Measuring what matters
in artificial intelligence

Probs AI builds safety measurement tools and open-source infrastructure for teams that need AI they can trust, quantify, and control.

Safety Research

Task decomposition and goal inference methods for evaluating AI safety during inference — measuring alignment before outputs reach users.

Enterprise Measurement

Patent-pending tools that quantify the ROI and safety profile of AI interactions through automated analysis.

Open Source

Developer tools built in public. MIT-licensed core with extensible architecture for teams that want full control.

Products

Shipping tools across the AI safety and measurement stack. Each product lives on its own domain.

Unblocks

Live Open Source

AI-native SaaS foundation. Open-core architecture with auth, billing, teams, and 12+ features pre-wired. TypeScript-first, built for AI agents and vibe coding.

unblocks.ai

NoProbs

Coming Soon Open Source

Agentic execution layer. Autonomous task completion with built-in safety constraints — the agent handles it, no worries.

ROIFY

Coming Soon

Enterprise AI ROI measurement through automated task decomposition. Quantify the real value of every AI interaction.

roify.com

Designating.AI

Coming Soon

Output control and hallucination prevention. Formally designate which AI outputs are acceptable before they reach your users.

designating.ai

Intellectual Property

15 patent filings including nonprovisional and international PCT applications in prosecution. Coverage spans AI safety evaluation, enterprise ROI measurement through task decomposition, multi-agent coordination and secure execution, and intelligent context management — with additional filings in foundational AI architecture.

15 Applications Filed across safety, measurement, infrastructure, and foundational AI
Nonprovisional Filed and in prosecution — AI ROI & safety measurement
PCT International Filed and in prosecution — international protection
Filing Activity April 2025 – present, ongoing

Our Approach

Most AI safety work happens before or after deployment — benchmarks that test known scenarios, or filters that catch outputs too late. We focus on what happens during inference: decomposing AI reasoning into measurable tasks, inferring goals from behavior patterns, and detecting misalignment before it manifests.

The same task decomposition methodology that powers our safety evaluation also enables precise ROI measurement — every AI interaction can be broken into constituent tasks, mapped to professional roles, and quantified in time and cost.

We believe safety should be built in, not bolted on. What gets measured gets managed.