ERR-01
Hallucinated outputs
Your model invents facts, cites sources that do not exist, or confidently returns answers that are simply wrong.
AI SOS · on call now
When your AI feature breaks, we’re the emergency response.
PlayNot.ai rescues, stabilizes, and de-risks AI-powered software before it becomes expensive, embarrassing, or dangerous. We diagnose what went wrong, fix it fast, and install the evals and guardrails so it never happens again.
Confidential by default. We work under NDA and never name clients.
- Focus
- Rescue · Stabilize · De-risk
- Works with
- Eng · Product · Leadership
live diagnosticsPLAYNOT://monitor
Status
Recovered
Hallucination
↓ 94%
Evals
Passing
Fault signals
If any of these are blinking red, you are not overreacting.
These are the failures that break user trust and create real liability. The earlier we catch them, the cheaper they are to fix.
ERR-02
Eroding user trust
Support tickets climb, power users churn, and the feature that was supposed to wow now quietly scares people.
ERR-03
Compliance exposure
Sensitive data leaks into prompts, outputs cross legal lines, and nobody can explain how a decision was made.
ERR-04
Unpredictable in prod
It worked in the demo. In production it degrades, drifts, and fails in ways your tests never caught.
The engagement
Three ways we get your AI back under control
Whether you are mid-incident or pre-launch, the work follows the same arc: understand it, fix it, and make it defensible.
01 — Triage01
Rescue
Something is on fire in production. We drop in as your AI SOS response team: investigate the failure, contain the blast radius, and ship a fix that holds.
- Root-cause diagnosis of bad outputs
- Immediate containment & rollback strategy
- Hot-fix shipped with a safety net
02 — Reinforce02
Stabilize
Fixing the symptom is not enough. We install the evaluation systems, guardrails, and workflows that turn a fragile feature into a dependable one.
- Eval harness & regression suite
- Guardrails, retries & fallback flows
- Observability and drift monitoring
03 — Pre-flight03
De-risk
Best case, we never meet in a crisis. Before you launch, we pressure-test the idea, choose the right model, and design flows that protect users and data.
- Model selection & architecture review
- Safer UX & human-in-the-loop design
- Data protection & hallucination budget
Response protocol
What happens when you pull the alarm
Every engagement runs the same disciplined loop — so you always know what comes next and when.
- 01
Signal
A 30-minute call to understand the failure, the stakes, and who is affected. You leave with an honest read on severity.
- 02
Diagnose
We instrument the system, reproduce the failure, and trace it to a cause — model, prompt, data, or architecture.
- 03
Intervene
A fix ships fast, paired with evals so we can prove it works and catch regressions before your users do.
- 04
Harden
Guardrails, monitoring, and a runbook hand the system back to your team — defensible and built to last.
What good looks like
From on-fire to defensible
The point of an engagement is not a one-off patch. It is an AI feature your team can stand behind — and your users can trust.
Typical time to contain
From first call to a shipped fix on the most severe production incidents.
Eval coverage installed
Critical paths put under automated evaluation before we hand the system back.
Repeat incidents
The goal of every engagement: the same failure never reaches your users twice.
* Figures are illustrative of typical engagements. Yours will get a concrete, honest assessment on the first call.
Briefing
Questions teams ask before they call
If yours is not here, email us — we answer fast.
What exactly is an "AI SOS" engagement?
When an AI feature starts producing bad outputs, breaking user trust, or behaving unpredictably in production, we step in as an emergency response team — part technical investigators, part product strategists. We diagnose the root cause, ship an immediate fix, and install the guardrails and evals so it does not happen again.
Is this confidential? Will anyone know we brought you in?
Discretion is the default. We work under NDA, and we never publish client names, logos, or engagement details — the outcome figures on this site are deliberately anonymized and aggregated. AI failures are sensitive and often embarrassing; protecting your reputation is part of the job. The fact that you reached out is itself confidential.
Do you only help after something breaks?
No — the cheapest crisis is the one you avoid. If you are adding AI to your product, we pressure-test the idea before launch: choosing the right model, designing safer user flows, building evals, reducing hallucinations, and protecting sensitive data.
Who do you work with inside a company?
Engineering, product, and leadership together. Fixing AI reliability is rarely just a code change — it usually means aligning on acceptable risk, user experience, and the evaluation bar the feature must clear.
What does an engagement cost?
It depends on severity and scope. Rescue work is priced for speed; de-risk and stabilization work is scoped as a fixed engagement. Book an SOS call and you will leave with a clear recommendation, whether or not we work together.
Which models and stacks do you work with?
Frontier and open models alike — including Claude, GPT, Gemini, and self-hosted Llama-class models — across the common orchestration, retrieval, and eval frameworks. The right model is an outcome of the engagement, not an assumption going in.
lines are open
Don’t wait for the next bad output to make the decision for you.
Book a 30-minute SOS call. You’ll leave with an honest read on severity and a clear next step — whether or not we end up working together.