Tessel, an innovative firm recently co-founded by Simon Reisch, is shaking up how enterprises build relationships with generative AI (GAI) models. By pushing past conventional research practices, Tessel hopes to reset expectations between model behavior and quantifiable business impact. This ground-breaking guidance underlines the importance of understanding how models are behaving. Tessel thinks this is important for tying AI performance to critical business metrics.
Where Tessel’s platform really sets itself apart is in how it approaches model behavior as a design-oriented, structured feedback engine. This combination gives teams the ability to create, test, and iteratively improve their AI systems with greater assurance. The company’s vision is to provide an evaluation and remediation platform that ensures model internals align seamlessly with specific use cases. As organizations continue to accelerate their adoption of AI, Tessel’s perspectives will be essential for deftly maneuvering the complicated avenues of model performance.
Bridging the Gap Between Models and Business Outcomes
Tessel understands this huge gap in the AI model lifecycle most companies go through to manage AI models. According to Reisch, “Most companies treat models like fixed black-box systems.” This narrow view obscures our ability to understand the underlying factors driving model behavior. Tessel is on a mission to address this gap. They offer resources that help enable practitioners and teams better analyze, understand, and improve their AI systems.
The company’s platform enables businesses to evaluate and remediate their models, ensuring that they remain aligned with their intended applications. This alignment serves as a foundation for developing trust in AI systems. This allows organizations to identify compliance issues before they become a bigger problem and rectify them in advance. Tessel has built a really impressive structured feedback engine. By capturing insights about model behavior, it’s enabling teams to better understand models and make better decisions based on real-time data.
Empowering Teams with Confidence
Learning how and why your model behaves the way it does is not just an academic pursuit, it directly affects business impact. Reisch emphasizes this point, stating, “Understanding why a model behaves a certain way allows teams to connect AI performance directly to their most important business metrics.” By offering a straightforward guidepost for assessment, Tessel equips teams with the tools necessary to tackle issues and maximize the potential of their AI systems before they become problematic.
Their cutting-edge approach enables organizations to tell the difference between individual errors and system errors. Reisch notes, “Without knowing the cause, you can’t tell whether it’s an isolated failure or a systemic flaw that could impact similar cases.” This ability will be absolutely critical for enterprises seeking to keep their day-to-day operations running smoothly while managing important AI model deployment risks.
A New Era of AI Evaluation
Tessel’s mission to improve the way we interact with AI fits perfectly with an increasing demand for transparency in machine learning models. The company values depth of understanding and clear, organized critical feedback. Together, these initiatives raise the bar for transparency and accountability in how companies develop and deploy AI technologies. With its one-of-a-kind platform, Tessel is at the leading edge of an industry that is desperately in need of transparency and accountability.
