Built for testing laboratories, QBench is a cloud LIMS that brings samples, workflows, lab data, inventory, reporting, quality processes, and client interactions into a single system.
From order placement through sample processing, results, and automated reporting, QBench supports every stage of lab operations. Lab teams configure their own workflows, data fields, and automation rules, so the software fits existing processes instead of forcing new ones. When requirements shift, labs can adjust fields, processes, and automations without depending heavily on custom code.
Instruments, software, and outside systems connect to QBench through file parsers and a robust API, which moves data automatically and limits repetitive entry and transcription mistakes. For reporting beyond the LIMS, labs can connect QBench to Tableau, Power BI, Domo, or Looker via REST API.
What labs get with QBench:
Sample and workflow management
Configurable workflows and custom data fields
Workflow automation
Instrument and system integrations
File parsing and API connectivity
Inventory management
A client portal
Automated reporting
Analytics and operational insights
Connections to Tableau, Power BI, Domo, and Looker
A built-in Quality Management System (QMS)
Because it runs in the cloud, lab teams get secure access to their data, workflows, quality management, and reporting in one centralized platform. Implementation and ongoing guidance come from a team that includes former bench scientists who understand laboratory workflows.
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SciSure is a Scientific Management Platform built to support the full range of laboratory operations for scientific organizations. It combines ELN, LIMS, and Health & Safety functionality, giving teams a single system to document experiments, track sample lineage, manage chemical inventory, and run structured, audit-ready compliance processes.
Instead of relying on disconnected systems, organizations get one governed platform that improves reproducibility, increases visibility into lab operations, and reduces risk as they scale.
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Eyezag
Eye tracking is a prevalent qualitative research technique utilized in market research, predominantly by larger firms that often operate their own dedicated laboratories. As a result, studies employing this method tend to be both costly and time-intensive, typically limited to a select number of projects and participants. In the context of analyzing user interactions on websites and interfaces, researchers gather visual data alongside mouse movement recordings. Due to the need for specialized and expensive equipment, participants generally must be present in a laboratory environment. Consequently, the high financial burden associated with eye tracking has led to its infrequent use in the UI/UX field, making researchers seek alternative methods that are more accessible and cost-effective. This lack of commonality in eye tracking usage underscores the challenges faced in integrating advanced research techniques into broader design practices.
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SentiGaze SDK
SentiGaze provides advanced gaze tracking capabilities utilizing standard webcams, allowing for real-time eye movement tracking or the option to record sessions for future evaluation. Users can create heat maps from individual or grouped sessions, seamlessly overlaying this data onto websites, applications, images, or videos, complete with live preview features.
This gaze data can be harnessed to manage software and devices, facilitating pointer-based interfaces across a variety of applications. It also enables the development of accessibility solutions tailored for individuals who require alternative input methods outside the norm of traditional tools.
Notably, the technology operates without the need for specialized cameras or head stabilization gear, as proprietary algorithms effectively detect and monitor eye movement from webcam footage without any physical contact. A quick calibration process adjusts the system for any screen size and viewing distance, requiring users to focus on specific points on the screen prior to each session.
SentiGaze excels in both single-user tracking and collective analysis, making it an ideal choice for user experience research and the advancement of accessibility initiatives, thus enhancing the interaction between users and their digital environments.
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