The Chicago company that stopped asking people what they remember - and started measuring what they actually do.
Somewhere in a suburban kitchen right now, a small cloud-connected tag is noting that someone reached for the laundry pods at 7:14 a.m., used three, and put the box back facing the wrong way. Nobody filled out a form. Nobody sat behind a one-way mirror. The data just arrived.
That quiet tag belongs to QualSights, a Chicago insights-technology company that has spent the better part of a decade on a stubborn idea: the truth about how people use products is not in what they say, but in what they do when no one is officially watching. The brands you buy from - Nestle, PepsiCo, Clorox, Estee Lauder - run their next launches through it.
For an industry built on questionnaires and conference-room mirrors, this is a small heresy. QualSights does not particularly care what you would tell a moderator. It cares about the laundry pod at 7:14 a.m. - the timing, the quantity, the wrong-facing box - because that is where real preference hides. The company calls the result authentic insight; everyone else just calls it what people do when they think the research is over.
QualSights blends the depth of qualitative research with the speed of quantitative. The industry spent years insisting you could only pick one.
Traditional market research has a charming flaw: it asks people to remember. How often do you moisturize? How many spoonfuls of coffee? Be honest. The honest answer is that nobody knows, and the polite answer is whatever sounds reasonable. Decades of multimillion-dollar product decisions have been built on this gentle fiction.
The alternatives were not much kinder. Focus groups deliver depth but move at the speed of catering logistics. Surveys deliver scale but flatten human behavior into checkboxes. Ethnographic studies - sending researchers into homes - are rich and slow and expensive enough to make a CFO weep. Brands were forced to choose between knowing a little about many people or a lot about a few.
QualSights looked at that trade-off and decided it was a false one - the kind of constraint everyone accepts right up until someone refuses to. The refusal had a thesis. If the bottleneck in research was not the questions but the act of asking, then the fix was to stop asking and start observing - at scale, continuously, and cheaply enough that depth no longer had to be rationed.
There was a second, quieter problem hiding inside the first. People are not lying when they misremember; they are doing their honest best to reconstruct a behavior they were never paying attention to in the first place. You cannot survey your way around inattention. You have to be in the room - or, more practically, you have to leave something in the room that remembers for them.
Surveys ask what you think. Sensors watch what you do. Only one of them has an incentive to flatter you.
QualSights did not begin as QualSights. It started life as Georama, a platform for live-streaming travel experiences - which is why, in a detail too good to invent, its old Twitter handle is still @GeoramaLive. Founder and CEO Nihal Advani had built the plumbing for capturing real moments through a phone camera, anywhere on earth. The pivot was recognizing that the same plumbing could capture something more valuable than a virtual gondola ride: it could capture consumers being themselves.
Advani's wager was that technology could collapse the gap between qualitative richness and quantitative reach. Capture authentic video. Layer on sensors. Run AI across the pile. Do in days what used to take months. It was an ambitious bet, and the people who agreed to back it were not amateurs.
The QualSights board reads like a reunion of the research industry's old guard: Mitch Barns, former CEO of Nielsen; Gian Fulgoni, former CEO of IRI and Comscore; Rishad Tobaccowala; Sanjay Khosla; and Kellogg's Mohanbir Sawhney. When the people who built the last era of measurement sign on to the next one, it is worth a second look.
The most telling human insights happen when we least expect them.
QualSights is less a single tool than a kit for catching reality in the act. Participants opt in, then the company's technology does the rest - quietly, continuously, and with a precision that recall could never match.
A subscription usage-intelligence platform billed as the first Physical AI built for consumer products. It measures how people actually use things, with category-level benchmarks.
Patented, always-on, cloud-connected sensors that passively track if, when, how often, how long, and where a product gets used.
An award-winning mobile app for capturing video, mobile ethnographies, and in-context feedback from people anywhere in the world.
Analytics that fuse video, sensor, and survey data into clear answers for innovation, concept testing, and pre-launch validation.
Smart Tags don't ask if you liked the product. They notice you used it every morning for three weeks. That is a different kind of honesty.
A clever idea is cheap. A clever idea that Fortune 500 brands route their innovation budgets through is something else. QualSights counts among its clients the names sitting in most American pantries and bathroom cabinets - the kind of companies that do not hand their product pipelines to a vendor on a hunch.
The validation is not only commercial. In 2022 the firm closed a $7.7M Series A led by 4490 Ventures, money earmarked less for survival than for scale - more engineers, more sensors, more categories. A year later the Financial Times ranked it #19 among the Americas' fastest-growing companies, a list it has now reached twice. Growth, it turns out, is its own form of peer review.
QualSights frames its purpose plainly: help brands decode real-world human behavior to make better decisions, by pairing research technology with deep category expertise. The unglamorous version is that a lot of perfectly good products fail because the company behind them misread how people would actually live with them. QualSights wants to shrink that blind spot.
There is a subtle ambition underneath the sensors. If you can see how products genuinely fit into daily life - the workarounds, the abandonments, the small daily rituals - you stop guessing and start designing for the human who exists rather than the one in the persona deck. The payoff compounds: every reformulation, package tweak, and pricing call gets a little less superstitious and a little more evidence-shaped.
It is also, in its way, a more respectful relationship with the consumer. Nobody is asked to perform their preferences or rate a concept on a scale of one to seven they do not believe in. They simply live, and the product team learns to fit itself around that life instead of the other way round.
Most insight tools sample memory. QualSights samples life as it happens.
"Physical AI" is QualSights' term for the next move: artificial intelligence trained not on scraped web text but on the messy, unglamorous reality of physical product use. As AI grows hungry for grounded, real-world data, a company that has quietly been instrumenting kitchens and bathrooms for years is sitting on something scarce. The web is nearly exhausted as a training corpus. The physical world is barely tapped.
Which brings us back to that suburban kitchen. The laundry pods went back facing the wrong way, three were used instead of the recommended two, and the box will run out eleven days sooner than the brand's forecast assumed. None of that would have surfaced in a survey. All of it now sits in a dashboard, and somewhere a product team is quietly revising its plans. The person in the kitchen never noticed a thing - which was always the point.