AI agents for consumer brands
We understand consumers better than anyone. That is why we can build the agents.
Hugo holds a measured, maintained map of real consumers: who they are, what they think, what they need, and what they will buy next. It is the hard part, it is the part we own, and it is the only reason an agent pointed at your assortment says something your competitor's agent cannot.
Every number on this page is published with its method and its error rate.
The edge
Four layers per audience, built by observing real people.
This is the company. Everything else we ship runs on top of it. The map holds a named audience across four layers, built from live public conversation, images and video, and it is maintained continuously rather than commissioned once. Four decisions separate it from a scrape.
Reaching real people, not whoever the algorithm surfaced
Hashtag and keyword search produces a sample whose selection probability is set by an unobservable ranking algorithm, which cannot be corrected for afterwards. Hugo uses chain referral sampling, known in survey science as respondent-driven sampling, which reaches populations no register lists. The New York map reached 876 real accounts across 4,464 posts that way.
Seeing what people wear, not what the caption said
A fine-tuned garment detector runs on every image and video in the stream, at F1 0.597, with shoe recall lifted from 0.21 to 0.80. Only 2 percent of the frames in that run were outfit posts. The other 98 percent of observable clothing appeared incidentally, in posts about something else, which is precisely the behaviour keyword listening cannot see.
Embedding the garment rather than the photograph
Naive embeddings cluster by lighting and pose instead of by clothing. Training on the garment cut photo bias from 0.672 to 0.428 and reached pair accuracy of 0.959, so two people wearing the same jacket resolve to the same garment rather than to the same aesthetic.
Resolving to an actual product
Corroborated visual search takes a garment to a real, buyable SKU at recall@1 of 0.87 to 0.90 automated, and 0.76 when hand-audited. That is the step that turns a cultural observation into something a merchandiser can act on.
Why the claim holds
We publish our error bars. Ask anyone else in this category for theirs.
Saying we understand consumers better than anyone is worth nothing as an adjective, so we do not leave it as one. Every number above sits on this site with its method attached. Simulated audiences are run blind against published real-world surveys they have never seen: 71 percent winner accuracy and a mean Jensen-Shannon divergence of 0.039, across 70 held-out questions, 19 audiences, 11 countries and 5 languages. It generalises across language, at 0.028 divergence in Finland.
And the limits, in the same breath, because a measurement without them is a marketing claim. It only works where people talk in public. It over-represents the delighted and the annoyed, so frequency is directional rather than incidence. It cannot test a concept nobody has launched or discussed. The degree-weighted bias correction is designed and not yet implemented. A near-duplicate rule removes about 18 percent of crops, unevenly by category. Magnitudes flatten, so trust the ranking before the gap.
That is the whole basis of the claim, and it is checkable, which is the only kind worth making. Read the accuracy report, or the mapping programme it runs on.
Why it decides everything
Agents are easy now. That is exactly why the map is the product.
This is not a claim that other agent products are badly engineered. Most are fine, and the underlying models genuinely can do the reasoning these tasks need. Wrapping one in a brief and a schedule is a weekend of work, which is why there are suddenly so many.
Which is the point. When the reasoning is commodity, the only thing separating two agents is what they know. Ask an agent what a brand should make next season and it has to know what that brand's specific customers wanted and could not find. A general model has never met them. It knows the category's average opinion, which every competitor's agent also knows, and that is why those answers come back plausible, confident and interchangeable.
The constraint was never reasoning. It is observation. We built the observation first, and the agents are what became possible afterwards.
What the map makes possible
Four agents, live. Each one a decision, not a dashboard.
Every agent below is the same shape: a decision a brand makes on a cycle, and the layer of the map it reads to make it well. They work continuously and report into the channel your team already uses.
The product agent
What to make next. It reads the demand language in the map: requests, workarounds, compromise purchases and abandonment, grouped by the job the product does rather than the category it sits in. None of that is in your sales data, which can only contain products you already sell.
“What are our customers asking for that we do not make?”
The range agent
What is wrong with what you already sell. It reads the positions an audience holds on your current products, in the words they use when no researcher is in the room. Reviews you own are survivorship-filtered. The map includes the people who bought once and never came back.
“Why did the autumn knitwear underperform?”
The marketing opportunity agent
The opportunity already in your catalogue. It matches live cultural movement against products that already exist, at SKU level. Most brands are carrying the right product for a moment happening now and cannot see it, because nobody watches the catalogue and the culture in the same place.
“What should we push this week, and why now?”
The creator agent
The one creator who would move one product. It reads the graph, not a follower count. Influence travels through trends and communities rather than through accounts, so the question is which creator sits inside the community that buys this product, not which creator is largest.
“Who should seed the new runner to Stockholm women aged 22 to 30?”
Next
What the same map supports, and we have not shipped yet.
Stated plainly rather than implied, because a roadmap presented as a product is the fastest way to lose a research buyer.
The pricing agent. Where a price sits against what an audience believes the product is worth, and what they compare it to when deciding. The map already holds the comparison set and the language people use about value, which is the input a pricing decision needs and rarely has.
The market expansion agent. Which market or audience holds unmet demand for what you already make. The same four layers, run against a population you do not sell to yet.
The test
Bring a decision you already made and know the outcome of.
The honest way to evaluate this is not a demo of our choosing. Pick a launch, a range call or a campaign from the last year where you now know how it went, and let the agent answer it blind against the audience as it stood at the time. If it tells you what you already learned the expensive way, that is the whole argument. If it does not, you have lost an afternoon and learned something real about the category.
Frequently asked questions
Why is the consumer map the thing that matters, not the agent?
Because every decision a brand wants automated is bounded by knowledge, not by reasoning. Deciding what to make next requires knowing what a specific audience asked for and could not find. No amount of model quality substitutes for that observation. The agents are the hands. The map is the part that is hard to build, hard to copy, and the reason the answers are specific to your customers rather than to your category.
What makes Hugo's understanding of consumers better than anyone else's?
That it is measured and published rather than asserted. Hugo reaches real people by chain referral sampling instead of hashtag search, identifies the garments they actually wear rather than the words in their captions, resolves those garments to buyable products, and reports the error rate of every one of those steps. Ask anyone else in this category for their recall, their divergence against held-out real-world surveys, or their sampling method, and see what comes back.
How is this different from wrapping ChatGPT or Claude in a prompt?
A general model reasons over what it absorbed in training, which is the internet's average opinion about a category. It has never observed your customers. Hugo observes a named audience, learns a behavioural model per person, and connects people, products and trends on a graph. The agent reasons over that. One interprets, the other retrieves and evidences.
What does Hugo actually observe, and how accurate is it?
Public consumer conversation, images and video across social platforms, forums, reviews and the open web. Garment detection runs at F1 0.597 with shoe recall lifted from 0.21 to 0.80. Product resolution reaches recall@1 of 0.87 to 0.90 automated and 0.76 hand-audited. Simulated audiences answer held-out real-world survey questions with 71 percent winner accuracy and a mean Jensen-Shannon divergence of 0.039, across 70 questions, 19 audiences, 11 countries and 5 languages.
What are the limits?
It only works where people talk in public. It skews toward the delighted and the annoyed, so frequency is directional rather than incidence. It cannot test a concept nobody has launched or discussed. The degree-weighted sampling-bias correction is designed but not yet implemented, and a near-duplicate rule removes about 18 percent of crops unevenly by category. Magnitudes flatten, so trust the ranking before the gap.
Which agents are live today?
Four: the product agent that recommends what to make next, the range agent that gives feedback on what a brand already sells, the marketing opportunity agent that finds the opportunity already sitting in the catalogue, and the creator agent that identifies the one creator who would genuinely move one product for one audience. A pricing agent and a market expansion agent are what the same map supports next, and are not deployed yet.
Do the agents replace a research team?
No. They remove the part of the work that is retrieval and waiting, which is most of the elapsed time in a research cycle. Judgement about what to do with the answer stays with the team. Heads of insights use Hugo to answer more questions per quarter, not to answer fewer questions with fewer people.
Where do the agents run?
In the brand's own Slack or Microsoft Teams, so people ask the way they already ask each other. The web app holds historical runs, longer reports and exports. Most answers arrive without anyone leaving the thread.
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