AI Operating Layer Targets Modern Lab Efficiency

Scispot, announced an $8 million USD Series A led by Avenue Growth Partners, a Washington, DC-based investment firm.

Scispot is already used by 100+ labs across biotech, pharma, diagnostics, CRO/CDMO, bioproduction, biobanking, and testing workflows. The company supports 250+ instrument types, 1,000+ experiments per month, and millions of samples across high-throughput labs.

Modern labs are under pressure to move faster, but much of their work is still split across disconnected instruments, spreadsheets, electronic lab notebooks, lab information management systems, scientific data systems, reports, dashboards, and manual handoffs. That creates a coordination gap. Teams spend time moving data, checking context, reconciling results, building reports, and making sure work can be traced. This slows experiments, decisions, and the path from lab work to market.

Scispot gives labs one governed operating layer to coordinate execution. The platform captures context as work happens, traces each step, automates routine digital work, and turns lab activity into structured data that teams and AI agents can use.

That makes Scispot useful beyond lab operations. For model builders, hyperscalers, and AI infrastructure providers working in life sciences, the hard problem is not only compute or model access. It is access to a governed, real-world lab context: sample lineage, instrument runs, protocol state, approvals, data provenance, exceptions, and human review. Scispot provides a model-agnostic context layer for labs, without forcing teams to lose control of their data or workflows.

“The next generation of labs will not be run by people stitching together instruments, spreadsheets, reports, and approval steps,” said Guru Singh, co-founder and CEO of Scispot. “They will run on an operating layer that connects every sample, instrument run, workflow, result, approval, and decision as the work happens. AI models are getting better, but they need a governed lab context before they can help with real scientific work. Scispot has built that layer, so scientists stay in control while the digital work around science moves automatically.”

For regulated and sample-heavy labs, speed cannot come at the expense of traceability or control. Teams need permissions, audit trails, sample lineage, instrument context, and human review built into the workflow. Scispot is designed to make those pieces work together, so labs can automate more of their digital work while keeping scientists and lab operators in control.

“The life sciences AI stack needs more than compute and models,” said Brian Goldsmith, Founding Partner at Avenue Growth Partners. “It needs the governed execution layer that turns physical lab work into a structured context. Scispot gives labs that layer, so AI agents can support real lab work

with traceability and control.”

Scispot’s long-term vision is the self-driving lab: a lab where routine coordination, data capture, analysis, and reporting run automatically on a governed operating layer. Scispot coordinates execution across instruments, samples, workflows, approvals, data, and AI agents, while scientists and lab operators stay in control of judgment, review, validation, and sign-off.

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