What Overstory’s vegetation intelligence reveals about wildfire and outage risk.
Updated
January 15, 2026 8:03 PM
Aerial photograph of a green field. PHOTO: UNSPLASH
Managing vegetation around power lines has long been one of the biggest operational challenges for utilities. A single tree growing too close to electrical infrastructure can trigger outages or, in the worst cases, spark fires. With vast service territories, shifting weather patterns and limited visibility into changing landscape conditions, utilities often rely on inspections and broad wildfire-risk maps that provide only partial insight into where the most serious threats actually are.
Overstory, a company specializing in AI-powered vegetation intelligence, addresses this visibility gap with a platform that uses high-resolution satellite imagery and machine-learning models to interpret vegetation conditions in detail.Instead of assessing risk by region, terrain type or outdated maps, the system evaluates conditions tree by tree. This helps utilities identify precisely where hazards exist and which areas demand immediate intervention—critical in regions where small variations in vegetation density, fuel type or moisture levels can influence how quickly a spark might spread.
At the core of this technology is Overstory’s proprietary Fuel Detection Model, designed to identify vegetation most likely to ignite or accelerate wildfire spread. Unlike broad, publicly available fire-risk maps, the model analyzes the specific fuel conditions surrounding electrical infrastructure. By pinpointing exact locations where certain fuel types or densities create elevated risk, utilities can plan targeted wildfire-mitigation work rather than relying on sweeping, resource-heavy maintenance cycles.
This data-driven approach is reshaping how utilities structure vegetation-management programs. Having visibility into where risks are concentrated—and which trees or areas pose the highest threat—allows teams to prioritize work based on measurable evidence. For many utilities, this shift supports more efficient crew deployment, reduces unnecessary trims and builds clearer justification for preventive action. It also offers a path to strengthening grid reliability without expanding operational budgets.
Overstory’s recent US$43 million Series B funding round, led by Blume Equity with support from Energy Impact Partners and existing investors, reflects growing interest in AI tools that translate environmental data into actionable wildfire-prevention intelligence. The investment will support further development of Overstory’s risk models and help expand access to its vegetation-intelligence platform.
Yet the company’s focus remains consistent: giving utilities sharper, real-time visibility into the landscapes they manage. By converting satellite observations into clear and actionable insights, Overstory’s AI system provides a more informed foundation for decisions that impact grid safety and community resilience. In an environment where a single missed hazard can have far-reaching consequences, early and precise detection has become an essential tool for preventing wildfires before they start.
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As workplace knowledge spreads across chats, AI firms are building systems that can structure, retrieve and preserve it over time.
Updated
May 11, 2026 5:24 PM
A messaging app on a phone. PHOTO: ADOBE STOCK
Votee AI, an enterprise AI company headquartered in Hong Kong, has partnered with its Toronto-based research lab Beever AI to launch Beever Atlas. The new platform is designed to turn workplace chats into searchable knowledge that AI systems can retrieve and understand.
The release focuses on a growing issue inside organisations. Much of today’s workplace knowledge now exists inside chat platforms such as Slack, Microsoft Teams, Discord and Telegram. Important discussions, project decisions and technical information often disappear into long message histories that are difficult to search later.
Beever AI developed the platform to organise those conversations into a structured system for AI assistants. The software connects with Telegram, Discord, Mattermost, Microsoft Teams and Slack, then converts conversations into linked records of people, projects, files and decisions.
The collaboration combines Votee AI’s enterprise infrastructure work with Beever AI’s research around AI memory systems. The companies are releasing two versions of the product. The open-source edition is aimed at individual developers, researchers and creators. The enterprise edition is designed for banks, government agencies and larger organisations with stricter security requirements.
The release also reflects a broader shift happening across the AI industry. Companies are increasingly looking at how AI systems store and retrieve long-term knowledge, rather than relying solely on large context windows or search-based retrieval.
Earlier this year, OpenAI founding member and former director of AI at Tesla Andrej Karpathy discussed the growing need for what he described as “LLM Knowledge Bases.” He argued that AI systems need structured and evolving memory rather than depending only on context windows and vector search.
Beever Atlas approaches that problem through workplace communication. Instead of focusing mainly on uploaded files, the system is designed around conversations that happen daily across team chat platforms. It can also process images, PDFs, voice notes and video files within the same searchable system.
The companies say the software is designed to work directly with AI assistants and coding tools such as Cursor, AWS Kiro and Qwen Code. Integrations for OpenClaw and Hermes Agent are expected later in 2026.
Pak-Sun Ting, Co-Founder and CEO of Votee AI said: "Hong Kong has always been known for property and finance. Beever Atlas is proof that world-class AI infrastructure can emerge from an HK-headquartered company and be shared openly with the world. Every growing organization faces the same silent liability: conversational knowledge loss. Beever Atlas turns this perishable resource into a compounding organizational asset."
A large part of the enterprise version focuses on privacy and access control. The system mirrors permissions from Slack and Microsoft Teams so users can only retrieve information they are already authorised to access. Permission updates are reflected automatically when access changes inside company systems.
The enterprise edition also includes audit logs, encryption controls and data retention settings for organisations handling sensitive internal data. Companies can run the software entirely inside their own infrastructure using Docker and connect it to their preferred AI models through LiteLLM.
The companies argue that organising information is more useful than simply storing chat archives. Jacky Chan Co-Founder and CTO of Votee AI said: "The key technical decision was to treat agent memory as a knowledge engineering problem, not a retrieval problem. Structure beats similarity — a typed graph of who works on what is more useful to an AI than vector search over a Slack archive."
The software also includes protections against prompt injection attacks and systems designed to reduce hallucinated responses. According to the companies, the AI is designed to return “I don't know” with citations when confidence is low instead of generating unsupported answers.
As workplace communication becomes increasingly fragmented across chat platforms, companies are beginning to treat internal conversations as information that AI systems can organise, retrieve and build on. Beever Atlas reflects a broader push to turn everyday workplace communication into long-term organisational memory.