Test your market
before it exists

How does Echo stay grounded in reality when it works with synthetic consumers?

Echo grounds its simulations in structured profiles built from real-world datasets and behavioral science. Each synthetic consumer is defined by attributes such as demographics, preferences, interests, Big Five traits, memory, and decision-making patterns, then responds within a specific scenario rather than as a one-off generated answer. That structure is what makes the outputs more consistent, explainable, and commercially relevant than generic AI responses.

Is Echo replacing research, or helping teams do research differently?

Echo is best understood as a research acceleration layer. It helps teams narrow uncertainty earlier, test more scenarios, and identify which questions deserve deeper validation before they commit time and budget to fieldwork or live experiments.

How does market simulation actually work inside Echo?

Echo starts by translating a business question into a modeled scenario: a price change, a new product, a campaign, a promotion, or a competitive move. It then generates synthetic consumers with structured attributes and decision logic, places them into that scenario, and observes how those individual choices aggregate into market-level signals.

Where does the data come from to build an Echo market?

Echo is calibrated using a mix of first-party and external signals. That can include CRM, CDP, loyalty, transaction, and other real-world datasets, which are used to define segments, attribute distributions, and behavioral patterns.

Can Echo simulate customers we do not already have data on?

Yes. Echo can model both observed customers and adjacent audiences that are strategically important but underrepresented in your current data - for example, a new segment, a new geography, or a competitor's likely buyer.

How is Echo different from surveys, panels, or A/B tests?

Surveys and panels collect stated responses. A/B tests measure behavior once something is already in market. Echo sits earlier in the decision cycle: it uses simulation to test multiple 'what if' conditions before launch, at much higher speed, with the ability to rerun the same question under different assumptions.

How does Echo validate that its simulations are trustworthy?

Echo uses multi-metric validation - scoring outputs on rank agreement, calibration, and decision relevance. Every result traces back to the data, model version, and validation metric behind it.

Is Echo a self-serve platform, or does it come with support?

Echo is designed to be used alongside AuxoAI's Forward Deployed Engineers who help configure markets, review simulation design, and ensure outputs are decision-ready.