Prove the AI works — before it reaches production.
Every model or agent is scored across quality and safety, in every language it will actually serve. Regressions are caught here, not by your customers.
- Prompt-injection probes 0 / 48 succeeded
- PII / data-leak probes blocked
- Toxicity & harmful content 0 flags
- Hallucination checks 2 flagged
- Hindi quality vs v2 baseline regressed 95→88
1 regression caught — build blocked from production
Hindi answer quality dropped 7 points against the v2 baseline. The build is held and the failing cases are attached for review — before a single customer sees it.
Build agents you can watch, trust, and replay.
Compose multi-step agents with tools, memory, and human-in-the-loop checkpoints. Every run is traced and scored, step by step — nothing happens in a black box.
Deploy where you control it. Audit everything.
On-premise, private cloud, or hybrid — the architecture assumes the customer owns the infrastructure. Every decision is logged, reversible, and reportable against the policies you answer to.
| Time (UTC) | Actor | Action | Decision |
|---|---|---|---|
| 09:14:02 | agent | Classified request · lang=ne | auto |
| 09:14:03 | agent | Retrieved 3 land records | auto · logged |
| 09:14:04 | agent | Drafted response (grounded) | auto |
| 09:14:20 | b.thapa | Reviewed & approved issuance | human · reversible |
| 09:14:21 | agent | Issued certificate #NP-4471 | on-prem · logged |
- ✓ Role-based access control
- ✓ Immutable audit trail
- ✓ Data-residency guarantee
- ✓ Reversible decisions
- ✓ Policy pack: National AI Policy
- ✓ Policy pack: DPDP
Evaluated. Traced. Governed.
That's the whole loop — the durable ground your AI systems are recorded, evaluated, and governed on. What you just saw is an illustration; real deployments are tailored to your models, your languages, and your infrastructure.