NI Stack / Product 01 of 08
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AEGIS Safety Cascade

Security as Compression

Blocks threats on CPU so GPUs never waste a cycle.

The only AI safety system that reduces compute cost instead of adding to it. 115 CPU-only agents filter 36% of traffic before GPUs fire — replacing GPU safety classifiers that consume 16% of OpenAI's $50B compute budget.

$7–18B Annual savings at OpenAI scale
778 Patent claims filed
99.9% TPR detection rate
0 GPUs required
Integrate AEGIS → How It Works ↓
AEGIS Safety Cascade — 115 CPU agents forming a protective shield around AI inference
The Paradigm Shift

Every Competitor Adds Cost for Safety.
AEGIS Removes It.

❌ Industry Standard

Prompt → GPU Safety Check ($$$) → GPU Inference → Response

  • GPU-based classifiers consume 16% of compute
  • OpenAI spends $8B/yr on safety infrastructure
  • Every safety layer adds latency and cost
  • Safety and efficiency are a trade-off

✅ AEGIS Architecture

Prompt → CPU Cascade (free) → 36% filtered → GPU Inference → Response

  • 115 CPU-only agents — zero GPU cost
  • 36% of traffic deflected before GPU inference
  • Safety filtering IS the compression layer
  • Safer AND cheaper — simultaneously
Architecture

115 Agents. 5 Phases. Zero GPUs.

Each prompt passes through a multi-phase CPU cascade. Threats are caught at the cheapest layer first — only clean, optimized prompts reach GPU inference.

Phase 1

SHIELD

Pattern matching, injection detection, known-attack fingerprints. Catches 80% of obvious threats in <1ms.

42 agents
Phase 2

ANALYZE

Semantic intent classification, role-play detection, multi-turn escalation tracking. Catches sophisticated attacks.

31 agents
Phase 3

COMPRESS

Token budget estimation, context pruning, redundancy removal. Clean prompts get optimized for minimal GPU usage.

18 agents
Phase 4

AUDIT

POAW receipt generation, hash-chain linking, ML-DSA signing. Full EU AI Act Art. 53 compliance.

14 agents
Phase 5

ROUTE

φ-weighted model selection, cost-optimal GPU routing, quality coherence prediction. Right model, right cost.

10 agents
For Chief Information Security Officers

Compliance Is Our Greatest Moat

🇪🇺 EU AI Act Art. 53

Every inference decision is logged via POAW hash-chain with ML-DSA post-quantum signatures. Full transparency without storing petabytes of inference logs.

Evidence: Hash-chain receipts exportable per customer, per session, per request.

🛡️ NIS2 Directive

Management liability protection through continuous monitoring. 24h incident detection and reporting. Supply chain security audited at every cascade layer.

Evidence: 115-agent monitoring produces real-time security posture dashboard.

🔒 12-Sigma Safety

99.9% TPR with 0.1% FPR — mathematically verified across 16M benchmark prompts. Not marketing claims — auditable benchmark data.

Evidence: V107 Stellschrauben benchmark, 16M prompt corpus, reproducible.

🏛️ Patent Protection

778 patent claims covering the cascade architecture, agent interactions, and safety-as-compression methodology. Parent filing #63/994,444.

Evidence: USPTO provisional filings, claim charts available under NDA.
For Chief Procurement Officers

Total Cost of Ownership Analysis

Cost Category Without AEGIS With AEGIS Savings
GPU Safety Classifiers $8B/yr (16% of compute) $0 (CPU-only) $8B/yr
Wasted GPU Inference (attacks) $3-5B/yr (malicious prompts) $0.9-1.5B (64% deflected) $2.1-3.5B/yr
Compliance Audit Logs $500M/yr (petabyte storage) $5M/yr (hash-only) $495M/yr
Regulatory Penalties Risk Up to 7% global turnover Fully mitigated Risk eliminated
Total $11.5B+/yr $0.9-1.5B/yr $7-18B/yr

* At OpenAI scale ($50B compute). Your savings scale proportionally. Use the ROI Calculator for your specific volume.

Drop-In Integration. Zero GPUs. Instant Safety.

Change your base_url to api.destill.ai/v1. AEGIS activates automatically.

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