# Foresight uses consumer simulations to narrow CPG research decisions

Canonical: https://mudpie.ai/companies/foresight/
Breadcrumb: [Home](https://mudpie.ai/) / [Companies](https://mudpie.ai/companies/) / [Foresight uses consumer simulations to narrow CPG research decisions](https://mudpie.ai/companies/foresight/)
Author: Ali Abouelatta (https://mudpie.ai/authors/ali-abouelatta/)
Published: 2026-09-19
Updated: 2026-09-19
Research type: Company profile
Method: Company and accelerator sources checked 2026-09-19. Product claims are attributed to their sources; this is research, not a hands-on product trial.

Foresight is a market-research product for CPG and consumer teams that want to test pricing, positioning, packaging or campaigns before spending on a real launch.

## What it does

The company describes AI-powered simulations of human behavior. A team can test possible moves, see how simulated consumers are likely to react and choose a path before going into fieldwork. [Foresight YC profile](https://www.ycombinator.com/companies/foresight) [Foresight homepage](https://foresight.tt/)

The public YC launch reports a blind benchmark against a Fortune 500 company with 100+ paired estimates, 95% accuracy against fieldwork and a 0.88 Lin’s concordance correlation coefficient. Those are company-reported results from a launch claim, not an independent replication. [YC launch](https://www.ycombinator.com/launches/QHw-foresight-understand-your-consumers-better-than-ever)

That distinction is the whole buyer decision. A simulation can help narrow questions and compare directions. It should not automatically replace real customer research when the decision is expensive, novel or hard to reverse.

## Why I’d look closer

The product fits a team that needs more iterations before committing to a survey, focus group or launch. The company says it can test pricing, positioning, packaging, campaigns and product concepts against target audiences. That is a useful pre-fieldwork layer if the output helps a researcher decide what to test next.

The founder context supports both sides of the product. The [YC profile](https://www.ycombinator.com/companies/foresight) describes Antoine Bertrand as a former Bloomberg engineer and second-time founder who previously grew a gaming platform to 100,000 monthly active users. Eytan Rozenblum is described as CEO and cofounder of the simulation product.

## What could make it the wrong choice

The central risk is false confidence. A simulated consumer is not a real respondent, and a benchmark against fieldwork does not tell a new buyer how the model performs for a different category, audience or price decision.

Pricing was not published in the sources checked. A buyer should ask what consumer data shapes the simulation, how target audiences are defined, how calibration works, what the blind benchmark included, and where the model is expected to abstain.

## My editorial take

I would shortlist Foresight for a CPG or consumer-insights team that already knows the question it wants to explore and needs a faster way to narrow options. I would not use it as the only evidence before a major launch. The fit is strongest as a pre-fieldwork filter: run more ideas, kill weaker ones, then validate the finalists with real consumers.

## Quick facts

| Field | Sourced detail |
| --- | --- |
| Product | AI-powered consumer-behavior simulations |
| Buyer | CPG, marketing, advertising and consumer-insights teams |
| Public validation | Company-reported blind benchmark against fieldwork |
| Pricing | Not published in the checked sources |
| Main fit question | Is the simulation narrowing a real research decision or replacing one? |

## Sources checked

Foresight YC profile, homepage, YC launch post and linked case-study path were checked on 2026-09-19.

## Cohort context

Foresight 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](https://mudpie.ai/research/yc-cohorts-2026-09-19.json).

## Public website snapshot

Observed 2026-09-19T16:16:14.380Z in raw homepage HTML. This records visible metadata and advertised links, not agent execution or product quality.

| Signal | Homepage observation |
| --- | --- |
| Product description metadata | Observed |
| Canonical link | Not observed in this response |
| H1 or H2 heading | Observed |
| Typed structured data | Observed |
| 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](https://mudpie.ai/research/yc-homepage-links-2026-09-19.json) · [Collection method](https://mudpie.ai/research/yc-homepage-methods/README.md). Missing links here do not establish that a capability or file is absent elsewhere.


## Author disclosure

I cofound Lazyweb and publish Mudpie. This is an owner-written publication, not an independent testing organization. Research notes distinguish observations, sourced reporting and editorial judgment.