Doppeliq builds AI-powered digital twins of a company’s existing customers, enabling businesses to run predictive simulations of behavior before spending on initiatives. Users can test pricing changes, campaign messaging, and other scenarios in natural language, leveraging the platform’s ability to process large volumes of transaction data and generate actionable insights.
Funding
Funding not disclosed
Founders
Product
Problem
Enterprises lack tools to reliably forecast how individual customers will react to pricing changes, marketing messages, or product launches before spending on campaigns, leading to costly trial‑and‑error and delayed decision‑making.
Solution
DoppelIQ transforms existing customer datasets into dynamic AI‑driven digital twins that emulate the behavior of real consumers at scale. Users interact with these twins through natural‑language prompts to simulate scenarios such as price adjustments, promotional offers, or new product introductions. The platform runs population‑scale simulations, generating predicted actions and quantifying the impact of each variable. It also surfaces the key drivers behind the simulated outcomes, enabling marketers and product teams to prioritize strategies with the highest expected ROI before allocating spend.
Target Audience
Primary customers are large enterprises and brands in retail, consumer goods, and services that need data‑driven insights for pricing, marketing, and product development decisions.
Features
- Automated creation of AI twins from any structured customer data source, leveraging over $6.8 M of transaction records
- Natural‑language simulation interface for testing pricing, messaging, and product concepts without coding
- Real‑time predictive analytics that output expected consumer actions and revenue impact for each scenario
- Driver analysis that identifies the most influential factors influencing simulated behavior
- Scalable architecture supporting hundreds of thousands of individual twins for enterprise‑level research
- Secure, cloud‑based environment ensuring data privacy and compliance while handling large transaction volumes