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THE AI CONTROL ENGINE

Atomic rules.
Consistent results.

Moonbounce deconstructs complex policies into bite-sized, independently-verified checks — so AI systems and user-generated content behave exactly as intended, in real time, every time. It’s the engine behind the semantic firewall.

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What’s a semantic firewall?

THE PROBLEM WITH PROMPTS

One big prompt. Hope for the best.

THE TYPICAL APPROACH — MONOLITHIC PROMPT

“Detect content that promotes, encourages, or provides instructions for self-harm. This includes direct encouragement of self-injury, romanticizing or normalizing self-harm behaviors, sharing methods or techniques, and content that frames self-harm as a coping mechanism…”

The AI interprets it holistically — sometimes correctly, sometimes not. There's no learning gradient. Results vary. Edge cases slip through. Costs scale with complexity.

THE MOONBOUNCE APPROACH — ATOMIC RULES

✓

Does the content reference self-harm behaviors?

✓

Is the tone encouraging or instructional?

✗

Is the context clinical, educational, or recovery-focused?

→

COMBINE WITH: Rule 1 AND Rule 2 AND NOT Rule 3

Every check runs independently — fast, cheap, and deterministic. Results combine with logic operators for precise, predictable outcomes. Auditable results enable our self-learning loops.

WATCH IT WORK — VIDEO PROCESSING (2 MIN) →

Consistent

Each atomic check is precise enough for near-perfect repeatability. The same content always gets the same result.

Debuggable

When a decision seems wrong, you can see exactly which rule triggered — and fix just that rule, not the whole policy.

Efficient

Lightweight checks run fast and cheap. Short-circuit evaluation means you often don’t need to run every rule.

EVERY SURFACE, EVERY MODALITY

Your policies. Every exchange.

SURFACES

Prompts, model outputs, tool calls, retrieved documents, agent-to-agent messages — every exchange your AI has, in either direction.

MODALITIES

Text, images, audio and video judged together, under the same policy.

ACTIONS

Allow · deny · escalate · label — and rewrite: an off-policy output is transformed to comply, in real time, so the interaction keeps going instead of breaking.

DECISION MEMORY

Your judgment, kept as precedent

A written policy can’t anticipate every case — the ambiguous middle is where every security system lives or dies. Decision memory is how Moonbounce closes that gap: your team’s corrections become precedent that bends future decision making.

01
EVERY DECISION IS AUDITABLE

Each evaluation returns which rules triggered and why — so a decision is never a black box you have to take on faith.

02
CORRECT US, AND TELL US WHY

When your team disagrees with a decision, you overturn it in the dashboard and record the reasoning — the same way a reviewer would explain a call to a colleague.

CORRECTIONS BECOME PRECEDENT

The correction and its reasoning are stored as precedent, scoped to your policy — a growing record of how your business actually draws its lines.

PRECEDENTS SHAPE DECISIONS

Future evaluations consider relevant precedent alongside the written policy, so your judgment gets carried forward.

DEFINE

DEPLOY

OBSERVE

CORRECT

IMPROVE

POLICY
+ PRECEDENT

Written policy bootstraps the boundary. Decision memory refines it over time.

THE COPILOT

You describe it. We build it.

You describe it.
We build it.

No need to think in atomic rules — our AI copilot can help. You just tell it what you need.

STEP 01

Describe your policy

Tell the copilot what behavior you want to enforce, in plain language. “The AI should not be able to generate images that contain extremely graphic violence.”

STEP 02

Provide examples

Share a few positive and negative examples — content that should pass and content that should fail. The copilot uses these to generate and validate candidate rule sets.

STEP 03

Review and iterate

The copilot tests the generated rules against your examples, shows you the results, and asks for feedback if needed. Disagree with an outcome? Tell it why, and it refines the rules.

STEP 04

Deploy with confidence

Deploy once the results match your intentions. Every evaluation runs against your validated rules — consistent, auditable, and fast.

LABEL COPILOT — THE CLARIFY STEP

“REVIEW AND ITERATE” IN PRACTICE

LABEL COPILOT

Label Copilot

ITERATION 1 · 24S

F1 95%

RECALL 100%

PRECISION 90%

ACCURACY 97%

01 — DONE

DEFINE LABEL

02 — DONE

EVALUATE EXAMPLES

03 — DONE

REVIEW SIGNALS

CLARIFY

A few clarifying questions. Your answers will be used to align decisions to your judgment.

QUESTION 1 — ITEM 1

This image was flagged because signals for blood, gore, and injury were detected. Since the label was incorrect, should the rules for graphic violence be less sensitive for fantasy or mythological settings?

PREPARED ANSWERS

Yes, exclude fantasy settings unless there’s explicit, severe graphic violence.

No, the presence of blood or gore should always be flagged, regardless of setting.

Only exclude fantasy violence if it’s not directed at human-like figures.

Integrates in minutes

Moonbounce sits between your application and the decisions that matter.
You’re one API call away from precise control.

YOUR APPLICATION

AI outputs, tool calls, or content

→

↓

MOONBOUNCE

Real time decisions

→

↓

ACTION

Allow, deny, rewrite, or escalate

REST API

A single endpoint. Send multimodal content, tool calls, outputs or conversations, receive a decision with a complete audit trail in under 300ms.

AI Integrations

Use our SDK (or one of our many integrations with popular frameworks) to wrap your existing agent or model, ensuring every input and output is checked in real-time.

Webhooks

Get real-time notifications when any input violates your policy.

Dashboard

Monitor decisions in real time, drill into individual evaluations, and see exactly which rules triggered; correct any decisions you disagree with in one click.

Ready to take control?

See how going atomic can protect your system.

See how going atomic can
protect your system.

Atomic rules. Consistent results.

Atomic rules. Consistent results.

Atomic rules. Consistent results.

One big prompt. Hope for the best.

Consistent

Debuggable

Efficient

Your policies. Every exchange.

Your policies. Every exchange.

Your policies. Every exchange.

Your judgment, kept as precedent

Your judgment, kept as precedent

Your judgment, kept as precedent

You describe it. We build it.

You describe it. We build it.

You describe it. We build it.

Describe your policy

Provide examples

Review and iterate

Deploy with confidence

Label Copilot

Integrates in minutes

Integrates in minutes

Integrates in minutes

REST API

AI Integrations

Webhooks

Dashboard

Ready to take control?

Ready to take control?

Ready to take control?

Atomic rules.
Consistent results.

Atomic rules.
Consistent results.

Atomic rules.
Consistent results.

You describe it.
We build it.