Four months ago, Carl and I started Starseer with a realization that felt obvious to anyone with a cybersecurity background: if you’re going to deploy AI systems that make real decisions, you’d better understand how they actually work. Today, I’m excited to share that Gula Tech Adventures agrees! They’re leading our $2M seed round, with participation from strategic angels and other notable investors who’ve seen this movie before in other critical infrastructure.
“Where are your logs?” is so oft-repeated by digital forensics and incident responders that it’s become a meme. Yet when it comes to AI, there’s been a widely accepted mindset of “we don’t know how, but it works.” Progress continues to be made, but with minimal understanding of why. No cybersecurity professional would accept deploying systems without logs, monitoring, or debugging capabilities — yet somehow this has become the norm in AI.
This is where cybersecurity brings a unique opportunity to AI interpretability. Moving from zero to basic visibility unlocks unprecedented data that can be used for audit trails, compliance documentation, vulnerability discovery and remediation, threat intelligence on model-specific attacks, and understanding why defenses fail when they do.
Read More – Vanta has raised a $150M Series D and is now valued at $4.15 billion
We can take any model (whether it’s the latest large language model, an image classification model, or something custom you built in-house) and give you unprecedented visibility into how it actually works. Not just “here’s what the model outputs,” but “here’s how it made that specific decision, here’s what parts of the input it was paying attention to, and here’s how you can verify it’s behaving as expected.”
What’s really exciting is that we built this to work with your existing setup! Whether you’re running models in a private cloud or on-premises, whether they’re open-weight models from Hugging Face or something you developed internally, our tools integrate without requiring you to rebuild your entire AI stack.
We’re using this funding strategically:
- Accelerating platform development to handle the increasingly complex AI deployments our customers are running
- Expanding our engineering team with people who share our obsession with making black boxes transparent
- Building out go-to-market to reach the enterprises and government agencies who need these capabilities yesterday
The response from early customers has been overwhelming. We’re working with teams in finance, healthcare, manufacturing, and defense who are discovering that their existing security and compliance frameworks weren’t built for AI’s unique risks.
Read More – april has raised a $38 million Series B round led by QED
