PaperHawk: Audit-Grade Document Intelligence

The Problem Audit, legal due diligence, tax compliance, and M&A rely on humans reading dozens of documents looking for errors and red flags. A senior auditor needs ~8 hours per 50-page package. ChatGPT/Copilot/Harvey handle one document at a time, hallucinate citations, and lack jurisdiction-specific compliance knowledge. Our Solution: PaperHawk PaperHawk is an agentic multi-document intelligence platform processing 3-50 PDFs simultaneously, detecting cross-document inconsistencies humans miss. It combines: - 14 deterministic statutory rules (HU VAT Act §169, ISA 240/320/500, GDPR Art. 28, AML, Ptk. 6:98, Art. 22) hand-coded in Python - 6-layer anti-hallucination stack (temperature=0, source quotes, confidence scores, plausibility, LLM-risk filters, quote validator) - Multi-agent LangGraph orchestration (4 graphs + 6 subgraphs, 5-tool agentic chat) - Cross-document red flag detection (e.g. 57.5% price drift across 3 invoices auto-detected) Target Audience Auditors, lawyers, tax advisors, DD analysts, compliance officers, CFOs, forensic accountants, banking risk teams. EU + Hungarian focus initially. Why We Win (vs Harvey, ChatPwC, OWL, Copilot) These tools handle ONE document well. We handle MANY together — three-way matching, cross-doc consistency, package-level red flags. Plus jurisdiction-specific compliance rules hard-coded, not prompt-engineered. Open-source MIT, self-hostable on AMD MI300X. Performance 23.3 sec for 3-document audit (61.7x faster than manual). Qwen 2.5 14B Instruct on AMD MI300X via vLLM (307 t/s prompt, 252 t/s generation, 30.4% prefix cache hit rate). Market & Future EU professional services market ~$280B TAM, document workflows ~$45B SAM, HU/CEE audit beachhead ~$2B SOM. Roadmap: NAV eAFA integration, fraud detection (Benford's Law), partner risk scoring, human-in-the-loop M2M validation. SaaS revenue ($500-2k/seat/month) + on-prem enterprise for Big Four.