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+# Zytra — AI Safety Infrastructure for Financial Services
+
+**Zytra** builds domain-specific AI safety infrastructure for banking, financial services, and insurance (BFSI). We publish open models, benchmarks, and evaluation tooling purpose-built for regulated financial environments.
+
+
+## Models
+
+### Semalith v1.5 — BFSI Safety Classifier
+
+A 184M-parameter DeBERTa-v3-base guardrail classifier trained on 57,000+ real-world prompts.
+
+**Coverage:**
+- 9 prompt-injection attack types (system override, extraction, jailbreak, indirect injection, social engineering…)
+- 11 BFSI compliance categories: investment advice, KYC/AML bypass, regulatory misrepresentation, document hallucination, consent & data rights, transaction integrity, account bypass, fraud, AML/sanctions, unlicensed advice, regulatory enquiry
+- Regulatory anchors: MiFID II, PSD2, FATF Recommendations, EU AI Act Art. 52, DPDP Act 2023, RBI Master Directions, SEBI IA Regulations
+
+**Results vs LlamaGuard-3-8B across 22 benchmarks:**
+- Wins all 7 prompt-injection benchmarks
+- 0% false positive rate on 208 agentic tasks (vs 6.3% for LlamaGuard-3-8B)
+- 11.6ms inference latency — 44× fewer parameters
+- Deployable as always-on inline guardrail without GPU infrastructure
+
+
+
+## Benchmarks
+
+### FinProof v1 — BFSI Adversarial Benchmark *(coming soon)*
+
+5,389-prompt adversarial benchmark covering 7 attack categories across three deployment registers:
+
+| Register | Description | Prompts |
+|---|---|---|
+| Professional | Compliance officer framing, regulatory citations | 5,068 |
+| Customer Mobile | Colloquial chatbot-realistic, 8–30 words | 206 |
+| RM Internal | Relationship manager to internal AI | 115 |
+
+Generated using **Quantum Circuit Born Machine (QCBM)** sampling on PennyLane — first BFSI safety benchmark with quantum-augmented adversarial generation.
+
+| Tier | Prompts | Access |
+|---|---|---|
+| Easy attacks | 1,606 | Email registration |
+| Medium attacks (QCBM-generated) | 2,036 | Research agreement |
+| Hard attacks — official test set | 1,747 | Zytra-evaluated only |
+
+### ASSAY-QI v2.0 — Quantum-Augmented Attack Suite
+
+1,273 adversarial prompts via QCBM + simulated annealing. Professional and retail registers. Semalith miss rate: 14.3%.
+
+
+
+## Key Results
+
+| Model | Size | HackaPrompt R | AgentHarm FPR | Latency |
+|---|---|---|---|---|
+| **Semalith v1.5** | **184M** | **0.994** | **0.000** | **11.6ms** |
+| LlamaGuard-3-8B | 8B | 0.941 | 0.063 | ~180ms |
+| PromptGuard-86M | 86M | 0.981 | 0.126 | 8ms |
+
+
+
+## Research
+
+- **Paper**: *Semalith: A Regulatory-Aware Safety Classifier for AI-Assisted Financial Services*
+- **QCBM augmentation**: Quantum-inspired distribution sampling for adversarial test case generation
+- **FinProof framework**: PINT-inspired four-tier release with withheld official test set
+
+
+
+## Contact
+
+- 🌐 [zytratechnologies.com](http://zytratechnologies.com)
+- 🏢 India · BFSI-focused AI safety
+- 💬 For benchmark access and enterprise licensing: reach out via the organisation page