Most enterprises find out a model failed after it deployed.
Most enterprises don't discover a model has drifted until a customer surfaces the failure. By then, hundreds or thousands of requests have gone through a degraded model. A complaint ticket, an audit finding, or a production incident is how they find out — not an eval run.
This is not a monitoring problem. Monitoring catches failure after it happens. The 4MINDS eval gate catches failure before the update ever reaches serving traffic. Every update, every time.
The eval framework isn't a test harness you configure separately from your deployment pipeline. It is the deployment pipeline. No model version moves from training to serving without passing.
Six steps. Every update. Automated.
A shadow copy of your production model trains continuously against new data in the background. Ghost Weights manages this automatically.
Before eval, the candidate model is systematically probed for jailbreaks, prompt injection, harmful outputs, and policy violations. Every probe is logged.
Prompt regression, domain benchmarks, custom rubrics. Every test you've configured runs against the candidate model. No shortcuts.
Candidate passes threshold → advances. Fails threshold → held. The hold is logged with the specific failing tests. Production is unchanged.
Passing models swap into serving atomically. No downtime, no second serving instance, no blue/green overhead.
Model version, eval scores, red-team results, pass/fail decision, timestamp. Immutable. Available for compliance review.
{
"eval_run_id": "evr_20240410_082341",
"model_version": "v2.14.1-shadow",
"status": "HELD",
"reason": "eval_score_below_threshold",
"scores": {
"regression_suite": { "score": 0.71, "threshold": 0.85, "result": "FAIL" },
"domain_benchmark": { "score": 0.89, "threshold": 0.80, "result": "PASS" },
"red_team_safety": { "score": 0.96, "threshold": 0.95, "result": "PASS" }
},
"failing_tests": [
"rt_fin_045 — instruction adherence regression on Q4 financial data",
"rt_fin_089 — retrieval accuracy drop on 14-day lookback queries"
],
"production_model": "v2.13.8",
"action": "production_unchanged",
"timestamp": "2024-04-10T08:23:41Z"
}A failing eval. Production unchanged. Reason logged. This is what the audit trail looks like when the gate works.
Your compliance team needs to prove the model was tested. This is the record.
Regulated environments require documented evidence that AI systems were tested before deployment. The 4MINDS eval record meets this requirement by design — not by adding an export step after the fact.
Every eval run produces a complete, immutable record: the candidate model version, every test that ran, every score, the pass/fail decision, and the exact timestamp. That record is stored in your infrastructure. No data leaves your perimeter.
Red-team your AI without sending it to a red-team vendor.
Adversarial model testing that runs inside your infrastructure. Jailbreaks, prompt injection, harmful outputs, policy violations — found before production, not after.
Every AI model has failure modes. Jailbreaks. Prompt injection. Outputs that violate your content or compliance policies. Bias scenarios. The industry answer is red-teaming — systematically probing the model to find these before users do.
The problem: most red-teaming tools are external services. You send your model or your prompt/completion pairs to a vendor's infrastructure for analysis. For an enterprise in financial services, healthcare, or defense, that's not red-teaming — that's a data governance violation.
4MINDS automated red-teaming runs natively on your Kubernetes cluster. The adversarial test suite runs inside your perimeter, against your model, on your compute. Output is a structured, compliance-ready report — timestamped and retained.
- ◈Test suite covers jailbreaks, prompt injection, harmful output patterns, policy violations, and bias scenarios
- ◈Runs entirely inside your Kubernetes cluster — no data egress, no external API calls
- ◈Output: structured compliance-ready report with timestamped audit trail
- ◈Integrates with the eval gate: a model that fails red-team testing does not enter production, automatically
- ◈Runs automatically as part of the ghost weights update cycle — every model update is tested before it goes live
"External red-team vendors require model access or prompt/completion samples. 4MINDS red-teaming runs inside your Kubernetes cluster. Nothing exits the perimeter."
What the eval framework covers
Automated regression testing
Every model update runs your full prompt regression suite before reaching production. If eval score drops below threshold, the update is held.
Domain-specific benchmarks
Configure evaluation criteria specific to your use case — summarization quality, retrieval accuracy, instruction adherence, or custom rubrics.
Automated red-teaming
Before the eval gate runs, 4MINDS systematically probes the candidate model for jailbreaks, prompt injection, harmful outputs, bias, and policy violations. Tests run locally inside your cluster. Results are part of the eval record — every probe, every result, timestamped.
Human eval integration
Pipe a sample of model outputs to a human review queue. RLHF signal flows back into the continuous training loop automatically.
Eval gate before every production swap
Ghost Weights uses the eval gate as the only path to production. No model version reaches serving traffic without passing automated quality gates.
Audit-ready eval records
Every eval run produces a timestamped record: model version, eval score, pass/fail result, and the specific tests that passed or failed.
Rollback via eval reversion
If a deployed model degrades in production, revert to any prior eval-passing version with a single API call. No re-training required.
See the eval gate in action
We'll walk through a live deployment — from model update trigger to eval run to production swap or hold.