In most medical device companies, regulatory teams are overworked, under-resourced, and forced to reinvent the wheel every time a new submission begins.

You’ve done the research. You’ve written justifications. You’ve gathered testing strategies.
But when it’s time for the next device, that knowledge is scattered across spreadsheets, Word docs, old slide decks, or worse — in someone’s head who’s already left the company.

What if your past submissions weren’t just archived — what if they were assets?

What if your regulatory process could actually learn from itself?

The Problem: No Memory, No Momentum

Right now, most teams suffer from one major flaw: no regulatory memory.

Instead of getting smarter over time, regulatory strategy starts from scratch over and over again.

• Predicate research is redone from the ground up
• Justification language is rewritten, even if it worked before
• Institutional knowledge lives in silos, not systems
• Past reviewer feedback is rarely captured or reused

And every repetition isn’t just inefficient — it introduces risk, inconsistency, and delay.

The Opportunity: Learning From the Past, Automatically

In other areas of business, machine learning is already helping teams work smarter:

• Sales platforms optimise based on prior deals
• Marketing tools learn from past campaign data
• AI coding assistants improve with every new prompt

So what about regulatory?

Imagine a future where:

• Your system remembers which predicates worked best
• Past reviewer comments inform future strategy
• Testing frameworks are reused across similar devices
• Language from accepted submissions is suggested automatically
• Outcomes continuously refine your submission planning

That’s the power of machine learning feedback loops — and it’s coming to regulatory affairs.

Visualising the Future: Smarter Every Cycle

Here’s how the feedback loop works:

Machine Learning Feedback Loop in Regulatory Submissions

1. Data Input – Submission components, predicates, justifications, test plans
2. Submission – Built using insights from past wins (and misses)
3. Reviewer Feedback – Acceptances, pushbacks, questions
4. Learning – Patterns are refined and used to improve the next submission

Then the cycle repeats — smarter and faster each time.

Agent Astro: Building the Foundation for Submission Intelligence

Stage

What It Includes

Input

- Prior 510(k) submissions
- Predicate device data
- Reviewer feedback

Processing

- Pattern recognition
- Machine learning classification
- Language surfacing

Output

- Faster, more accurate submissions
- Pre-filled justification suggestions
- Stronger, precedent-based strategies

Agent Astro was built for this future.

We’re already helping regulatory professionals:

• Discover predicates and similar devices in seconds
• Surface language from successful FDA justifications
• Compare testing strategies across submissions
• Build a central, searchable knowledge base for internal teams

While we don’t claim to be a full machine learning engine — yet — every submission made with Agent Astro brings your team closer to that reality.

We’re not just indexing data. We’re creating the infrastructure for a smarter, more strategic regulatory future.

What This Unlocks for MedTech Teams

• ⚡ Faster submission prep (no need to start from scratch)
• 🧠 Stronger consistency across product lines
• 🔁 Reusable insights from past work
• 🔐 Institutional knowledge that survives team turnover
• 📈 Smarter strategy over time — even across multiple business units

This isn't just automation. It's institutional intelligence that compounds with every submission.

Final Thought: Submissions Shouldn’t Be Disposable

Right now, every regulatory submission is treated like a one-off project. Once submitted, it’s done.
But the best teams are starting to treat their submissions like code: versioned, reusable, and always improving.

If you’ve submitted five times, you shouldn’t be five times more tired — you should be five times more prepared.