Raidu vs TrustModel AI
TrustModel evaluates AI before deployment. Raidu proves governance after deployment. A pre flight inspection rates the aircraft. The flight recorder writes every flight. Both matter. They answer different questions.
What it is
A pre deployment AI evaluation platform that scores or rates AI models against trust criteria (robustness, bias, security, transparency) before they go live. The output is a deployment readiness score or assessment that helps governance teams decide whether to approve a model for production.
What it is
The AI Accountability Layer. Raidu intercepts AI interactions in production, enforces policy at runtime, and writes a cryptographically signed record per interaction. The output is per call evidence that the approved model behaved within policy on every actual production interaction, not in a pre flight test.
How pre deployment evaluation differs from runtime accountability
Pre deployment evaluation answers the question “is this model safe to ship?” The output is a structured assessment, often a score or a pass / fail decision, produced before the model sees real production traffic.
Runtime accountability answers the question “did the approved model, governed by approved policy, behave correctly on this specific interaction?” The output is a signed record per interaction, produced continuously while the model is live.
The two cover different stages of the AI lifecycle. They are not in competition. An enterprise that has only pre deployment evaluation cannot answer the runtime question. An enterprise that has only runtime accountability cannot prove the model was responsibly approved.
Side by side
| Dimension | TrustModel AI | Raidu |
|---|---|---|
| Category | Pre deployment AI evaluation | AI Accountability Layer (runtime) |
| Stage | Pre production | Production |
| Mode | Structured assessment | Continuous signed record |
| Primary unit | Evaluation suite over a model | Single AI interaction |
| Approval gate | Yes | No (assumes approval already happened) |
| Per interaction record | Out of scope | Yes, RSA-4096 signed, SHA-256 chained |
| PII redaction at runtime | Out of scope | Yes, 99.2% accuracy, 60+ entities |
| Prompt injection blocking | Out of scope | Yes, runtime |
| EU AI Act Article 9 / 10 | Direct fit | Operational evidence |
| EU AI Act Article 12 logging | Out of scope | Direct |
| HIPAA AI rule audit trail | Partial | Direct |
When to pick which
Pick TrustModel alone if the obligation is to evaluate models before approval and produce structured deployment readiness reports. The buyer is the AI governance lead approving model selections.
Pick Raidu alone if the obligation is to govern and prove behavior in production. The buyer is the CISO, CTO, or compliance owner whose mandate begins after a model is approved.
Pick both if you operate AI in a regulated industry, where pre deployment evaluation is required for approval and runtime accountability is required for ongoing evidence. Most enterprises with EU AI Act high risk systems will need both.
The structural difference
Pre deployment evaluation is a verdict on the model. Runtime accountability is a verdict on every interaction the model produces. The first is finite work that ends when the model is approved. The second is infinite work that continues for the life of the deployment. Confusing them produces governance programs that approve well and then fail to prove anything in production.
Where to read more
- What is an AI Accountability Layer?
- What is governance explainability?
- Raidu Trust Center for the regulator facing evidence the runtime produces.
Buyers ask, before they pick a side.
Is pre deployment evaluation a substitute for runtime accountability? +
Is runtime accountability a substitute for pre deployment evaluation? +
When does each add the most value? +
How do they fit together in a regulated enterprise? +
Which one helps with the EU AI Act? +
Which one helps with HIPAA AI? +
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Read →Decide on the proof, not the pitch.
Bring a use case. We will show you the runtime, the signed record, and what a regulator readable trail looks like for your AI stack. Thirty minutes.