Dystr

Dystr operates as a developer of an AI-powered math and analysis platform tailored for non-software technical teams, particularly in mechanical and electrical engineering. The company was co-founded by CEO Nabeel Allana, who was motivated by the belief that engineers needed better tools to translate insights into impact seamlessly.

The core of Dystr's business is a browser-based, unified workspace that allows engineering teams to conduct complex analyses more efficiently. The platform is designed to automate workloads by combining purpose-built engineering computing with generative AI. This enables users to perform iterative calculations, run analyses with visualizations, and automate repetitive tasks without extensive coding knowledge. Users can describe their calculation needs in plain language, and the system handles the coding, leveraging the Python ecosystem. The platform supports natural language code manipulation, the use of triggers and scheduled tasks, and remote cloud environments that are provisioned as needed. It automatically parses various engineering file types, such as CAN bus logs and HDF5 datasets, removing the need for custom import scripts.

Dystr's business model appears to be subscription-based, offering different pricing tiers, including a free trial and plans with varying levels of storage, compute hours, and AI actions. Revenue is generated by providing these tiered plans to individuals and teams, with options for overage spending. The company targets its services at engineering teams in sectors like advanced manufacturing, aerospace, and defense. Dystr has secured venture capital funding from investors including Long Journey Ventures, Omni Venture Labs, Unpopular Ventures, and Female Founders Fund.

A significant feature is the platform's ability to turn raw analyses into live, formatted reports that automatically update as data evolves. This fosters collaboration by allowing team members to access, build upon, and reproduce technical analyses in one environment. Security is a key aspect, with the platform providing isolated virtual sandboxes for each workspace, encrypting all data at rest and in transit, and not using customer data to train its models. Dystr is scheduled for SOC2 Type II certification in September 2025 and offers enterprise deployment options like private VPCs and GovCloud configurations to meet stringent security and regulatory requirements.

Keywords: engineering analysis, AI-powered analysis, technical computing, hardware engineering, automation software, data visualization, collaborative workspace, calculation automation, engineering workflow, generative AI, mechanical engineering software, electrical engineering tools, data parsing, live reporting, cloud-based engineering, enterprise security, advanced manufacturing, aerospace technology, defense industry tools, python automation, natural language processing, simulation software