For builders who go deeper

You wouldn't ship code you can't debug. Why ship AI you can't understand?

The teams building the most reliable AI agents aren't guessing what their models do. They're seeing inside them. Prysm gives you that vision — so you build with understanding, not hope.

Prysm AI — light passing through a prism to reveal AI model internals

See through the complexity. Understand what your AI is actually doing.

Works with your stack

LangChain
CrewAI
OpenAI
Anthropic
Meta / Llama
Hugging Face
AutoGen

The blind spot

Right now, you're building in the dark.

You've spent weeks perfecting your agent. The prompts are tight. The tools are connected. It works in testing. Then it hits production — and something breaks. A hallucination. A jailbreak. A response that makes no sense. You open the logs and start guessing. Eight hours later, you're still guessing. Not because you're not good enough. Because no one gave you the tools to actually see what's happening inside your model.

You're guessing, not debugging

When your agent fails, you can't trace WHY it failed. You read logs, tweak prompts, and hope. That's not engineering. That's gambling.

More vulnerable than you think

Jailbreak attacks succeed over 90% of the time against unprotected agents. One bad prompt can make your AI leak data, ignore rules, or go completely off-script.

You can't explain what you can't see

Your board asks how your AI makes decisions. Your compliance team needs an audit trail. Your customers want to trust it. You don't have answers — because you've never been able to look inside.

A different kind of builder

Anyone can wrap an API. Not everyone can understand what's inside.

There are two kinds of teams building AI agents. The first kind calls an API, writes some prompts, and ships. When it breaks, they shrug. The second kind goes deeper. They want to understand every decision their model makes. They don't just want their agent to work — they want to know WHY it works. Prysm is built for the second kind.

Without Prysm

Deploying and hoping
Debugging by guessing
Explaining by hand-waving
Shipping with anxiety

With Prysm

Deploying with understanding
Debugging with precision
Explaining with evidence
Shipping with confidence

What you get

See every decision your AI makes. Understand why it made it.

Understand, don't guess

See which internal patterns activated, which features fired, and why your model chose that response. For the first time, you'll actually know what your AI is doing.

Catch threats before your customers do

Prysm analyzes every prompt in real-time and blocks attacks before they reach your model. You set the rules. Prysm enforces them.

Debug in minutes, not days

Stop reading logs for hours. Prysm shows you exactly where things went wrong — which layer, which feature, which decision. What took 8 hours now takes 8 minutes.

Answer any question about your AI

When your board, your compliance team, or your customers ask how your AI works — you'll have the answer. With evidence.

What we're building

A new kind of observability. Built for AI.

We're building tools that let you look inside your AI models in real-time — not just at their outputs, but at the internal processes that produce them. Here's the direction we're heading.

Real-time model inspection

Watch internal feature activations as your model processes each request. See which concepts light up and which stay silent.

Prompt threat detection

Analyze incoming prompts for adversarial patterns, jailbreak attempts, and injection attacks — before they reach your model.

Explainability reports

Generate human-readable explanations of why your model made a specific decision. Built for compliance, audits, and trust.

Actively in development — early access coming soon

The best AI isn't built by the biggest teams. It's built by the teams who see the deepest.

Stop guessing. Start understanding. Join the builders who go deeper.

No credit card required. Be the first to know when we launch.

Built on research from

Anthropic

Sparse Autoencoders

OpenAI

Superposition Research

DeepMind

Circuit Discovery

MIT

Mechanistic Interpretability