How DoppelIQ leverages AI to turn first-party data into actionable insights

In modern consumer businesses, decisions on pricing, product launches, promotions, and messaging happen at unprecedented speed, yet systems for understanding consumers have barely evolved. Most B2C companies still rely on surveys, focus groups, and panels, methods built for a slower commercial environment. 

Custom studies take four to eight weeks, cost $25,000–$65,000, rely on small samples, and deliver static insights that often arrive too late. Data quality further limits usefulness, with 38% of responses discarded for bias and 46% of companies updating personas only once every one to four years.

“The gap between how fast decisions are made and how slowly insights are generated has become unsustainable. Consumer behaviour now evolves in real time, driven by pricing, competitors, social signals, and shifting contexts, but most research systems still look backwards. When insights arrive weeks later, they’re no longer insights; they’re history. Brands need a way to understand consumers at the same speed at which they operate,” says Mohd Azam, CEO of DoppelIQ.

Traditional methods ask consumers what they might do, not what they actually do. Even newer AI persona tools struggle with credibility due to reliance on static or synthetic data.

“What we kept seeing was that teams were forced to choose between two imperfect options. You either worked with slow but real data that arrived too late, or fast but unreliable synthetic data that couldn’t be trusted for real decisions. Neither option genuinely helped teams make better choices under pressure,” he explains.

This gap led to a different approach, one that moves away from questioning consumers toward simulating how they behave based on historical behaviour.

What DoppelIQ is building

Founded in 2025, DoppelIQ is an AI-based market research and consumer intelligence platform built for B2C brands. Instead of running surveys, the platform uses first-party customer data to create AI-driven digital representations of real consumers. These digital consumers allow brands to simulate behaviour, test decisions, and generate insights on an ongoing basis rather than through episodic research cycles.

The Bengaluru-based platform integrates with enterprise systems such as CRMs, e-commerce platforms, customer data platforms (CDPs), and email tools. It ingests data, including purchase history, browsing patterns, engagement metrics, demographics, and campaign interactions. This data is processed through a multi-layer modelling system that converts raw inputs into behavioural models reflecting preferences, decision-making patterns, and brand affinity.

Crucially, the system is designed to operate even when data is incomplete. Machine learning models infer missing attributes using historical behaviour, peer cohorts, and population-level signals.

“No enterprise has perfectly clean or complete data. That’s just reality. So the system had to work with real-world messiness, not ideal assumptions. If your model only works when the data is perfect, it won’t work anywhere that matters,” Azam says.

Simulating consumers

Once digital consumers are created, teams can interact with them using natural language. Users can test scenarios such as pricing changes, new product concepts, messaging variations, loyalty programs, or discount strategies. Individual responses are aggregated to surface broader behavioural patterns across thousands or hundreds of thousands of simulated consumers.

Unlike traditional research, these models are continuously updated as new data flows in. As real customers change their behaviour, their digital counterparts evolve as well. Validation is carried out by comparing simulated outcomes with historical data, live campaign results, and controlled A/B holdout tests.

According to the startup, behavioural prediction accuracy ranges between 80% and 95%, depending on data depth and use case. Across benchmarks involving price sensitivity, value perception, and habit formation, DoppelIQ reports an average accuracy of approximately 83%, assessed against transaction outcomes, campaign results, and focus group comparisons.

“Accuracy was not optional for us. If predictions don’t consistently align with what happens in the real world, the product simply has no reason to exist. Speed without correctness just creates more confident mistakes,” Azam says.

Distinct use cases

DoppelIQ currently operates two product lines. The first, DoppelIQ Atlas, is a self-serve simulation platform powered by US Census data and large-scale synthetic populations. It enables teams to build custom audiences and test creative concepts, messaging, packaging, brand imagery, and perception. Atlas is designed for rapid experimentation and does not require first-party data integration.

The second offering, DoppelIQ Enterprise, is designed for large brands and retailers seeking deeper insight from their own customer data. It connects directly to CRMs and CDPs, creating digital twins of actual customers that evolve over time. This product supports use cases such as segmentation, purchase behavior modeling, price elasticity analysis, demand forecasting, loyalty and retention modelling, brand tracking, campaign evaluation, and competitor benchmarking.

“The distinction is intentional. Some teams need speed and directional learning, others need depth and precision rooted in their own customer base. The platform is designed to support both without compromising rigour,” Azam notes.

While building the underlying technology was complex, Azam says the more difficult challenge was earning trust. “We spent months validating outputs against real datasets and historical outcomes. The objective wasn’t just accuracy in one scenario, but consistent accuracy across industries, cultures, and decision contexts. If the system breaks when you change the category or geography, it’s not a decision tool; it’s a demo,” he says.

This emphasis on validation influenced the platform’s architecture, prioritising interpretability and grounding models in real consumer behaviour rather than relying solely on generative outputs.

The team behind

Azam brings over a decade of experience scaling SaaS businesses across the US, the Middle East, and India, and previously served as Vice President of Marketing at Lucidity, where he helped scale the startup to a valuation exceeding $100 million. Engineering is led by Mohd Amir, an enterprise technology veteran with experience across more than 100 product builds. 

The AI function is headed by Chief AI Officer Aslam Khan, a doctoral researcher in multi-agent systems with over eight years of experience building large-scale AI systems. The team is further supported by Ankur Mandal, former Director of Product Marketing at Lucidity and an IIM Calcutta alumnus, with advisory support from Aditya Sanghi, co-founder and CEO of Hotelogix.

The founding team came together through a mix of long-standing trust and deliberate professional alignment. Azam and Amir, cousins and former college peers, had spent years discussing technology and systems design.

“When DoppelIQ moved from an idea to execution, Amir was the first person I called. There was already trust, shared context, and a common understanding of how enterprise systems actually behave in production, not just on slides,” Azam says.

As the product vision matured, the team prioritised finding a leader who could combine academic rigour with real-world applicability. Through industry references, they connected with Aslam Khan, then pursuing his PhD in the US.

“We didn’t want something that was simply described as AI-powered. The goal was to build an AI-first system where intelligence is foundational, not layered on afterwards. The bar was shared thinking, trust, and the willingness to stay with difficult problems long enough to solve them properly,” he explains.

Market focus and build-vs-buy reality

The Indian digital twin market is projected to reach $10.99 billion by 2033, growing at a CAGR of 37.3% from 2026 to 2033, according to Grand View Horizon. DoppelIQ’s primary market focus is the United States, followed by the GCC region. The decision is driven by enterprise buying behaviour rather than geography.

“In the US, the economics clearly favour buying. Building a comparable AI system internally can cost over $2.5 million annually and take more than a year. Running a pilot on an existing platform costs significantly less and delivers usable answers almost immediately,” Azam says.

In contrast, Indian enterprises often attempt to build similar systems internally, resulting in longer adoption cycles and delayed ROI. GCC markets are showing increasing interest, particularly among large retail and B2C enterprises exploring AI-driven decision systems.

Long-term view

DoppelIQ remains early-stage and largely bootstrapped. Over seven to eight months, the founders invested approximately $35,000 (Rs 2.91 million) of personal capital, followed by a small angel round of around $50,000 (Rs 4.15 million) from industry operators. 

The team currently comprises nine members across geographies, with core AI, engineering, and GTM functions based in India.

Looking ahead, the startup plans to deepen adoption of its two core products while maintaining a focus on accuracy and trust.

“Our belief is that consumer research shouldn’t be episodic. It should be continuous, accessible, and embedded into everyday decision-making. That’s the only way insights can keep up with how businesses actually operate today,” he explains.

The startup operates in a landscape that includes traditional research firms such as Nielsen and Kantar, as well as newer AI-driven players like Quantilope, Civicom, and emerging synthetic persona platforms used for concept and message testing. While these tools either rely on slow, survey-led workflows or static AI-generated audiences, DoppelIQ positions itself around continuous behavioural simulation grounded in first-party data rather than stated intent.

“Our differentiation is simple. We don’t ask consumers what they think they’ll do; we model what they actually do, and we keep those models evolving as behaviour changes. That’s what makes the insights usable in real decisions,” says Azam.