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.
Keep Reading
A planned city explores how real-time data and automation can shape everyday urban systems
Updated
May 1, 2026 2:25 PM
A package being delivered by drone using the Meituan app. PHOTO: ADOBE STOCK
A newly built district in northern China is being used to test how cities function when infrastructure, data and automation are integrated from the ground up. In Xiong'an New Area, traffic systems, public monitoring and urban services are designed to respond in real time rather than operate on fixed rules.
At the centre of this is a traffic management system powered by more than 20,000 roadside sensors. These track traffic flow, vehicle types and congestion levels, feeding data into an AI system that adjusts signals in milliseconds. Official figures show this has reduced the average number of stops per vehicle by half. The system also detects equipment faults, sends alerts and generates maintenance requests without manual input.
Automation extends beyond roads. Drones are deployed across the city for routine monitoring. In the Rongdong district, roadside units release drones that follow fixed patrol routes of around 1.27 kilometres, completing each run in about five minutes. They are used to monitor traffic, detect illegal parking and inspect public spaces. Similar systems operate in parks to track water levels and issue flood alerts, while in some work zones, drones transport packages of up to five kilograms between buildings.
These applications reflect a broader approach: integrating multiple systems into a single, connected urban framework. Unlike older cities where infrastructure evolves in layers, Xiong’an has been built with coordinated digital systems from the outset. This allows transport, maintenance and public services to operate through shared data systems rather than in isolation.
Alongside this, the area is being developed as a technology and innovation hub. Since its establishment in 2017, it has attracted more than 400 branches of state-owned enterprises and over 200 companies working in sectors such as artificial intelligence, aerospace information and digital technology.
This ecosystem supports projects like the “Xiong’an-1” satellite, which completed research, design, production and testing within eight months of regulatory approval in 2025. The satellite is currently undergoing testing, with a planned launch expected in the second quarter of 2026. It forms part of a broader push to build an aerospace information industry in the region.
The area is also structured to bring companies, research and production closer together. At the Zhongguancun Science Park in Xiong’an, which spans 207,000 square metres, 269 technology companies operate across sectors including AI, robotics and biotechnology. The park hosts more than 2,700 researchers and industry professionals, with companies organised into sector-specific clusters.
Policy support continues to shape this development. In early 2026, the State Council approved the upgrade of Xiong’an’s high-tech industrial development zone to national level status, with a focus on attracting high-end research and strengthening links between scientific development and industrial output.
Xiong’an is positioned as a testing ground for how smart city systems can be deployed at scale. The model depends on coordinated planning, integrated infrastructure and sustained policy support. Whether these systems can be adapted to existing cities, where infrastructure and governance are more fragmented, remains an open question.