Company
Relyance is a data privacy, security, and AI governance company with roughly 150 employees across the United States and India. Its work sits at the intersection of legal expertise and machine learning, which means internal validation workflows need to be both flexible and precise.
Overview
At Relyance, legal experts play a central role in validating predictions generated by the machine learning team. That review process is critical to the company’s broader AI governance work, but managing it efficiently at scale required more than spreadsheets could support.
After evaluating its options, Relyance chose Label Studio because the platform could be customized to fit a highly specific QC workflow. Three years later, that decision has helped the team improve annotation speed by an estimated 30 to 40 percent while giving legal reviewers a more structured system for high-stakes validation.
The Challenge
Relyance’s core Quality Control process depends on legal experts reviewing and validating model predictions. The work requires accuracy, traceability, and consistency, but the team’s earlier workflow in Google Sheets created friction as the process grew.
Spreadsheets made it harder to manage review work at scale and introduced bottlenecks that limited throughput. For a workflow where expert time is especially valuable, that inefficiency became increasingly costly.
At the same time, Relyance’s QC setup was not conventional. The team needed a system that could support a custom review experience rather than forcing the process into a rigid labeling interface.
AI governance workflows require more than a generic review tool. They need structure, traceability, and the flexibility to match how expert validation actually happens.
How Relyance Uses Label Studio Enterprise
A customizable interface matched a non-traditional QC workflow
Relyance moved quickly through the evaluation and purchase process, completing it in about two weeks.
Two factors made Label Studio the right fit. First, the team was already familiar with the open source version. Second, and more importantly, Label Studio gave Relyance the ability to fully customize the interface around its QC process. That flexibility was essential because the workflow did not fit a standard annotation pattern.
Custom pipelines connected BigQuery to expert review in Label Studio
Implementation took about one month. To support the workflow, Relyance built custom pipelines and automations that moved predictions from BigQuery into Label Studio.
The backend setup involved multiple teams. Data engineering handled the system connections and automations, while DevSecOps managed deployment and cluster operations. During rollout, the team also surfaced a cloud security concern and resolved it by moving to the on-premise version of the product.
The interface was simple enough to onboard reviewers quickly
While the backend integration required technical work, the reviewer experience itself was straightforward. The labeling interface proved intuitive enough that a single kickoff call was enough to get the annotation team up and running.
That ease of adoption mattered because the workflow depends on legal experts, not just technical users. The team needed a system that could support complex infrastructure behind the scenes without creating friction for the people doing the validation work.
Results
Three years into using Label Studio, Relyance has seen clear operational gains from moving off Google Sheets and into a purpose-built review workflow.
- Approximately 30 to 40 percent improvement in annotation speed
- Faster expert validation compared with the previous spreadsheet-based process
- A more structured and traceable QC workflow for legal review
- A customizable system that fits Relyance’s AI governance process
For a workflow where legal expert time is limited and high-value, that efficiency gain has had a meaningful impact.
Why It Matters
AI governance depends on more than model outputs alone. It also depends on whether expert reviewers can validate those outputs efficiently and consistently.
For Relyance, Label Studio became the foundation for a more scalable QC process. It replaced spreadsheet bottlenecks with a system that could be customized around legal review, connected into the company’s technical stack, and adopted quickly by expert validators. The result is a workflow that better supports the speed, traceability, and precision that AI governance demands.
