Give every decision
a proving ground.

Native AI digital twins are dynamic models of consumers and audiences, grounded in real human evidence such as research, transaction data and reviews. Organizations query them, test scenarios against them and validate them as decisions evolve, without commissioning new research for each question.

Each twin population consists of individually modeled consumers with distinct attitudes, behaviors, budgets, preferences and contradictions. Every response is traceable to its underlying evidence.

General AI tools generate responses from broad, general-purpose knowledge. Native AI digital twins are built from real human evidence representing a defined audience. They deliver quantified results, accompanied by the evidence supporting each response.

The AI models that power the Native AI platform never train on client data. Native AI’s differentiation lies in the methodology surrounding those models. Native AI digital twins are:

• Grounded in real research, transaction data and market signals
• Validated against human benchmarks before informing decisions
• Limited to responses supported by their underlying evidence
• Traceable from each result to its original sources

In its 2025 Sample Landscape Report, EMI Research Solutions conducted a blind evaluation of synthetic data providers. Native AI achieved the highest alignment with real human responses of all providers evaluated and outperformed EMI’s internal general-purpose AI baseline.

Native AI digital twins draw on three layers of evidence: your selected intelligence, Native research and benchmarks, and market context. Each layer contributes additional context, making the twins increasingly native to an organization’s customers, category and strategic priorities.

• Your selected intelligence: research, segmentations, CRM records, transaction data and other proprietary evidence relevant to the audience or decision being modeled
• Native research and benchmarks: validated surveys, experiments, benchmark studies and other human evidence
• Market context: reviews, retailer data, forums, social discourse, demographic structure and other external signals

Native research and market context are established in advance, so engagements begin without any data sharing. Selected intelligence is incorporated where it strengthens a specific decision.

Native AI benchmarks its digital twins against real human responses before they inform decisions. Native’s standard parity target is within ±5 percentage points of the human result, and clients set alternative thresholds where a decision requires them. Validation reports present the twin result, the human result and the difference between them.

In a 2025 blind evaluation of synthetic data providers by EMI Research Solutions, Native AI demonstrated the highest alignment with real human data of any provider tested.

No. Native AI does not use your data, prompts, twin configurations or outputs to train models or to support other customers. The AI models that power the platform never train on client data. Your data grounds your organization’s twins and nothing else.

Yes. Native AI is SOC 2 compliant, and an independent auditor reviews its security controls. Each client organization operates in a tenant-isolated workspace, with data encrypted in transit and at rest. The SOC 2 report and supporting documentation are available to security and procurement teams under NDA.

Native AI supports strategic decisions across product launches, pricing, messaging, feature development, competitive positioning and sales readiness. Teams evaluate how target audiences respond to each scenario and identify the factors driving those responses before committing investment.

The platform serves insights and research, brand and marketing, product and innovation, strategy and sales functions, as well as research agencies and partners adopting AI-native methodologies.

Digital twins originated in engineering, where aircraft, vehicles, cities and power grids are modeled and stress-tested before capital is committed. Native AI applies the same discipline to business decisions, using digital twins of consumers built from real human evidence.

Business decisions have traditionally been made first and validated only after the investment. Digital twins give pricing, launch and messaging decisions the same proving ground engineering has long relied on.