Pablo Manuel Suarez@TheLinconX

THE PROBLEM. Italian banks face dual regulatory urgency: DORA (mandatory since Jan 17 2025 for 22,000+ EU financial entities — UniCredit, Intesa Sanpaolo as G-SIBs) + EU AI Act Article 14 (enforceable Aug 2 2026, fines up to €35M or 7% of global revenue). When Banca d'Italia asks "why did your AI decide X?", the bank needs tamper-evident, third-party-signed audit evidence. Existing tools (Galileo $73M, Lakera $30M, Patronus $20M, Credo AI) don't generate court-grade compliance evidence from production runtime. THE SOLUTION. CONSILIUM is a 3-tier open-source platform: (A) OSS Apache-2.0 entry — 9-vendor adversarial LLM ensemble + 78-rule deterministic judge layer + INV-15 Z3 SMT formal proof (UNSAT in 10.08ms). (B) Governance OS core — 4-stage SOAR pipeline + 6 compliance framework dashboards (EU AI Act, NIST AI RMF, ISO 42001, SOC 2, GDPR, NIST 800-53) + HMAC-SHA256 verdict chain + STIX 2.1 export. (D) CAICEP module — RFC 3161 TSA-timestamped verdict chain via freetsa.org (live evidence today) + roadmap to court-admissible attestation Q3 2026 via eIDAS QTSP partnership (Actalis Italia). LIVE EVIDENCE. apohara.dev/consilium/verify — interactive demo: paste any prompt → 9-vendor decision. Click any of 3 demo verdicts → verify RFC 3161 timestamp against freetsa.org independently. api.apohara.dev shows 10+ SOAR endpoints live, /v1/verdicts/{hash}/verify-timestamp returns valid:true with real Freetsa.org-signed token (1312 bytes, signed 2026-05-19T12:21:50Z). BUSINESS VALUE. TAM AI governance $3.59B by 2033 (36% CAGR). SAM EU regulated industries $400-800M by 2027. Initial wedge: Italian G-SIBs + Milan Fintech District (200+ companies) = $15-30M ACV in 12 months. Revenue: OSS free + Cloud Pro $299-999/mo + Business $2-5K/mo + Enterprise+CAICEP $25-200K/year. Exit reference: Cisco acquired Robust Intelligence Aug 2024 (~$350M, 451 Research). Built solo by Pablo M. Suarez (UNT, Argentina) in 8 days for Milan AI Week 2026.

Apohara PROBANT is a cross-AI code verification platform. Gemini writes a review; a 12-vendor adversarial ensemble (Claude, GPT, DeepSeek, Kimi, GLM, Qwen, Nemotron, MiniMax, Big-Pickle, Mistral Large, Grok 2, Perplexity Sonar) independently audits the output for prompt injection, vulnerabilities, and logic bugs. Verifiable, not claimed: - 12 vendors via OpenRouter, each in an isolated KV-cache enforced by INV-15 JCRSafetyGate. Paper v3.0 (formal Z3 SMT proof, UNSAT on negation in 10.08 ms) complements v2.0.1 empirical sweep (0/1210 violations). DOI 10.5281/zenodo.20277875. - JBB-Behaviors block rate 93.75% (Wilson 95% CI [86.2%, 97.3%], n=80 holdout). Numbers from logs/*.json, not marketing. - 120+ pytest tests + 15+ measurement JSON logs. - Multi-hardware: AMD MI300X (ROCm 7.2) + NVIDIA H100. Four hardening layers (auditable in repo): 1. Veea LobsterTrap DPI subprocess pre-check — measured: SQLi block 50% (n=20, CI [29.9%, 70.1%] directional), benign FPR 9.8% (n=51). Live demo SQLi returns verdict=blocked in ~25 ms. 2. Prompt envelope nonce-tagged sentinels (Hines et al. arXiv 2403.14720). 3. HMAC-SHA256 verdict ledger chain. verify_chain() catches tampering. 4. NO-HEDGING gate (HARD/SOFT split, judge uncertainty flagged). Distribution: Cursor / Claude Code plugin shipped as VSIX. MCP server (stdio) for Claude Desktop / Cursor / Zed. /v1/verify_stream SSE for live per-vendor results. /dashboard for ops view. Stack: FastAPI/Python 3.11+, React+Vite + Next.js SSR PoC, Apache-2.0, monorepo across 3 GitHub orgs. Live demo BYOK or 5 free/IP/day.

Multi-agent AI pipelines waste up to 70% of their context window re-encoding shared information from scratch. ContextForge eliminates this at the infrastructure layer — without changing models or prompts. ContextForge is a shared context compiler that sits between your orchestration layer and your vLLM/LMCache inference backend. It implements six peer-reviewed research papers (arXiv 2024–2026) as production-grade Python modules: • TokenDance Master-Mirror storage: 10.81x KV-cache compression across 12-agent committees (arXiv:2604.03143) • JCR Safety Gate: prevents critic-agent context corruption with INV-15 enforcement, 0 violations (arXiv:2601.08343) • KVCOMM cross-context protocol: 7.8x TTFT improvement via shared KV-cache communication (arXiv:2510.12872) • Speculative Coordinator: cross-agent draft-and-verify decoding, >70% acceptance rate • Visual KV Cache: 5x fewer vision encoder calls in multimodal pipelines • LSH + FAISS semantic deduplication: 79.85% token savings on live demo Results: 310 unit tests passing (0 failures), 15/15 benchmark scenarios PASS, live Gradio dashboard deployable via single Docker command on AMD MI300X ROCm hardware. Target customers: Any team running agentic AI at scale — LLM API costs drop immediately, no code changes required. Revenue model: usage-based SaaS per million tokens optimized, plus enterprise on-prem licensing for AMD MI300X clusters.

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