Update: This article has been updated as we have been informed that the funding round was a different amount.
Expert Intelligence, a Santa Clara-based startup developing AI systems that automate expert decision-making in regulated laboratory settings, has raised a $5.8 million seed round led by Sierra Ventures, with participation from TSVC and Acorn Pacific Ventures. The company positions its platform as “foundational infrastructure” for autonomous decision-making in high-compliance environments where data is scarce, tightly controlled, and difficult to use for conventional machine learning approaches.
Founded by Lalin Theverapperuma, Ph.D., a longtime AI and machine learning engineer with experience at Apple, Meta, Intel, and Bosch, Expert Intelligence is targeting a persistent bottleneck in lab operations: while laboratories have spent heavily on sophisticated instrumentation, many critical determinations still depend on slow, manual human review. Expert Intelligence argues that this reliance on expert analysts constrains throughput and consistency, particularly in workflows where auditability, traceability, and regulatory readiness are non-negotiable.
The company’s technical thesis is that most AI approaches struggle in regulated labs because they depend on large labeled datasets or operate on downstream reports rather than the raw instrument outputs, where expert judgment is formed. Expert Intelligence says its system instead works directly on raw instrument data, aiming to learn how analysts interpret signals and make decisions in context, using relatively few samples.
At the center of its approach is what the company calls the Limited Sample Model, or LSM, designed to learn expert decision processes from a small number of examples. The company frames LSM as an alternative to large language models and traditional machine learning pipelines that typically require broad training sets. By learning directly from limited data, Expert Intelligence says it can better fit environments where data access is constrained, variability is high, and compliance requirements demand transparent, auditable outputs.
The company reports commercial deployments beginning in early 2025 and says it has secured customers across analytical testing workflows in pharmaceuticals, drug manufacturing, and food and beverage safety. It describes current use cases, including automated result review, anomaly detection, and improved decision consistency across expert-level tasks. Over time, Expert Intelligence plans to expand into other lab-driven and industrial domains, including areas such as diagnostics, environmental monitoring, advanced materials, and chemical manufacturing.
Proceeds from the seed round will be used to accelerate customer expansion in pharma, deepen integrations across laboratory systems, and build out go-to-market and customer success capabilities. The company is effectively betting that regulated labs will adopt automation not just at the reporting layer, but at the decision layer, provided the systems are built to handle limited data and can satisfy audit and compliance expectations from the start.
KEY QUOTES:
“Expert Intelligence is building foundational infrastructure for autonomous decision-making in some of the most demanding environments in the world. Their ability to learn from limited data and operate at the instrument level unlocks a category of automation that simply wasn’t possible before in regulated labs.”
Ben Yu, Managing Partner, Sierra Ventures
“Labs have invested heavily in instrumentation, but critical decisions still bottleneck on human review. We built LSM so regulated labs can scale expertise with accuracy, transparency, and audit readiness from day one. Our models learn directly from how experts interpret raw signals, allowing labs to increase throughput without introducing new compliance risk.”
Lalin Theverapperuma, Ph.D., Founder and CEO, Expert Intelligence
