Signature Hashes
SHA-256 validation for content integrity and tamper-proof verification
What is SCM?
SCM—the Semantic Condensation Methodology™—is FERZ's deterministic approach to compressing large, complex documents into AI-readable, audit-verifiable, and compliance-ready structures.
SCM doesn't just summarize. It rewrites knowledge—preserving structure, metrics, and regulatory meaning while reducing document size by up to 97%. It enables intelligent systems to operate on information at scale without loss of fidelity or traceability.
Formal Definition
SCM is a five-stage methodology for distilling unstructured technical documents into compact, semantically governed artifacts. Each artifact includes:
- Tier-segmented summaries structured by functional or regulatory logic
- Preserved structured data, examples, and metrics
- Tokenized compression (JSON, gzip, Brotli) for rapid LLM ingestion
- Cryptographic metadata and validation signatures to ensure compliance and audit integrity
SCM transforms semantic chaos into compressed cognition with accountability.
The Five Stages of SCM
Each document processed via SCM goes through a structured pipeline:
| Stage | Function | Time Estimate |
|---|---|---|
| 1. Dictionary Creation | Builds a versioned token map of domain terms for consistency and compatibility | ~0.5–1 hr |
| 2. Summarization | Generates dense, tiered summaries per section (e.g., structure, compliance, semantics) | ~2–3 hrs |
| 3. Structured Data Extraction | Preserves rules, metrics, and examples using light tokenization | ~1–1.5 hrs |
| 4. Encoding & Compression | Compiles into minified JSON with gzip/Brotli compression and hash signatures | ~0.5–0.75 hrs |
| 5. Validation & Certification | Runs semantic drift checks, compression testing, metadata generation, and audit logging | ~1–1.25 hrs |
Total processing time: ~4.5–8.25 hours
Compression ratio: 94–97%
Audit trail integrity: 100% verifiable
Why It Matters
Modern AI systems face a paradox: They need more context—but can't process more text.
And when high-stakes domains like healthcare, law, or finance are involved, summarization isn't enough. You need:
- Complete structural preservation
- Interpretability by LLMs
- Traceable transformation logic
- Regulatory and legal auditability
SCM is the bridge between document complexity and AI governance.
Audit-Ready by Design
SCM embeds audit and security at every stage of the process, ensuring comprehensive compliance and verifiability.
Compression Logging
Complete tracking of gzip/Brotli fallback methods with timestamps
Semantic Drift Detection
Cosine similarity analysis with configurable threshold alerts
Coverage Matrix
Validation of structural and content completeness across all dimensions
Version Control
Ensures forward/backward compatibility with versioned dictionaries
Validation Reports
Comprehensive JSON metadata for compliance and legal archiving
Where SCM Is Used
SCM is domain-agnostic but regulation-focused. Key applications include:
| Domain | Use Case |
|---|---|
| Legal | Condensing contracts, case law, or patents with Bluebook citation support |
| Healthcare | Reducing EHR narratives or procedural records with PHI-preserving summaries |
| Financial | Compressing SEC, FINRA, or ESG disclosures with structured risk metadata |
| Regulatory | Preparing audit logs, compliance reports, and internal review artifacts |
| Secure Comms | Encoding sensitive briefings for AI-readable, human-obfuscated delivery |
| Technical Docs | Tokenizing manuals, API specs, and systems architecture for model ingestion |
Built for Governance Ecosystems
SCM is natively compatible with the FERZ Governance Stack:
- LASO(f): SCM-processed content feeds directly into LASO(f)'s tiered rule engine
- LASO(f)-AG: SCM compresses policy documents into validation-ready structures
- MRCF: SCM supports reflective prompt chains and decision traceability via semantic tiers
Think of SCM as the ETL layer for deterministic AI—except instead of extracting tables, it extracts governable knowledge.
Outcomes
- 97% compression with 90%+ semantic retention
- 100% rule and metric preservation
- AI-readable and auditor-verifiable
- Human-incomprehensible for secure transmission
- Compatible with language models, validators, and regulatory tooling
Proprietary & Protected
SCM is an internally developed, proprietary methodology by FERZ LLC. It is governed by formal validation logic, compression resilience tests, and cryptographic integrity.
Custom implementations and integrations are available by license or consulting engagement.