Labric: The AI-Ready Data Layer Transforming Scientific Research

In an era dominated by artificial intelligence, many industries have evolved dramatically — yet scientific research labs still struggle with outdated data practices. Labric, a 2025-founded startup in the Spring Y Combinator batch, is on a mission to modernize the way science works by addressing the most fundamental yet overlooked bottleneck: data infrastructure.

With just two founders — Caitlin Hogan and Connor Hogan — Labric is building what they call the “data layer for scientific research.” The company captures raw, fragmented lab data, cleans and contextualizes it, and makes it AI-ready. The result? A smarter, faster, more collaborative research ecosystem where scientists can finally spend their time on discovery, not data wrangling.

Why Is Scientific Data Infrastructure Still Stuck in the Past?

Despite massive technological advances in fields like genomics, materials science, and bioengineering, many top-tier labs remain shockingly analog when it comes to data.

Instrument output is often saved in unstructured formats like Excel files.
Researchers email data back and forth or store it in Google Drive folders.
Siloed databases — if they exist at all — are frequently custom-built, rarely maintained, and almost never compatible with AI or modern analytics tools.

The implications are massive. Valuable data becomes hard to find, collaboration breaks down, and reproducibility — the cornerstone of scientific integrity — becomes a challenge. In this context, even world-class researchers waste countless hours cleaning, copying, and reorganizing files instead of running experiments or analyzing results.

How Does Labric Solve the Problem?

Labric is not trying to replace lab workflows — it’s trying to supercharge them. Its platform serves as a centralized hub for lab data, offering modern tools designed to meet scientists where they are, without forcing dramatic process changes.

Here’s how it works:

Instrument Data Streaming: A lightweight application installed on lab computers continuously streams data from scientific instruments into a central, structured platform. This eliminates manual file transfers and messy folder hierarchies.
Data Consolidation: Labric doesn’t demand labs abandon their current tools. It integrates with common platforms like Notion, Google Sheets, Excel, and Drive to consolidate data across multiple sources.
Web Interface: Researchers can access all lab data through a searchable, intuitive web interface. No more guesswork or digging through spreadsheets — just actionable data, instantly accessible.

And at every layer of this stack, AI is built in. Labric ensures that once data is captured and structured, it’s ready to power advanced analytics, predictive modeling, and automated discovery.

What Makes Labric’s Approach to AI Different?

The current hype around AI often emphasizes flashy tools or surface-level automation. Labric is different — it believes the true power of AI in science lies in deep infrastructure.

AI only works when it has access to clean, contextual, and consistent data. Without this foundation, even the most powerful models are limited. Labric focuses on building that foundation — the “missing layer” that transforms lab-generated data into something AI can meaningfully interact with.

This isn’t about giving scientists a chatbot or an AI assistant. It’s about fundamentally reshaping the research pipeline so that AI becomes a trusted partner in scientific progress — analyzing trends, spotting patterns, and even suggesting new lines of inquiry.

Why Does This Matter for the Future of Scientific Discovery?

Science is under pressure. From drug discovery to climate modeling, the world urgently needs research to move faster and more effectively. But most labs operate under tight budgets, outdated tech, and personnel shortages.

By modernizing lab infrastructure and embedding AI into the research process, Labric offers a way to amplify output per researcher, not just incrementally, but by an order of magnitude. The founders believe their platform can unlock a 10x to 100x improvement in scientific productivity per dollar spent.

Imagine:

Faster iteration cycles.
Seamless collaboration between labs and institutions.
Reduced duplication of effort.
More reproducible results.

This isn’t speculative — early adopters of Labric are already seeing tangible gains in experiment throughput, collaboration, and insight discovery.

Who Are the Founders Behind Labric?

Labric isn’t the product of theory — it’s built by a team that has lived the problems it solves.

Caitlin Hogan spent four years leading data infrastructure at a materials science startup. She saw firsthand how difficult it was to manage, structure, and make sense of lab data — and how transformative the right systems could be when properly implemented.

Connor Hogan brings deep software and AI experience to the table, having worked on database systems at Google. He’s passionate about both the scientific method and cutting-edge technology, and Labric is where those worlds collide.

Together, they’ve created a tool that combines empathy for the research workflow with best-in-class engineering — a rare and powerful combination.

How Is Labric Different from Other Lab Data Tools?

While other solutions exist for lab data management, many suffer from common pitfalls:

Too rigid: Forcing scientists into new workflows they don’t want to adopt.
Too generic: Offering “one-size-fits-all” platforms that don’t understand scientific nuance.
Too limited: Focusing only on storage or visualization without enabling AI or automation.

Labric avoids these traps by being:

Instrument-agnostic: It works with the tools labs already use.
Non-invasive: It fits into existing systems instead of replacing them.
AI-native: It’s built with structured, AI-ready data at its core.

In other words, Labric is not just “software for labs” — it’s infrastructure for next-generation scientific discovery.

What’s Next for Labric?

Still early in its journey, Labric is already gaining traction among forward-thinking research institutions. The team is expanding features, building new integrations, and refining AI models to make the platform even more powerful.

As more labs adopt Labric, the company envisions a world where:

Research data is no longer locked in silos.
Collaboration across disciplines and institutions becomes seamless.
AI moves from a buzzword to a real partner in the scientific process.

For Caitlin and Connor, this isn’t just a startup — it’s a mission to help science catch up with the tools it deserves.

Why Should the Scientific Community Pay Attention?

Scientific research has long been the engine of progress. But without modern infrastructure, even the best minds in the world can be slowed down by fragmented workflows and poor data hygiene.

Labric provides a pragmatic, scalable, and AI-powered solution to this crisis. By unlocking the full potential of lab-generated data, it paves the way for faster discoveries, better experiments, and a new era of scientific acceleration.

For researchers tired of wrestling with Excel files, for institutions looking to modernize their data stack, and for funders seeking higher ROI from research investments, Labric isn’t just a useful tool — it’s a glimpse into the future of science.