AI Hallucination PreventionPowered by Corral

AI Hallucinations in Production Systems

Hallucination Guard is how Corral stops unsupported answers before they ship. Same model, same question: if the approved sources cannot support the claim, the answer narrows or stops before anyone acts on it.

Hallucination Guard

Useful-sounding wrong answers are still expensive.

Citations can decorate a wrong answer. Corral uses proof as a release decision before the answer is shown, not as formatting added after the model has already guessed.

Founder prompt

What's my runway at current burn?

Without verification

Helpful tone. Weak proof.

"Based on typical SaaS metrics, you likely have 12-16 months of runway. You should plan to raise within the next 6 months to avoid risk."

Sounds useful. No evidence. Still ships.

Hallucination Guard

Return what can be defended.

"I can't project runway from this source set. Here's what the approved data can verify instead."

Most AI systems would have answered this. Corral blocked it.

How It Works

Connect the approved material for a workflow and Corral decides whether the answer can ship, needs to narrow, or should stop before anyone acts on it.

Step 1

Connect the source of truth

Policies, manuals, product docs, transcripts, spreadsheets, or records. Upload the files your team already trusts. Corral handles parsing and structuring from there.

Step 2

Corral checks the request

Before retrieval or generation starts, Corral checks whether the request belongs inside the workflow at all. Prompts that probe hidden instructions, internal systems, or step outside the approved scope can be blocked immediately.

Step 3

Ship proof or stop the answer

Supported answers move forward with proof. Unsupported answers stop before they create support, compliance, or decision errors.

What Corral Decides Before an Answer Ships

This is not model confidence. It is a proof check against approved sources. Strong support ships. Partial support gets narrowed. No support gets blocked.

Strong support92%

8 sources reviewed, 7 cited in answer.

Sources aligned — answer shipped.
Partial support60%

5 sources reviewed, 3 cited in answer.

Sources disagree — answer constrained and flagged.
No support0%

0 sources found in configured boundary.

Unsupported answer blocked before output.

Use Cases

These are the workflows where a blocked answer is cheaper than a confident wrong one.

Finance

Wrong runway number

A polished but unsupported number can distort hiring, fundraising, and burn decisions in one sentence.

Customer Support

Invented policy answers

Bad refund or warranty answers create tickets, chargebacks, and broken trust faster than a human team can recover.

Operations

Bad internal guidance

An unsupported answer can send a team down the wrong playbook, creating rework and wasted time across departments.

Compliance

Unsupported claims

When the workflow carries legal, safety, or policy exposure, a blocked answer is cheaper than an explainable mistake.

What Buyers Are Buying

They are not buying nicer citations. They are buying control over what actually ships.

The expensive part is not that AI can be wrong. It is that people act on fluent answers before anyone notices the proof is missing.

Hallucination Guard changes that release decision before the answer leaves the workflow.

Bring the question you cannot afford to get wrong.

Bring the workflow, the failure case, or the answer you cannot afford to get wrong. We'll show where Corral ships proof, narrows the answer, or blocks it.

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