What Heuritech is genuinely good at
Heuritech was founded in Paris in 2013 and built its product around computer vision. It processes millions of social images per day and detects thousands of individual fashion attributes inside them: colour, print, fabric, neckline, length, silhouette. Those detections become time series, so you can watch an attribute rise or fall in a given market and get a forward projection.
That is a serious engineering achievement and a genuinely useful input, especially for a large brand planning volume. When the question is "is this attribute real and how fast is it moving", quantified visual detection at that scale is the right instrument. It was acquired by Luxurynsight in November 2024 and sells mainly into large fashion and luxury groups.
Where a pure attribute score stops
A score can only cover what already exists. This is the structural limit of scoring images alone, whoever is doing the scoring. Vision detects what was made, worn and photographed. The single most valuable thing a fashion brand can learn is what people want that nobody is currently selling, and that demand is invisible to a picture. It shows up as a sentence: "why does nobody make this in a tall fit", "I would buy this instantly if it came in wool".
A momentum number doesn't say why. A rising attribute curve tells you something is happening. It doesn't tell you whether people are buying it for the price, the fit, the status, or because a competitor's version keeps falling apart. Reason is what makes an insight actionable, and reason lives in language, not in the detection itself.
Photographed is not the same as bought. Social imagery skews toward what is worth posting. That is a real signal, but it is not a neutral sample of what people wear or buy, and the gap between the two is different in every category and price tier.
A dashboard doesn't take a follow-up question. You can read the attribute curve. You can't ask it who that curve is rising with, or what those specific people would need to hear to switch.
What Hugo does differently
Hugo is AI for fashion brands. It runs the same kind of visual work Heuritech is known for, computer vision that identifies the actual garments people wear in social video and photos, category, silhouette, colour, fabric. It doesn't stop at the detection. Every visual read is tied to who is wearing it, by identity and location, and to the live consumer conversation, so a rising attribute comes with a reason attached instead of just a number.
You ask a question in plain language in Slack, and it comes back with segments, verbatims, sentiment and a written recommendation, sources attached. That combination is what reaches the class of question a pure attribute score cannot:
- Unmet demand. What people are explicitly asking for and cannot find, including in categories you do not stock.
- Reason. Why a garment gets bought, kept, returned or replaced, in the customer's own words.
- Product flaws. The fit, fabric and sizing complaints that never reach your support inbox, on your products and on competitors'.
- Switching. Who left you for whom, and the sentence they used to explain it.
- Local difference. How the same category behaves in two cities or two communities, rather than one global curve.
It also starts from nothing. No integration, no data access, no implementation phase, which matters when you want to test the value before committing to an enterprise contract.
Side by side
| Heuritech | Hugo | |
|---|---|---|
| Identifies garments visually | Yes, purpose-built at large scale | Yes, computer vision on real posts and video |
| Reads | Social images | Images and video, plus consumer language: posts, comments, forums, reviews |
| Core output | Attribute momentum curves and forecasts | Answers with verbatims, segments and a recommendation |
| Answers "what is rising" | Yes, quantified | Yes, with the reason attached |
| Answers "why" | Not directly | Yes, that is the point |
| Ties a trend to a specific audience | Aggregated by market | Yes, by identity, city and community |
| Products that do not exist yet | Invisible to a score | Visible, people ask for them out loud |
| Interface | Dashboard | Slack conversation, plus reports |
| Follow-up questions | Not the model | The main way you use it |
| Typical buyer | Large brands and luxury groups | Mid-market and challenger brands, plus teams inside larger ones |
| Pricing | Custom enterprise, not published | Scoped per team |
Where they genuinely differ
Heuritech's edge is depth in one lane: a decade of tuning a visual detection model purpose-built for attribute forecasting at the scale large luxury and fashion groups operate at. If that specific benchmark is what a team needs, that specialisation is real and hard to replicate.
Hugo's edge is that the visual read isn't the whole answer, it's one input that gets tied to why, who, and where in the same workflow, instead of a score you then have to go interpret with a separate research process. For a team that wants the reason and the audience attached to the trend, not just the trend, that's the more complete answer.
Knowing an attribute is rising is a start. Knowing who it's rising with, and why, is what actually changes a buy.
Other tools in this space
WGSN for long-range macro direction. EDITED for competitor assortment and pricing. Trendalytics for cross-channel trend validation. StyleSage for benchmarking inside a PLM implementation. The full honest rundown is in our comparison of fashion trend forecasting tools.