would have ended.
The same scope can be re-run each quarter, which turns a study into a trend nobody has to rebuild.
Every claim in a summary links back to the listings and reviews it came from, so the analysis survives scrutiny.
Adding a country means adding a scope, not starting a new research project from scratch.
The stack for market research
Each product is bought separately, so a first study can be collection only — and the reading layer can be added when the scope justifies it.
Data Scraping
Assembles the dataset: listings, attributes, assortment structure and reviews from the shops, marketplaces and portals that define the category.
LLM Analytics
Reads what was collected: classifies assortment, extracts what reviews complain about, and writes the summary your stakeholders will actually open.
Proxy IP
Needed when the study crosses borders: residential exits per country so assortment and prices are captured the way a local buyer sees them.
Questions about market research
Whatever the question needs: assortment and attributes per competitor, price levels, review volume and sentiment, and category structure — collected from the sources that define the category.
Either. A token pack covers a single study; re-running the same scope each quarter turns it into a trend without new setup.
LLM Analytics reads the collected reviews and returns structured output: themes, complaints, praised attributes and their frequency, each traceable back to the source.
Yes. Each market is its own scope, collected through local exits so assortment and prices reflect what a buyer there sees.
No. Data Scraping alone produces the dataset. LLM Analytics is what turns it into a readable analysis, and Proxy IP matters once the study crosses borders.
Start with one question
Tell us the category and the market, and we will scope the first study with you.