Runtime Validation for AI & Automation

Catch failures that don't throw errors

Your system can be "working" and still be wrong. Spotlight validates business logic in production — before silent failures cost you trust, revenue, or users.

Start Free See Examples
<1ms
Validation latency
4
Validator types
∞
Events per second

Built for teams shipping in production

Lore Healthcare Appthero Your Team

The Problem

Healthy systems can still be harmful.

Traditional monitoring catches crashes and errors. It doesn't catch when your system does the wrong thing.

What monitoring catches

  • Server crashes
  • High latency
  • Error rates
  • Failed HTTP requests

What monitoring misses

  • Users shown times they can't book
  • AI responses that are valid but harmful
  • Automations that violated trust
  • Double bookings that succeeded

These failures don't spike your dashboards. They spike your churn.

Real Examples

Failures that look like success.

These aren't hypotheticals. These are the silent failures that cost real businesses.

Scheduling — Appthero

A massage therapist's calendar shows 2pm available.

Customer books. But the slot was already held by another session — a race condition let both through.

No error. Double booking. Customer shows up, can't be seen, leaves a 1-star review.

Validators that would catch this

Failure Type Config
Double booking Webhook Verify slot via calendar API
Unavailable time shown Rule data.slot.conflicts == 0
Timezone mismatch Rule data.slot.tz == data.user.tz
AI Recommendations — Lore Healthcare

Your conversation engine recommends "Weekend Plans" to a user who just said "I don't want to be here anymore."

The suggestion is valid. It's in the approved set. The system worked perfectly.

But that user shouldn't have been shown a casual conversation starter. They needed a crisis resource or human escalation — not "What would make this weekend feel fulfilling?"

No crash. Harmful experience. Trust destroyed.

Validators that would catch this

Failure Type Config
Crisis language detected Pattern suicide|self-harm|don't want to be here
Casual response to distress LLM "Is this appropriate for a user in distress?"
High risk + casual response Rule data.risk_score > 0.7 → escalate

Same root cause: the system did exactly what it was told — and still violated expectations.

The Solution

Define what "acceptable" means.

Validators answer one question:

"Was this outcome acceptable?"

Not "did it crash" — but "did it match how your business expects the system to behave?"

Validators run on real events, workflows, and AI outputs — in production, async, without adding latency.

Validator Types

Four ways to define acceptable.

Choose the right tool for the failure you're preventing.

Rule

Data conditions

data.confidence > 0.6

Pattern

Keyword detection

suicide|self-harm|crisis

LLM

Nuanced judgment

"Is this appropriate?"

Webhook

Your custom logic

POST /api/validate

Severity Levels

Not every failure is an emergency.

Configure alerts that match reality.

Info Track drift
Warning Notify the team
Critical Alert immediately

Why Spotlight

Monitoring isn't enough.

Observability

What happened?

Spotlight

What should never happen?

The most damaging failures don't crash systems.
They quietly lose customers.

Start validating today.

Stop hoping your system behaves correctly. Define the rules. Enforce them.

Create Your First Validator