Auxos uses digital twins of real customers to speed up product and marketing research

Auxos is for product, marketing and research teams that need a customer signal before a real launch, but cannot run a traditional panel for every decision. It builds digital twins from real people in a defined ICP, then lets teams test concepts, pricing, messages and usability with that simulated audience.

What it does

Auxos says the workflow starts by defining an ideal customer profile, sourcing and interviewing people who match it, and grounding one digital twin in each person. Teams can then run a research conversation, survey or concept test across the audience and inspect quantitative preference alongside qualitative explanations. Auxos homepage

The product is more useful than a generic synthetic-persona tool if the audience construction is real and traceable. A team can compare an ad, landing page or pricing tier before exposing it to customers, then drill into segments instead of waiting weeks for recruitment. The company frames this as 100-times faster and cheaper than traditional research; that is a company claim, not proof that a simulation can replace a real experiment.

What to check

The central tradeoff is speed versus behavioral validity. Ask how participants are recruited and consented, how interview transcripts become a twin, how often twins are refreshed, how uncertainty and disagreement are shown, and where synthetic responses should stop. A pricing preference in a simulation is not a purchase, and a concept appeal score is not retention. Keep the real-customer validation step for consequential decisions.

The founder context fits the problem. The YC profile identifies Ashton Daniel as a former BCG consultant who worked on agentic-AI deployments and research programs, and Jerry Wu as a former Meta and Instagram engineer working on models for Reels, ads and Feed. The launch also describes Kerry’s Amazon Ads market-behavior work.

Auxos does not publish standard pricing in the checked sources. The product is demo-led, so the buyer should scope audience size, number of interviews, refresh cadence and export rights before comparing it with a panel or survey budget.

My editorial take

I would use Auxos as a fast filter for low- to medium-stakes product and marketing decisions, not as a substitute for a launch, purchase or retention test. The first engagement should compare a few simulation predictions with observed customer behavior and make the disagreement visible. If the twin audience cannot explain who it represents, the speed advantage is not enough.

Quick facts

Field	Sourced detail
Buyer	Product, marketing, insights and research teams
Product	Digital-twin audiences for simulated customer research
Use cases	Concept, prototype, pricing, messaging and usability tests
Pricing	Demo-led; no standard public price found
Main fit question	Can simulated feedback narrow the decision without replacing real validation?

Sources checked

Auxos’s YC profile, homepage and about page were checked on 2026-09-19.

Cohort context

Auxos is listed in Spring 2026. In our 2026-09-18 directory snapshot, 112 of 193 listed companies in that cohort have YC’s primary industry label B2B (58.0%). This is a current-directory comparison, not an original intake count or a performance ranking. Nine-cohort dataset.

Public website snapshot

Observed 2026-09-19T16:16:08.359Z in raw homepage HTML. This records visible metadata and advertised links, not agent execution or product quality.

Signal	Homepage observation
Product description metadata	Not observed in this response
Canonical link	Not observed in this response
H1 or H2 heading	Observed
Typed structured data	Not observed in this response
Docs/developer link	Not observed in this response
Pricing link	Not observed in this response
llms.txt link	Not observed in this response
Markdown alternate	Not observed in this response

Public observations · Collection method. Missing links here do not establish that a capability or file is absent elsewhere.