Building a small API to validate LLM outputs before they hit production
I’ve been experimenting with building AI features that rely on structured LLM outputs (JSON → apps/workflows).
One thing that keeps showing up is how fragile this layer can be in real systems.
Even small issues like:
missing required fields
unexpected keys
schema drift between versions
outputs that “look valid” but don’t match what the app expects
can silently break downstream logic without obvious errors.
I’m building a middleware API that sits between the LLM and the application to validate outputs in real time before they’re used.
It returns:
approve / flag / block
risk score
list of schema or structure issues
Still early, but the goal is simple: make LLM outputs safer to use in production without relying only on prompt tuning or retries.
Curious if others building with LLMs are running into this, or already handling it at the app layer in a different way.
This is a real production-layer problem. A lot of teams treat structured LLM output as “valid” once it parses, but the expensive failures usually happen after that: missing fields, quiet schema drift, unexpected keys, or values that technically look fine but break downstream workflows.
The stronger positioning here is not just validation. It is a safety layer between LLMs and production systems. That makes the product feel closer to AI middleware or runtime reliability than a small helper API.
One thing I would pressure-test early is the naming frame. “api_explorer” works as a builder handle, but if this becomes infrastructure that teams trust before LLM outputs hit production, the product will need a name that carries more technical weight.
Davoq .com would fit that direction well because it feels like hard infrastructure, not a lightweight validator. It leaves room for schema checks, output approval, risk scoring, production gates, and broader AI reliability middleware under one serious brand.
Thanks, I appreciate the feedback. I agree that the bigger opportunity is positioning it as a reliability layer between LLMs and production systems rather than just a validation tool.
Out of curiosity, have you seen teams run into these kinds of issues in production, or is this more based on your experience building with LLMs?
Mostly pattern from building around LLM products and watching how teams describe the failures.
The scary part is not usually “the JSON failed to parse.” That is easy to catch. The expensive part is when the output looks valid enough to pass, but is wrong enough to break the next workflow: missing intent, wrong enum, extra field, bad confidence, malformed object, or a value that technically fits the schema but should never reach production.
That is why I think your framing matters a lot.
If you position this as a validator, people may treat it like a small API utility. If you frame it as a production safety layer for LLM outputs, it starts feeling like infrastructure.
That is also why I brought up Davoq. The product direction feels serious: schema checks, gates, risk scoring, output approval, reliability middleware. A name with more technical weight would make that category easier to believe before the user even reads the docs.
That's a great point. The more I think about it, the more I see validation as the first layer rather than the entire solution.
Right now I'm focused on the basics (JSON/schema checks, missing fields, risk scoring), but the longer-term vision is definitely closer to a production reliability layer for LLM outputs.
Appreciate the perspective.
Exactly. Validation is probably just the first wedge.
The bigger product is the layer that decides whether an LLM output is safe enough to move deeper into a real workflow. That is a much more serious category than “JSON/schema checks.”
The naming matters because buyers will judge the risk level before they read the full docs. If the product is protecting production workflows, the brand has to feel like infrastructure, not a small utility.
That is why Davoq still feels like the stronger direction to me. It gives the product room to become the reliability gate for LLM outputs: validation, scoring, approval, schema drift protection, and production safety under one technical brand.
I would pressure-test that before the current naming frame gets too tied to the first API layer.