The challenge
What makes this hard
Regulatory Mandate: DPDP, RBI IT Framework, IRDAI, SEBI, UAE PDPL, ADGM, EU AI Act impose data localization requirements
National Security: Defence/intelligence cannot use shared-cloud inference; prompts and weights must remain air-gapped
Audit Traceability: Regulators require complete logs of decisions, prompts, outputs; public APIs provide none
Latency & Reliability: Mission-critical AI cannot tolerate external API latency
Cost at Scale: 10M+ tokens/day makes cloud APIs expensive; on-premise reduces inference costs 60–85%
IP Ownership: Fine-tuned models on proprietary data stay proprietary, not owned by hyperscalers
What we deliver
The Qapitol approach
01
Layer 1 — Model Hosting & Inference
vLLM/TGI serving on GPU clusters (NVIDIA A100/H100 or AMD MI300X). Supports Llama 3.1-70B/405B, Mistral Large, Phi-4, Qwen2.5, Falcon — fully air-gapped.
02
Layer 2 — Evaluation & Safety Gate (QAVE)
Every prompt/completion passes through SURE-Q evaluation. Real-time scoring for bias, toxicity, hallucination, policy compliance.
03
Layer 3 — Compliance & Audit Layer (CHEQ)
Logs every AI decision with timestamps mapped to regulatory obligations. Auto-generates IRDAI, RBI, EU AI Act, DPDP audit reports.
04
Layer 4 — Orchestration (Nexus/Agent Fabric)
Multi-agent workflows, RAG pipelines, tool-use orchestration within your perimeter. Integrates with existing data warehouse/API layer.