FlashQuery is an enterprise-grade AI integration platform originally developed within Planorama Labs and later spun out into its own company. It is designed to help software teams move generative AI initiatives from early proof-of-concept work into reliable, production-ready features.
Built as a control and integration layer between applications, language models, and data sources, FlashQuery addresses the practical challenges of deploying LLM- and RAG-based capabilities in enterprise environments. It provides support for authentication, role- and tenant-aware data access, security and governance requirements, and both single- and multi-tenant architectures, enabling teams to integrate AI features with confidence and operational discipline.
Sinfonia is a structured information hub designed to serve as a durable source of truth for complex systems. Rather than replacing existing tools, it connects and organizes critical artifacts such as requirements, plans, design elements, and constraints, while linking to external documentation, repositories, and systems where detailed work already lives.
Originally developed to manage product requirements, Sinfonia was intentionally designed to address a broader class of engineering problems. It is actively used for product development today and likewise hardware design and semiconductor verification, where teams must manage verification plans, design artifacts, and traceability across highly interdependent systems.
By treating structured information as a first-class engineering asset, Sinfonia enables AI-augmented workflows such as analysis, consistency checking, gap detection, and impact assessment. This makes it an extensible platform that helps teams across industries reason about what they are building, reduce ambiguity, and maintain alignment as systems evolve.
AIMEE, the Artificial Intelligence Media Evaluation Engine, is a purpose-built system for analyzing broadcast news content at scale. It applies advanced AI techniques to evaluate what news organizations are actually publishing, focusing on content characteristics such as topic coverage, style, tone, frequency of mention, and geographic distribution.
Designed to address real analytical gaps in media evaluation, AIMEE enables structured, apples-to-apples comparison of news content across stations, markets, and time periods. Rather than measuring audience engagement, it focuses on the substance of the content itself, providing a more objective view of how narratives, priorities, and coverage patterns differ across the media landscape.
AIMEE has been applied in real-world evaluations involving major television news networks in the United States, demonstrating its ability to operate reliably at scale and deliver actionable insights in complex, data-intensive environments. The system reflects Planorama Labs’ approach to applying AI where it can solve concrete problems and produce defensible, decision-ready analysis.
Electronic Design Automation, or EDA, is a class of highly specialized software used to design and verify semiconductor chips. Traditional commercial EDA platforms are expensive and complex, placing them largely out of reach for teams without significant capital and deep in-house expertise. This creates a high barrier to entry for innovation in hardware design.
The OpenROAD Project, originating from the UC San Diego VLSI CAD Laboratory, is an industry-supported effort to change this model by enabling an open, automated flow from RTL design to GDSII layout. Its goal is to reduce both cost and complexity while making advanced chip design workflows accessible to a broader range of engineers and organizations.
Planorama Labs spearheaded the development of OpenROAD Cloud, a SaaS platform that brings the OpenROAD toolchain into a production-oriented, browser-based environment. By making RTL-to-GDSII workflows easier to manage, execute, and reproduce, OpenROAD Cloud demonstrates how open EDA can be operationalized to solve real engineering problems at scale.
Launched in 2022, this generative AI application was an early exploration of how language models could assist product managers in defining and elaborating product requirements. It focused on reducing ambiguity and accelerating the transition from initial ideas to clearer, more actionable documentation.
Beyond its immediate utility, the system served as a proving ground for AI-assisted workflows in a real product context. The lessons learned directly informed the design of Sinfonia, making this effort both a practical tool for teams and a foundational step toward a broader platform for managing structured engineering information.
