Scispot Raises $8M Series A to Build AI-Native Operating Layer for Life Sciences Labs
Key Insights
Scispot (search), a Kitchener-Waterloo-based AI-native lab platform, has secured $8 million USD in Series A funding led by Avenue Growth Partners (search).
The company's platform centralizes and automates experiment planning, execution, documentation, and inventory tracking for over 125 life sciences labs.
The funding will expand product, engineering, AI, implementation, and customer success teams to meet growing demand from biotech and pharma clients.
Scispot (search), a Kitchener-Waterloo, Ontario-based startup, has raised $8 million USD ($11.1 million CAD) in Series A funding to scale its AI-native operating layer designed for life sciences laboratories. The company, founded in 2021, helps biotech, pharmaceutical, and diagnostics teams automate digital lab work, manage millions of samples, and maintain operational traceability across their workflows. The all-equity, all-primary capital round was led by Washington, DC-based Avenue Growth Partners (search), with participation from existing Seattle backer Breakwater Ventures (search), bringing Scispot's total funding to nearly $10 million USD.
The platform addresses a persistent challenge in life sciences R&D: the fragmentation of data and digital workflows across spreadsheets, lab systems, robotics, hardware, and electronic notebooks. This fragmentation, according to Scispot (search) co-founder and CEO Guru Singh, makes it difficult for biotech and pharmaceutical companies to leverage AI and automation effectively.
"We started receiving more demand than we could really handle," Singh told BetaKit, noting that the company has achieved "clear product-market fit" with over 125 life sciences labs already using its technology. The startup now finds itself in a position where it must "say no politely" to many prospective clients, a dynamic that drove the decision to raise additional capital.
Platform Capabilities and Vision
Scispot (search)'s AI-powered software centralizes and automates the typically fragmented and time-consuming processes of experiment planning, execution, documentation, and inventory tracking. The platform connects instruments, samples, and workflows, turning physical lab activity into structured, traceable data for human review and AI integration.
The company's broader vision is to enable "self-driving labs," where routine coordination, data capture, analysis, and reporting are largely automated, while scientists and lab operators retain control over judgment, review, validation, and sign-off. "The goal is to solve this problem for the largest life science labs across the globe, but building from Canada," Singh said.
Funding Allocation and Growth Strategy
Scispot (search) plans to deploy the majority of its Series A capital toward what Singh described as a Palantir-like model, adding more forward-deployed AI engineers and scientists to its 30-person team to help clients adopt its technology. The company aims to expand its product, engineering, AI, implementation, and customer success teams, adding high-skill roles in Canada while scaling support for its growing base of life sciences customers across North America and globally.
Market Context and Timing
A molecular biologist by training, Singh previously held commercial leadership roles at Scientist.com, Science Exchange, and Labtwin before co-founding Scispot (search) with his brother and CTO Satya Singh, a former Expedia Group engineer. Singh noted that when the Y Combinator graduate first launched, "AI was still a luxury. It was not a painkiller," and labs were not prioritizing AI readiness. That changed dramatically with the release of ChatGPT in late 2022, after which interest in AI readiness spiked. "That's when we became a must-have," Singh said.
Currently, three-quarters of Scispot (search)'s customers and most of its investors are based in the United States. Despite the gravitational pull of Silicon Valley, Singh expressed a commitment to building a "durable" business from Canada, citing the country's "exceptional" talent pool and supportive government innovation programs as key factors in that decision.