repliQ: AI-driven digital twin store for in-store testing
MAD Services · Marketing & Sales
Certification per AWS Marketplace.
Evidence tier Source Confirmed · 4 captures on record
What the publisher says
As described on AWS Marketplace.
RepliQ is an AI-powered market research solution that combines digital twin technology, behavioral science, and predictive analytics to simulate real-world shopping environments and observe authentic consumer decision-making. Rather than relying on what consumers say they might do, RepliQ measures what shoppers actually do within a realistic virtual store, providing brands with a more accurate understanding of purchase behavior and business impact.
Using realistic 3D retail simulations, RepliQ enables companies to evaluate products, packaging, pricing, promotions, shelf layouts, and category strategies before investing in costly market implementation. Respondents complete realistic shopping missions, allowing researchers to analyze how consumers navigate stores, compare alternatives, and make purchase decisions.
Show the rest of the publisher’s description (47 more lines)
**How It Works**
RepliQ leverages AI-driven digital twin simulations to recreate authentic retail environments and shopper behavior:
Digital twin retail environments: Realistic 3D store simulations recreate category structures, merchandising, product placement, and competitive context.
Behavioral observation: Captures navigation patterns, product interactions, attention, selection behavior, and purchase decisions.
Scenario testing: Compare packaging, pricing, promotional concepts, assortment changes, and shelf optimization initiatives.
AI-powered analytics: Behavioral data is transformed into actionable business insights.
Predictive business outcomes: Quantifies projected sales impact, penetration, loyalty, switching behavior, and basket dynamics.
**What Can Be Tested**
RepliQ supports a broad range of commercial and innovation challenges:
New product launches and innovation concepts
Packaging design and renovation projects
Pricing and price elasticity scenarios
Promotional mechanics and activation strategies
Shelf layouts and planogram optimization
Category management initiatives
Assortment and portfolio changes
Brand positioning and competitive performance
Engagement Process
Every RepliQ project follows a structured delivery process:
Scoping and consultation: Define business objectives, research questions, success metrics, and scenarios.
Simulation design: Build the virtual retail environment, including products, competitors, and test conditions.
Behavioral fieldwork: Consumers complete shopping missions while interactions are recorded.
Analysis and modeling: AI-powered analytics identify behavioral drivers and projected business outcomes.
Insight reporting: Delivery of recommendations, scenario comparisons, and strategic implications.
**What You Receive**
Research design and methodology consulting
Custom digital twin retail simulations
Baseline and test scenario evaluations
Behavioral shopper journey analysis
Predictive business impact modeling
Category and competitor performance insights
Sales, penetration, and basket impact projections
Executive-ready reporting and recommendations
**Why RepliQ Is Different**
Traditional market research often relies on surveys and stated intentions. RepliQ focuses on actual consumer behavior within a realistic shopping context.
Behavior over declarations: Measures what consumers do, not only what they say.
Realistic retail context: Evaluates products alongside competitors where genuine trade-offs occur.
Predictive business focus: Connects shopper behavior directly to commercial outcomes.
Faster decision-making: Tests multiple scenarios simultaneously.
Risk reduction: Identifies winning strategies before implementation.
Scalable experimentation: Rapidly evaluates multiple business hypotheses.
**REPLIQ is built with AWS infrastructure**
The tool leverages key services such as Amazon EC2 and Amazon S3 for compute and data storage, AWS Lambda and AWS Amplify for serverless execution and rapid deployment, and Amazon SageMaker to support advanced analytics and AI/ML workloads. This cloud-native architecture enables large-scale simulations, rapid scenario testing, and efficient processing of complex behavioral datasets, while maintaining flexibility as research needs evolve.
**Use Case Example**
A consumer goods company planning a packaging redesign can use RepliQ to compare several concepts within a realistic category environment. Researchers can observe attention, comparison behavior, switching patterns, and purchase decisions while predictive modeling estimates potential impact on sales, penetration, loyalty, and category performance.
**Getting Started**
Contact the team at research@madresear.ch to schedule a scoping consultation. The team provides end-to-end support covering research design, simulation development, fieldwork execution, analytics, and reporting.
Highlights
Highlighted by the publisher on AWS Marketplace.
RepliQ is an AI-powered market research solution using digital twin store simulations to capture real shopper behavior. It enables brands to test packaging, pricing, and shelf strategies in a realistic environment, delivering predictive insights on sales, penetration, and consumer decisions—helping reduce risk and optimize outcomes before market launch.
RepliQ transforms market research by simulating real shopping behavior in a digital twin store environment. It enables brands to test and optimize packaging, pricing, and shelf strategies in context, delivering predictive insights on consumer decisions and sales impact. By replacing assumptions with observed behavior, RepliQ helps companies reduce risk, accelerate innovation, and confidently launch high-performing solutions.
RepliQ enables brands to simulate real in-store behavior using AI and digital twin environments, revealing how shoppers navigate, choose, and purchase products. By testing multiple scenarios in a realistic retail context, it provides clear, predictive insights on sales and consumer decisions, helping companies optimize strategies, reduce uncertainty, and confidently implement high-impact changes with proven results.
Agent build and provenance
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Compliance
- FedRAMPConfirmedNot listed90%, registry-checkedNo FedRAMP Marketplace entry matched this vendor's domain, checked 2026-08-27registry recordas observed 2026-08-27
Confirmed means matched to a public authoritative registry. Claimed means the vendor or its listing states it, not yet cross-checked. A framework not shown was not found in any source we hold, which is not evidence against it. Not listed means a scoped registry check found no match for this vendor's domain: a No is a scoped registry check, not a compliance judgment. Confidence bands: 95% domain-verified, 90% registry-checked, 80% self-attested, 70% weak signal. Self-attested items marked “vendor's site” are gathered from the vendor's own website and are not verified by us.
Sources
Publisher resources
2 linksLinked repositories
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