Bussiness Ideas

Acutus AI is building a Synthetic Data as a Service (SDaaS) platform that empowers businesses to safely simulate, test, and train AI systems using high-fidelity, privacy-preserving synthetic data. Our core innovation lies in creating domain-specific, AI-generated datasets that mimic real-world behaviors without compromising user privacy or relying on historical, biased, or incomplete data. The biggest bottleneck for AI innovation today is access to diverse, high-quality data that is compliant with privacy regulations like GDPR, HIPAA, and CCPA. Traditional methods of data collection are time-consuming, expensive, and often biased or unusable due to legal constraints. Acutus AI solves this by enabling enterprises to define scenarios using a Domain-Specific Language (DSL) that maps real-world events, behaviors, and personas into dynamic simulations. These scenarios are then powered by advanced generative models (GANs, VAEs, and LLMs) to create synthetic datasets for model testing, validation, and training. Our platform is industry-agnostic by design, but focuses on high-impact verticals like: Healthcare: Generate synthetic EHR data, simulate clinical trials, and model patient journeys. Financial Services: Simulate KYC processes, fraud patterns, and stress-test risk models. Logistics & Supply Chain: Create synthetic IoT, demand forecasting, and delivery failure datasets. CX (Customer Experience): Model user journeys across digital channels without using real customer data. We are building a multi-layered product: Core Engine – A robust synthetic data generator with built-in compliance filters. Scenario DSL – A no-code/low-code layer for defining industry-specific simulations. LLM-powered Co-Pilot – Auto-generates test cases, data validation logic, and scenario expansions. Analytics Suite – Provides bias detection, data drift analysis, and explainability reports. Marketplace (2028+) – Enables sharing and monetization of synthetic datasets and test scenarios. With synthetic data, businesses can reduce costs by 30–50%, cut testing cycles in half, and access data that would otherwise be inaccessible due to legal or ethical restrictions. Our go-to-market strategy includes a freemium API model for developers, enterprise modules for regulated industries, and strategic partnerships with CX, QA, and data platforms. By 2034, we aim to become the platform of record for AI simulation, testing, and governance, generating $100M+ in recurring revenue and shaping how intelligent systems are trained and validated across the globe.

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Startup Teams

• 1 Udita Bhattacharya (Director)
• 2 Ankesh Saxena (Director)

Incubation Details

Not Incubated