HS Lab
Blade coating

AI-assisted automation for the next decade of production

Capture more. Improve faster.Innovate at any scale.

We build user-defined systems for pharmaceuticals, advanced materials, semiconductors, batteries, and research—turning previously invisible process information into repeatable production and faster innovation.

Discuss your process↗See automation in action
01Capture the invisible02Improve every cycle03Innovation for every team

About HuaSheng

HuaSheng, 花声—the sound of ideas coming to life.

HuaSheng represents the moment an idea begins to grow, takes form, and creates real-world impact. We believe the next generation of laboratories should do more than automate isolated tasks: they should sense, execute, learn, and improve as a connected system. Starting from real scientific and materials workflows, HS Lab integrates robotics, experimental equipment, process understanding, and AI-driven data loops into systems that are deployable, observable, and continuously optimizable—so science can run itself.

Hong Kong University of Science and Technology (Guangzhou) campus
Our origin

Born from HKUST(GZ)’s Bridge Program

HS Lab began in the Bridge entrepreneurship program at the Hong Kong University of Science and Technology (Guangzhou). HKUST(GZ)’s interdisciplinary environment brought automation, electronic and electrical engineering, materials science, AI, and entrepreneurship into the same working context, shaping our approach to turning scientific questions into deliverable engineering systems. Selected as one of the program’s Top 10 teams in 2026, we developed a clear path from project initiation to real customer validation: understand the science, translate experimental logic into an engineered system, and design for practical delivery from the beginning.

Why HS Lab

Materials insight. Automation engineering. AI intelligence.

Making an AI-enabled laboratory work requires more than excellent automation, materials expertise, or a powerful algorithm in isolation. Automation engineers know how to control equipment and integrate reliable systems, but may not understand how a materials question becomes a set of formulations, variables, and experiments. Materials scientists understand mechanisms and experimental logic, but may not be trained to translate those processes into robotic actions, control systems, and engineered platforms.

Our technical lead trained under the mentorship of a member of the Chinese Academy of Sciences and has long specialized in laboratory automation and artificial intelligence. Members of our R&D team graduated from leading universities worldwide, with foundations in automation and electronic and electrical engineering alongside interdisciplinary training in materials science and AI. This combination allows us to speak both languages: translating scientific intent into executable machine actions and turning process data back into the next experimental decision.

From research understanding to engineering implementation and on-site delivery, this complete capability chain is how HS Lab turns AI-assisted laboratories from a concept into a working reality.

From scientific questions to equipment actions, and from experimental data to the next decision—we make the entire loop run.
Selected Patent List

Engineering ideas protected through real filings.

Five Chinese invention applications and one US utility application covering automated coating, powder handling, and precise solid-liquid dosing.

5 Chinese filings1 US filing
Selected Awards
2026 International Exhibition of Inventions Geneva bronze medal diploma
Geneva · 2026

Bronze Medal, International Exhibition of Inventions Geneva

Recognized for our work on thin-film coating preparation and high-precision intelligent dosing.

2025 Guangzhou innovation and entrepreneurship competition excellence award trophy
Guangzhou · 2025

Excellence Award, HKUST One-Million-Dollar Entrepreneurship Competition

Awarded in the Guangzhou competition and its Hong Kong, Macao and Taiwan innovation and entrepreneurship division.

Demo of Product

Automation built around real materials workflows.

Three systems developed for previous users show how HS Lab connects robotics, process equipment, and controlled environments.

Each project is configured around the user’s process. Contact us to discuss system scope, interfaces, and technical details.

Selected Client Work

Proven across research and industry.

From university research to semiconductor and advanced-energy production, our client work demonstrates a practical model: start with the user’s real process, integrate equipment, sensing, robotics, and data intelligence, then deliver a system that can be observed, repeated, and improved.

Selected clients

AI4S market perspective

What we can measure today is only the market floor.

Laboratory automation is the starting point. As AI extends into R&D, production, and physical industries, it opens a much larger value space.

Measurable floor
5.1USD billion

Projected global laboratory-automation deployment market in 2030

This estimate mainly covers equipment and automation systems. Software, data, services, consumables, and value-sharing models remain largely outside the calculation.

From visible equipment markets to the full industrial value chainThe market loop is still forming
01

Equipment automation

Robotics · instrument integration · unmanned platforms

02

Software and data loops

Algorithm optimization · data assets · research agents

03

Ongoing services and consumables

Subscriptions · operations · specialized consumables · repeat purchasing

04

Industrial outcome value

Efficiency gains · yield improvement · value sharing

Hundreds of billionsPotential industrial space

The larger the industrial arena AI can enter, the larger the value space available to AI4S.

AI4SAI4S is not a narrow laboratory-automation category
It is the infrastructure layer that brings AI into physical industries

Any valuation based only on individual devices or present-day laboratory automation remains a conservative lower bound.

Contact Us

What could your processachieve if it could learn?

Tell us where your process is manual, variable, or difficult to measure. We will explore how automation, sensing, and AI assistance can turn it into a scalable advantage.

bpx823@gmail.com↗

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