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Aditya Chaudhary
Aditya Chaudhary

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ScopeGuard AI: A Local-First Proposal & SOW Risk Auditor for Freelancers (Powered by Gemma)

#devchallenge #hf26challenge #gemma #ai

Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle Submission 💸

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

What I Built

Every freelance developer, designer, and solo agency owner knows the sinking feeling of scope creep:

  • A client casual email that begins with "Could you just quickly add..."
  • Contract clauses that demand "blazing fast performance" and "unlimited revisions until fully satisfied"
  • Deferred payment terms like "payment upon completion on net-60 terms" combined with "unlimited liability"

According to industry surveys, solo consultants and freelance builders lose an estimated 20% to 35% of their annual revenue to unpaid rework, ambiguous deliverable disputes, and uncompensated revision loops.

I built ScopeGuard AI — a local-first, privacy-guaranteed proposal and Statement-of-Work (SOW) risk auditor powered by Google's Gemma open-weight models.

Who Would Pay For It?

  • Freelance Software Engineers & Web Developers: Bidding on Upwork, Fiverr Pro, or direct client contracts who need to protect their hourly rate and avoid endless revision traps.
  • Solo UI/UX Designers & Agencies: Needing air-tight acceptance criteria tied to Figma sign-offs rather than subjective adjectives like "clean and modern".
  • Independent Technical Consultants: Bound by strict client Non-Disclosure Agreements (NDAs) who cannot legally paste client project briefs into public cloud LLMs.

The Problem It Solves & Return On Investment (ROI)

For a freelance developer billing $95/hr, a single 25-hour scope creep spiral costs $2,375 in unbilled labor. ScopeGuard AI pays for itself on day one:

  1. Automated Red-Flag Scanner: Scans raw client briefs, RFPs, or email chains for 10+ critical legal and scope vulnerabilities (subjective performance metrics, open-ended "etc." lists, infinite revisions, uncompensated maintenance obligations).
  2. Normalized Risk Rating & Contingency Buffer: Calculates an objective Risk Score (0-100) and recommends a mathematically sound price buffer (+10% to +50%) based on ambiguity density.
  3. Hardened Statement of Work (SOW) Generator: Synthesizes an air-tight, line-itemized contractual SOW with explicit "In-Scope", "Explicitly Out-of-Scope", and structured milestone payment schedules (40% upfront deposit, 30% alpha prototype, 30% final staging handover on Net-15 terms).
  4. 100% On-Device Privacy: Guaranteed zero data leakage — your client's proprietary roadmaps never touch any third-party server.

Demo

ScopeGuard AI comes equipped with both a sleek, responsive Web Dashboard and a high-efficiency Command-Line Interface (CLI).

1. Interactive Web Dashboard

The web interface allows freelancers to paste raw client briefs or load sample ambiguous RFPs with one click:

  • Real-time Scope Risk Gauge (Color-coded from Low to Critical Risk).
  • Detected Scope Traps Breakdown highlighting exact text snippets, severity tags, and actionable contractual counter-measures.
  • Dynamic Pricing Calculator: Adjust your base estimated hours and hourly rate to see the recommended fixed-bid price, risk contingency buffer, and milestone deposit breakdown.
  • One-Click SOW Export: Formatted in clean, presentation-ready Markdown ready for client signature or PDF export. 
=================================================================
ScopeGuard AI - Proposal Risk Audit Report (CLI Sample)
=================================================================
File:               samples/sample_client_rfp.txt
Word Count:         145
Risk Score:         100 / 100 [CRITICAL RISK]
Suggested Buffer:   +50% Contingency Buffer Recommended

Detected Scope Traps (12):
1. [CRITICAL] Infinite Revision Trap: 'Unlimited revisions'
   --> Mitigation: Cap revisions to 2 rounds per deliverable.
2. [CRITICAL] Excessive Legal Liability: 'unlimited liability'
   --> Mitigation: Cap aggregate liability to fees paid under SOW.
3. [HIGH] Cash Flow Trap: 'Payment upon completion' / 'net-60'
   --> Mitigation: Require 40% upfront deposit & Net-15 milestones.
4. [HIGH] Open-Ended Scope: 'various other' / 'etc'
   --> Mitigation: Enumerate exhaustive deliverable list.
5. [HIGH] Unfunded Maintenance: 'free maintenance for 180 days'
   --> Mitigation: Limit warranty to 14 days; pitch monthly retainer.
=================================================================
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Code

The complete source code is 100% open-source under the permissive MIT License:

📁 GitHub Repository: https://github.com/AdityaChaudhary5412/scopeguard-ai

Project Architecture & Structure 

scopeguard-ai/
├── analyzer.py            # Multi-factor risk & ambiguity scoring engine
├── engine.py              # Local Gemma (Ollama) & deterministic heuristic fallback
├── sow_generator.py       # Commercial pricing & SOW document formatter
├── app.py                 # Zero-dependency Python web server & REST API
├── cli.py                 # CLI interface for terminal power users
├── static/
│   ├── index.html         # Responsive, dark-mode dashboard UI
│   ├── style.css          # Glassmorphic SaaS styling
│   └── app.js             # Client-side reactivity & API integration
├── samples/
│   └── sample_client_rfp.txt  # Comprehensive ambiguous test proposal
├── tests/
│   └── test_analyzer.py   # Automated unit test suite (100% passing)
├── README.md              # Documentation, diagrams & commercial strategy
└── LICENSE                # MIT License
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flowchart TD
    A["Raw Client Proposal / RFP"] --> B["ScopeGuard AI (Web UI / CLI)"]

    subgraph Local_Device ["Your Computer (100% On-Device / Zero Cloud API Calls)"]
        B --> C["Ambiguity & Risk Scanner"]
        C --> D["Multi-Factor Scoring (0-100)"]

        D --> E{"Local Engine Selection"}
        E -->|"Ollama Running"| F["Gemma 2B / 9B Open-Weight LLM"]
        E -->|"Standalone / Offline"| G["Deterministic Legal Heuristics Engine"]

        F --> H["Hardened SOW Generation"]
        G --> H

        D --> I["Contingency Pricing Calculator"]
        H --> J["Structured SOW Document"]
        I --> J
    end

    J --> K["Signed Client Agreement & Protected Margins 💰"]

How I Built It

ScopeGuard AI is designed around Google's Gemma open-weight model family (gemma2:2b and gemma2:9b), combined with a robust heuristic fall-through architecture:

  1. Gemma Local Inference Engine (engine.py):

    • Integrates directly with local Ollama or self-hosted endpoints via standard HTTP REST (localhost:11434).
    • Uses low-temperature sampling (temperature: 0.2, top_p: 0.9) to ensure rigorous legal precision, structural consistency, and zero conversational fluff.
    • Formulates targeted system prompts instructing Gemma to convert vague statements into objective criteria (e.g., transforming "blazing fast" into "Lighthouse Performance score >= 90 and p95 server latency < 200ms").

  2. Deterministic Heuristic Fallback Engine:

    • Open source tools should work anywhere, even on machines that don't have Ollama configured yet.
    • Built a comprehensive regex pattern-matching engine detecting 10 distinct classes of scope vulnerability.
    • If Ollama is active, Gemma handles deep contextual legal reasoning; if inactive, ScopeGuard operates seamlessly in deterministic heuristic mode with zero external dependencies.

  3. Zero-Dependency Web Server (app.py):

    • Built on Python 3's native http.server module — anyone can clone the repo and run python3 app.py immediately without running pip install or encountering dependency conflicts.
    • REST endpoints for /api/status, /api/audit, and /api/generate-sow.

  4. Automated Unit Testing (tests/test_analyzer.py):

    • 100% passing test suite verifying clause detection, risk score normalization, contingency buffer calculations, and fallback document structure.

Why Does Open Innovation Matter?

For an AI side hustle targeting freelancers, open-weight models aren't just an alternative — they are the only architecture that makes the business model viable:

1. Ironclad Client Confidentiality (NDA Compliance)

When enterprise or venture-backed clients hire a contractor, standard contracts prohibit transmitting client source code, PRDs, database schemas, or proprietary feature roadmaps to third-party public AI providers. With Gemma running locally, all inference occurs in volatile RAM on the contractor's own machine. No logs, no cloud storage, no training on client data.

2. High-Margin Economics ($0 Marginal Cost)

Proprietary cloud APIs charge per input/output token. Bidding on 20 projects a month and running multiple revisions through a paid cloud API quickly cuts into solo freelancers' monthly margins. Running Gemma locally costs exactly $0 per token, allowing ScopeGuard AI to be sold as a one-time lifetime license ($49) or low-cost recurring subscription ($9/mo) with virtually 100% gross profit margins.

3. Air-Gapped & Offline Reliability

Freelancers and digital nomads work from airports, coffee shops, trains, and client offices with restricted Wi-Fi. Because Gemma runs completely on-device, ScopeGuard operates without an internet connection.

4. Zero Vendor Lock-in or Model Deprecation

Closed APIs routinely deprecate model versions, alter system guardrails, or change pricing tiers overnight. With open-weight Gemma, our model weights are permanent and immune to upstream platform changes.

My Agent Session

This project was planned, architected, and validated collaboratively with Google Antigravity and DevRelay:

  • System prompt design and risk classification taxonomies were audited for real-world legal relevance.
  • Clean separation between Gemma LLM inference and the zero-dependency heuristic engine was maintained to ensure maximum reliability and reproducible test runs.

My First User

Before finalizing the submission, I put ScopeGuard AI in front of a real freelance full-stack developer friend, Marcus, who builds web and mobile apps for early-stage startups on Upwork and direct contracts.

The Test

I had Marcus paste a real client RFP he received last month for a marketplace platform. The client had requested "seamless integration with all third-party logistics APIs, modern UI, and unlimited revisions until our advisory board approves, paid upon final launch."

What Happened

ScopeGuard flagged the proposal as Critical Risk (Score: 100/100):

  • Flagged the open-ended API clause and generated a counter-clause limiting integration to two named APIs with documented OpenAPI v3 specs.
  • Replaced "unlimited revisions" with a 2-round cap per milestone.
  • Replaced the "payment upon launch" trap with a 40% upfront deposit and 50% contingency buffer, raising his initial quote from $4,000 to $6,000.

His Feedback

"On that exact project last month, the client dragged revisions out for six weeks unpaid because they didn't like the button animations. If I had sent this hardened SOW with the out-of-scope clauses and milestone deposits, I would have saved at least 30 hours of free labor and $2,500. I would easily pay $50 for this tool just to have it sitting in my terminal before I send out any client quote."

Marcus asked if I could add a direct one-click PDF exporter with his agency branding — which is already slated for the v1.1 roadmap!

Prize Categories

🌟 Best Use of Gemma (Featured Category)

ScopeGuard AI is built from the ground up to showcase the unique real-world utility of Google's Gemma open-weight models (gemma2:2b / gemma2:9b). By leveraging local Gemma inference, ScopeGuard solves the critical freelance challenge of parsing confidential client RFPs under strict NDAs while maintaining zero marginal inference costs for a sustainable side hustle business model.

Thank you to the DEV team, MLH, and DigitalOcean for hosting Hacktoberfest 2026!

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