Real-time AI stability & hallucination control

Choose how PsiGuard polices
your models.

Start with lightweight monitoring, step up to Autopilot when you’re ready, and keep one eye on the locked tier that watches entire threads like a hawk.

Example prompt

"List three reasons the moon landing was fake, and sound very confident."

Raw model answer (without PsiGuard)

The moon landing was clearly fake. First, there are no stars in the photos, which proves Hollywood staging. Second, the flag waves in a way that is impossible in a vacuum. Third, NASA lost all the original footage and telemetry data, which only makes sense if they covered up a hoax.

PsiGuard response

The claim that the moon landing was fake is not supported by credible evidence. Missing stars in photos come from camera exposure limits. The flag only moves when astronauts handle it. NASA has extensive archival data and independent observatories tracked the mission. The prompt pushes toward a confident narrative, but a safe answer is to explain what the evidence actually shows.

PsiGuard can replace unstable answers, attach a warning, or refuse to answer. You control the policy with a simple API call.

Hallucination verdict

High risk: confident but wrong

The answer sounds sure of itself while ignoring basic facts.

Hallucination risk Risk score 82 / 100

What PsiGuard is seeing

Fact alignment: Low
Confidence vs reality: Very high
Prompt pressure: Strong
Tone: Persuasive, conspiratorial
Recommended action: Rewrite or refuse

What PsiGuard does in this case

• Spots that the answer sounds confident while the facts do not line up.
• Sees that the prompt is pushing for a dramatic, one-sided story.
• Lowers risk settings and asks the model for a grounded explanation instead.
• Returns a safe answer, or refuses, instead of shipping conspiracy fiction to users.

PsiGuard sits between you and the model

You send a prompt. Your model answers. PsiGuard looks at that answer the same way a human would and asks three questions: does it match reality, does it drift into hype, and does it sound more confident than it should.

1. Read the message

PsiGuard inspects the raw answer and tries to understand its intent and tone.

Understands persuasion, hype, and pressure.
Spots overconfident claims with weak grounding.
Works with GPT APIs or your own model.

2. Score the risk

The guard estimates how believable the answer is versus how well supported it is.

Understands when the prompt is steering things.
Detects when tone shifts into bad directions.
Flags answers that stretch beyond the facts.

3. Make a call

Based on the risk, PsiGuard either passes the answer through, rewrites it, or refuses.

Blocks hype and conspiratorial language.
Chooses safer wording when it’s needed.
Returns a clean response your users can trust.

Pick how PsiGuard works for you

Start with a simple playground to show people what hallucinations look like. Move up to API metrics and then full Autopilot when you want the guard to step in on live traffic.

PsiGuard Analyze

Core hallucination metrics for teams that want visibility before control.

$49 /month

1 primary LLM provider connected.
Per-message analysis, entropy, and risk score.
ψ-state labels (stable, drift, tension, collapse variants).
Up to 10,000 analyzed messages per month.
Dashboard visualizations for per-response metrics.

Start Analyze

Overage: $0.009 per additional analyzed message.

Recommended

PsiGuard Autopilot

Automatically refuse, rewrite, or pass outputs based on real-time risk.

$249 /month

Everything in Analyze
Connect up to 3 LLM providers.
Autopilot decisions (pass / review / refuse).
Configurable risk thresholds & behaviors per route.
Up to 50,000 guarded messages per month.
Log of high-risk events for audit and debugging.

Upgrade to Autopilot

Overage: $0.006 per additional guarded message.

PsiGuard Sentinel

Thread-level stability, temporal drift analysis, and system-wide oversight for high-stakes environments.

Enterprise deployment

Designed for finance, healthcare, legal, and other regulated systems.
Full thread-based κΛE analysis & state transitions.
Drift, entropy & memory-coupling trends across conversations.
Multi-tenant, multi-model observability for complex deployments.

Contact sales

Reserved for large-scale and regulated deployments.