ZESTY LABS
Zesty Labs is a research and product studio focused on building practical systems that help people understand, organize, and act on the information that shapes their day. We work at the intersection of artificial intelligence, infrastructure, and human centered design - where insight becomes action, not noise. Our goal is not to chase novelty, but to create tools that feel obvious once they exist.
Modern software is powerful, but the path from raw inputs to clear decisions remains fragmented, fragile, and overly manual. Information is scattered across inboxes, dashboards, databases, and workflows, forcing people to spend more time managing systems than doing meaningful work. We believe the next generation of tools must be calmer, more legible, and dependable under real-world constraints—systems that reduce cognitive load instead of adding to it.
Our work spans applied research, core infrastructure, and product design, with a bias toward building things that ship and improve through use. We focus on durable systems rather than demos, and on architectures that can evolve without constant reconfiguration. Everything we build is meant to hold up over time, adapt to changing conditions, and earn trust through clarity, reliability, and thoughtful design.
Science is better when shared
Progress compounds when ideas are testable, reusable, and easy to build on. We publish notes, prototypes, and reference implementations as we go to help other brig the future of computational
Expect small artifacts frequently: write-ups, benchmarks, utilities, and patterns you can fork without ceremony.
GitHubFrīggs - Semantic AI for Real Work
Frīggs is Zesty Labs’ exploration into AI automation built on semantic understanding rather than brittle rules or static integrations. Instead of asking users to configure agents, workflows, or triggers, Frīggs focuses on understanding intent, context, and meaning across communication, documents, and systems. The goal is to reduce the cognitive overhead of modern work by allowing software to reason about what matters, what should happen next, and why—without requiring constant human intervention or fragile setup.
At its core, Frīggs treats workflows as evolving systems, not fixed pipelines. It models relationships between people, tasks, messages, and outcomes, enabling AI to make decisions that adapt as conditions change. This research is aimed at replacing inboxes, tickets, and task lists with higher-level decision surfaces that reflect how humans actually think and operate.
Energize - AI-Driven Grid Simulation & Energy Intelligence
Energize is a research initiative focused on simulating and understanding the real-world impact of large-scale energy production and demand on the electrical grid. Using real-time transmission line data, distribution system models, historical load patterns, weather data, and projected demand growth, Energize explores how new energy assets—such as renewables, storage, or high-demand consumers—interact with existing infrastructure.
The purpose of Energize is not trading or market speculation, but system-level intelligence: understanding constraints, failure modes, and optimization opportunities before they happen. Energy systems are high-stakes, high-frequency environments where bad decisions have real economic and societal consequences. Energize serves as a proving ground for AI systems that must reason under uncertainty, physical constraints, and long time horizons—capabilities that extend far beyond energy alone.
Large-Scale AI Data Systems - Foundations for Orchestrated Intelligence
This research track focuses on building large-scale data management and transformation systems designed to train, evaluate, and operate the input/output transformers that power Frīggs, Energize, and future Zesty Labs projects. Rather than training new foundation models, this work concentrates on how data is structured, filtered, transformed, secured, and fed into existing models to produce reliable, repeatable outcomes.
The goal is to create reusable tooling and frameworks that allow researchers and developers to work with massive, heterogeneous datasets—across time, domains, and trust boundaries—without sacrificing privacy or interpretability. Much of this work is intended to be shared openly, enabling others to build safer, more robust AI systems on top of these primitives. This layer acts as the connective tissue between intelligence and action, ensuring that models operate on high-quality signals instead of noise.