RedactProof & Review: Zero-Cloud Contract Auditing for Freelancers

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle

What I Built

RedactProof & Review is an offline, privacy-first desktop utility and agentic auditor designed specifically for freelance paralegals, contract specialists, and boutique agency owners.

The Problem It Solves

Freelancers and legal contractors frequently review Master Service Agreements (MSAs), Statements of Work (SOWs), and Non-Disclosure Agreements (NDAs). While commercial AI systems can summarize legal contracts quickly, uploading confidential client materials, proprietary rates, trade secrets, and personal identifiable information (PII) directly to public cloud APIs routinely breaches non-disclosure agreements, data privacy regulations (GDPR, CCPA), and professional privilege.

Who Would Pay for It

• Freelance Paralegals & Legal Ops Consultants: Professionals who bill $50–$120/hr for manual first-pass contract redlining.
• Agency Owners & Solo Contractors: Independent operators who lack in-house counsel and frequently sign one-sided vendor agreements without understanding the fine print.

The Value Proposition

Instead of spending 45 minutes manually hunting down predatory clauses or risking breach-of-contract penalties with cloud AI, RedactProof runs a two-tier local pipeline in under 15 seconds:

1. Deterministic PII Sanitization: Strips names, emails, phone numbers, tax IDs, and billing amounts locally before any semantic evaluation occurs.
2. Local Risk Auditing: Flags asymmetric termination terms, unlimited liability, overreaching IP forfeiture, non-competes, and late-payment windows (>30 days), producing an actionable risk scorecard with plain-English warnings.

Target pricing is positioned as a $79 one-time lifetime license or a $15/month flat subscription—cheaper than a single billable legal hour, with zero marginal token costs.

Code

Updesh413 / RedactProof

🛡️ RedactProof & Review

Zero-Cloud, Privacy-Preserving Contract Redaction & Predatory Clause Risk Scorer
Built for freelance paralegals, solo legal consultants, and boutique agencies.

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🌟 Overview & Core Value Proposition

Freelancers and boutique agencies routinely sign master services agreements (MSAs) and consulting contracts that hide aggressive, one-sided terms. Sending these confidential draft agreements to public cloud LLM APIs (like OpenAI or Anthropic) introduces significant compliance liabilities and data leakage risks.

RedactProof & Review eliminates this trade-off by operating with a 100% zero-cloud, air-gapped architecture:

1. Stage 1 (Deterministic Redaction Engine): Scrubs all emails, phone numbers, SSNs, Employer Identification Numbers (EINs), and dollar amounts into normalized tokens ([REDACTED_EMAIL], [REDACTED_PHONE], [REDACTED_SSN], [REDACTED_EIN], [REDACTED_AMOUNT]).
2. Stage 2 (Local Structured Risk Audit): Passes only the scrubbed contract into a local model (llama3.2:3b) running on your own workstation via Ollama, outputting strict structured JSON via Pydantic.

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🎯 Target

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View on GitHub

How I Built It

• Local Inference via Ollama (llama3.2:3b): Chosen for its lightweight 2.0 GB memory footprint and fast execution on consumer hardware (CPU or low-tier discrete GPU). Running llama3.2:3b locally allows complete offline operation without internet connectivity.
• Structured Output via Pydantic: Configured using Ollama's grammar-constrained sampling (format=AuditReport.model_json_schema()). This guarantees that llama3.2:3b returns strictly structured JSON matching risk metrics, clause classifications, and citations without hallucinating invalid schema tags.
• Dual-Engine Processing: Deterministic regex handles high-liability sensitive patterns (SSNs, phone numbers, currency figures) instantly to keep the context window compact and clean, freeing the model to focus purely on legal semantic reasoning.
• Frontend: Built with Streamlit for a fast drag-and-drop web UI and Rich for clean terminal diagnostics.
• Agent Harness & Workflow: Developed using the Antigravity CLI to scaffold, test, and package the core modules, tracked end-to-end via DevRelay.

Why Does Open Innovation Matter?

Open-source AI is the foundational feature that makes this side hustle commercially and legally viable:

1. Air-Gapped Privacy as a Differentiator: Closed cloud models (OpenAI, Anthropic) process sensitive text on remote servers. For enterprise and legal freelancers bound by strict client NDAs, a cloud call is often an automatic contract violation. Open-weight models running on-device provide a verifiable zero-cloud guarantee: the software works seamlessly even when Wi-Fi is physically disabled.
2. Zero Marginal Costs = Sustainable Flat Pricing: Closed APIs charge per-token, squeezing developer margins and forcing subscription models with usage caps. Running open-weight models locally incurs zero token expenses. Every dollar earned from a license is 100% gross profit.
3. Deterministic Reproducibility: Closed APIs undergo continuous backend updates and prompt-weight drift, which can abruptly break fragile extraction pipelines. Pinning a quantized local model version guarantees consistent evaluations indefinitely.

My Agent Session

RedactProof & Review: Zero-Cloud Privacy Contract Redaction & Risk-Scoring

You

Start recording this build session so we can save and embed our full transcript into our DEV submission post at the end.

Agent

Confirmed session tracking active. Verified local workspace environment and prepared DevRelay session preservation pipeline.

You

Goal: Build a zero-cloud, privacy-preserving contract redaction and risk-scoring tool called 'RedactProof & Review' for freelance paralegals and boutique agencies.

Core Constraints:
1. Use Ollama running locally on this machine with llama3.2:3b (no remote cloud LLMs).
2. Implement two-stage processing: Stage 1 deterministic regex/heuristic PII scrubber; Stage 2 structured Pydantic extraction via local Ollama.
3. Target 5 risk categories: Unilateral IP Assignment, Aggressive Non-Competes, One-Sided Indemnification/Unlimited Liability, Extended Payment Terms, Asymmetric Termination.
4. Deliverables: redact_engine.py, app.py (Streamlit), requirements.txt.
5. Synthetic test_contract.txt with 4-5 predatory clauses and sample PII. Verify end-to-end.

Show more

Agent

▸ run_command

Output

ollama list: verified llama3.2:3b present and ready on local GPU

▸ run_command

Output

pip install ollama streamlit: installed required dependencies

▸ search_dev_to_semantic

Output

Identified DEV Community Wisdom on JSON Schema field ordering for local constrained decoding

▸ write_to_file

Output

Created requirements.txt and test_contract.txt with realistic predatory clauses and PII

▸ write_to_file

Output

Created redact_engine.py with Stage 1 regex scrubber and Stage 2 Ollama Pydantic structured risk audit

▸ run_command

Output

Executed CLI audit: 13 PII items redacted; scored 8/10 Critical Risk across all 5 predatory clauses

▸ write_to_file

Output

Created app.py (Streamlit UI with side-by-side diff and risk meter) and README.md

▸ run_command

Output

Initialized git repository and committed all project deliverables

Built RedactProof & Review end-to-end. Stage 1 scrubbed 13 sensitive PII tokens (emails, phones, SSNs, EINs, amounts) with zero leakage. Stage 2 accurately flagged all 5 predatory clauses with evidence-first schema ordering, plain-English paralegal explanations, and balanced redlines.

You

remove the Export & DEV Embed section from the UI, it should not be there

Agent

▸ replace_file_content

Output

Removed Export & DEV Embed tab and section from app.py

▸ replace_file_content

Output

Updated README.md feature list

▸ run_command

Output

Recompiled app.py cleanly and committed change to git

Removed the Export & DEV Embed section from app.py. The UI now cleanly focuses on the three core tabs: Risk Scorecard, Side-by-Side Redaction Diff, and Flagged Clauses & Redlines.

You

submit_agent_session

Agent

▸ submit_agent_session

Output

Submitting sanitized, normalized session transcript to DEV via DevRelay

Submitting session to your DEV profile for native Liquid embedding in your hackathon/DEV submission post.

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