About AlignTrue

What

AlignTrue is the system of record for AI. Models commoditize, trust doesn’t. We focus on receipts and auditability, centering on deterministic behavior, envelopes-first design, and rebuildable projections so teams can trust and replay AI outputs.

The platform is beta and will change substantially as we dogfood. If something is broken, outdated, or missing, open a PR.

Why

TL;DR

• AI without receipts is a liability: If you can’t prove what it saw, why it decided, and what it did, you can’t trust it in production. Receipts turn “vibes” into accountability.
• Behavior drift is a death spiral: Agents copy inconsistency at machine speed across tools, teams, and time. A single system of record keeps behavior coherent and governable.
• If you can’t replay it, you can’t run it: Real operations require auditability, rollback, and deterministic recovery. Rebuildable history turns AI work into software.

AlignTrue is how we make AI trustworthy enough to run real workflows, not just write pretty text.

Details

I went deep with AI agents (especially when Sonnet 4.5 landed) and burned an irresponsible number of tokens fast. The power is obvious. So is the failure mode.

Most people aren’t failing because AI is weak. They’re failing because their guidance is scattered, inconsistent, and stale. Rules drift. Different agents see different instructions. Teams quietly invent their own “norms” and the AI just amplifies the chaos.

In regulated work, where you can’t ship what you can’t explain, I explored the problem from the interpretability side with GlassAlpha . That surfaced a deeper issue: if your rules aren’t aligned, nothing else is.

So, alignTrue starts further upstream: make it trivial to define, reuse, and sync rules everywhere so you get leverage instead of chaos.

I believe a meaningful chunk of future GDP growth is locked behind bad AI usage, not bad AI models. AlignTrue is how we fix that binding constraint to unlock what comes next 🚀

Who

I’m Gabe Mays . I love building tools that close the gap between AI hype and real output.