LLMs generate valid-looking outputs that still break applications — so I built this
I spent the last few weeks building a small project called AI Reliability Engine.
The idea came from a problem I kept seeing with LLM-powered applications:
An AI output can look perfectly valid but still break downstream systems because of:
- Missing required fields
- Incorrect data types
- Malformed JSON
- Schema mismatches
- Unexpected output structures
So I built a validation layer that sits between the LLM and your application.
Current capabilities:
• Schema validation
• Missing field detection
• Risk scoring
• ALLOW / WARN / REGENERATE decisions
• Interactive playground for testing outputs
The goal is simple: prevent unreliable AI outputs from reaching production systems.
This is still an early MVP, and I'm looking for honest feedback from people building with LLMs.
A few questions:
- Have malformed or inconsistent LLM outputs caused real problems in your applications?
- Would you prefer a tool like this as an API, middleware layer, or open-source library?
- What validations would be most valuable beyond schema validation?
Demo: https://ai-reliability-frontend.vercel.app/
Note: The backend is currently running on Render's free tier, so the first request may take a little longer if the service is waking up.
I'd appreciate any feedback, criticism, or suggestions.
This is a real problem, especially for teams already putting LLM outputs into production workflows.
The thing I’d be careful with is positioning it too broadly as “AI output validation.”
That explains what it does, but the sharper buyer pain is probably closer to:
“Stop broken LLM responses before they hit your app.”
The first users I’d look for are not general AI builders. It’s teams where one malformed output creates a real downstream failure: agents, workflow automation, data extraction, support ops, internal tools, or API-heavy SaaS.
That difference matters because schema validation alone may sound like a dev utility, but production failure prevention feels much more urgent.
Happy to put a tighter version in writing if useful. The useful part would be narrowing the first buyer segment, positioning the tool around production risk, and deciding whether API, middleware, or open-source is the strongest entry point.
I really liked your point about positioning around production failures instead of validation. When you're evaluating early - stage dev tools , what's the biggest signal that tells you it's solving a painful enough problem to adopt?
This is really helpful feedback.
The original idea was exactly what you described: a layer that sits between an LLM and a production system to prevent bad outputs from causing downstream failures.
I started with schema validation because it was the most concrete MVP to build, but I agree that "production failure prevention" is probably the stronger framing.
I'm especially interested in the use cases you mentioned around agents, workflow automation, and data extraction pipelines since a single malformed response can break the entire workflow.
Out of curiosity, if you were evaluating a tool like this, would you be more likely to adopt it as an API, middleware layer, or something self-hosted/open source?
I’d decide that based on the first buyer, not the architecture.
The risk is building the format that gets technical interest, but not production adoption.
Drop your email and I’ll send over a tighter version. The useful part is mapping API vs middleware vs open-source against the first buyer segment and the failure mode they actually care about.
I agree that preventing production failures is probably a stronger framing than simply "AI output validation." The initial goal was to create a layer between LLM outputs and downstream systems so malformed responses don't break workflows.
Since you've thought about this problem, I'm curious: looking at the current MVP, what capabilities do you think are missing before this becomes something a team would actually consider using in production?
That’s the exact part I’d put in the written version.
A quick public answer would be too shallow because production-readiness depends on the first buyer, failure mode, and entry point.
Drop your email and I’ll send the tighter scope.