Business model of DualBird

Aspect	Details
How DualBird Started	Founded in 2022 by Gilad Tal (CTO), Amir Gilad (CEO), Ehud Eliaz, and Ohad Gamliel—former Amazon AWS and semiconductor industry veterans. Emerged from stealth in November 2024 with technology leveraging FPGAs for data acceleration. Supported by Intel Ignite acceleration program.
Present Condition	Series A stage with $25M total funding (combined Seed and Series A). Headquartered in Boston, USA with operations in Israel. Team size: 2-10 employees. Currently in pilot phase with select enterprise customers, targeting general availability in early 2026.
Future of DualBird and Industry	Vision: Process every byte of data through DualBird within 5 years. Industry trend: Data processing shifting from general CPUs to purpose-built processors (similar to AI’s shift to GPUs). McKinsey forecasts $7 trillion data center investment needed by 2030, creating massive market for cost-reduction solutions.
Opportunities for Young Entrepreneurs	Data infrastructure market experiencing explosive growth driven by AI workloads. Opportunities in: cloud-native acceleration, FPGA/specialized hardware solutions, enterprise cost optimization tools, seamless integration plugins for existing systems. Focus on solving expensive enterprise bottlenecks.
Market Share of DualBird	Early-stage startup in competitive market with 3,066 active competitors including EXL, Tredence, and MathCo. Ranks 197th among 127 funded competitors. Yet to establish significant market share but positioned for rapid growth with unique FPGA-based approach.
MOAT (Competitive Advantage)	1) Unique “fused stack” merging hardware and software architecture; 2) 10-100x performance gains with 50-90% cost reduction; 3) Zero-migration deployment via simple plug-in; 4) Founding team’s deep AWS and semiconductor expertise; 5) Leverages existing cloud FPGA infrastructure; 6) No manual tuning required.
How DualBird Makes Money	Software-as-a-Service (SaaS) model: Charges enterprises for cloud-native acceleration platform access. Revenue likely based on usage (compute hours saved), infrastructure costs reduced, or subscription tiers for Apache Spark/Iceberg workloads. Targets enterprises spending millions on data processing infrastructure.