Getting Started on R&D Infrastructure

Most biotech startups begin their digital life in chaos. Data lives in personal Gmail accounts. Protocols exist in someone's notebook. Results scatter across laptops. Everyone promises to organize it "when things calm down."

Things never calm down. They get worse.

I've watched dozens of companies try to fix this problem after they've already generated two years of messy data. It's like trying to organize a library after someone dumped all the books in a pile and removed half the covers. You can do it, but you'll waste months, and you'll never fully recover.

Here's how to build infrastructure from day one that grows with you instead of strangling you.

Step 1: Map Your Company's Brain

Before you touch a computer, map out how information flows through your company. Who generates data? Who needs to see it? Who makes decisions based on it?

This isn't an org chart. It's a data model. Write down every type of information you create: experimental protocols, instrument readings, meeting notes, supplier contracts, patient records. Map where each type comes from and where it needs to go.

Now build this map into a digital workspace. Use Notion if you want something that works immediately. Try AppFlowy or Outline if you prefer open-source tools you can control. The specific tool matters less than the discipline of creating a single source of truth.

Most founders skip this because it feels like administration. It's the opposite. You're building the nervous system of your company. Every experiment you run, every decision you make, will flow through this system. Build it wrong and you'll feel the pain for years.

Step 2: Buy a Real Computer

Stop trying to run your company off laptops. Buy a proper machine that sits in your office and never moves. Not a server from Dell that costs $20,000. A high-end desktop PC. Something with 64GB of RAM, a decent processor, and enough storage for the next two years.

This one purchase, maybe $3,000, transforms what you can do. You can run databases without paying Amazon. You can experiment with AI models without sending your data to OpenAI. You can automate workflows without asking anyone's permission.

A three-to-ten person team can run entirely off one good machine for their first year. You'll move to the cloud eventually. But starting local means you understand your needs before you start paying cloud bills that look like mortgage payments.

Step 3: Learn to Run a Database

This is where scientists usually rebel. "I don't want to be a database administrator," they say. "I want to do science."

But here's the secret: running a local database in 2025 is easier than maintaining a lab notebook. Install PostgreSQL. Follow a tutorial. In one afternoon, you'll have a system that's better than Excel for everything Excel does, plus a thousand things Excel can't do.

Connect your instruments to it. Store your experimental results in it. Build simple web forms for data entry. Once your data lives in a real database instead of files, everything else becomes possible. Analyses that took hours take seconds. Reports generate themselves. Patterns emerge that you'd never spot in a spreadsheet.

(I'm writing a detailed guide on this. But don't wait for it. Start now. Make mistakes. You'll learn faster by doing than by reading.)

Step 4: Get Developer Expertise

Start with one developer who becomes part of your company. Even part-time. Even remote. Just get someone technical who owns your infrastructure.

Scientists always resist this hire. "We can't afford it." "We don't have enough work for them." "We'll hire one when we're bigger."

You're thinking about it wrong. A good developer doesn't just write code. They become the bridge between your science and your systems. They automate the experiment you run every week. They build the dashboard that shows whether your process is improving. They connect your instruments so data flows automatically instead of through manual copying.

Find someone who's curious about biotech, not just coding. Someone who asks why you need something, not just how to build it. That person is hard to find. You need someone who understands both Python and pipettes, databases and dose-response curves. If you're lucky enough to find them, hire them immediately.

If you can't find that unicorn, or if you want expertise across hardware integration, AI, and compliance without hiring a whole team, Aradon offers gradual infrastructure support designed for startups.

Why This Order Matters

Notice what I'm not telling you to do. I'm not saying "move to the cloud" or "adopt microservices" or "implement CI/CD pipelines." That's infrastructure for companies that already know what they're building.

You don't know what you're building yet. Your core discovery might come from experiment number 10 or experiment number 1,000. Your infrastructure needs to be flexible enough to support both paths.

Starting with local infrastructure gives you that flexibility. You can experiment without committees. You can fail without consequences. You can rebuild without migration projects.

The Hidden Benefits

When you control your own infrastructure, magic happens.

You can try that sketchy AI model without your data leaving the building. You can run analyses that would cost thousands on AWS. You can build custom tools for your specific workflows instead of cramming your science into someone else's system.

More importantly, you learn what you actually need. Every startup that rushes to the cloud ends up paying for services they don't use, struggling with complexity they don't need, and locked into decisions they made before they understood their requirements.

Your local server becomes your laboratory for infrastructure. You discover that you run protein folding simulations every night, so you need GPU power. You realize that your bioinformaticians query the same datasets repeatedly, so you need caching. You notice that your data grows by 100GB monthly, so you need a storage strategy.

When you do move to the cloud, you'll know exactly what to buy.

The Uncomfortable Truth

Here's what nobody admits: the companies with the best infrastructure started building it when they had no money, no customers, and no certainty they'd survive.

They didn't wait for Series A to hire that developer. They didn't wait for product-market fit to organize their data. They built infrastructure like they built their science: carefully, systematically, from first principles.

Your infrastructure is not separate from your R&D. It is your R&D. Every insight you'll have, every discovery you'll make, will come from data. The better you can capture, store, analyze, and understand that data, the faster you'll move.

Start today. Buy that server. Map that data model. Hire that developer. Six months from now, while your competitors are still emailing Excel files, you'll be building the future.

If you need help getting started, we made this for you: https://aradon.bio/for-startups