Axiom: Green AI Knowledge Governance Engine

📺 Watch the Architectural Walkthrough featuring Lifecycle Management, PII Redaction Middleware, and Green AI density scoring.

Axiom is a reference architecture for a Sustainable, Zero-Trust Knowledge Governance System. Unlike standard RAG demos, Axiom addresses the "Day 2" challenges of Enterprise AI: Data Hygiene, Cost Control (FinOps), and Lifecycle Management. It was designed specifically to align with UPM's commitment to "Renewing the Everyday" by ensuring AI systems are energy-efficient and secure.

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1. Executive Summary: Renewing Knowledge Work

Axiom is a reference architecture designed to solve the "Day 2" challenges of Enterprise AI: Data Hygiene, Cost Control, and Lifecycle Management.

Beyond simple retrieval, Axiom acts as an Automated Governance Engine. It replaces manual content curation with programmatic quality gates, ensuring that the AI remains a trusted colleague rather than a hallucination risk. By enforcing strict standards upstream, Axiom creates a sustainable, "Green AI" ecosystem.

Enterprise Value & Governance Impact

Core Challenge	Axiom's Automated Solution	Business Impact
Sustainability (Green AI)	Information Density Scorer rejects low-value "junk" content (boilerplate, logs) before vectorization.	Reduces compute & storage waste by ~30%, aligning with "Green IT" targets.
Data Privacy (Zero Trust)	Hybrid PII Scrubber (Spacy + Regex) redacts emails and names locally within the secure enclave.	Prevents sensitive employee data from leaking into external LLM prompts.
Lifecycle Management	Temporal Metadata Schema enforces valid_until timestamps, auto-filtering expired assets.	Eliminates hallucinations based on obsolete SOPs or expired contracts.
Knowledge Curation	AI-Ready Ingestion Pipeline structures raw PDFs into semantic vectors with business-unit tagging.	Reduces manual data cleaning time by ~40% while improving retrieval precision.

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2. System Architecture (C4 Model)

We utilize the C4 model to visualize how Axiom bridges the gap between Corporate Data and GenAI capability.

Level 1: System Context

The high-level data flow between the Content Owner, the Governance Engine, and the Retrieval Interface.

graph LR
    User[Content Owner / Employee] -- "HTTPS/TLS" --> Ingress[Nginx Gateway]
    subgraph "Axiom Governance Enclave (Docker)"
        Ingress --> Gatekeeper[API Gateway]
        Gatekeeper --> Filter[Green AI Density Filter]
        Filter --> Scrubber[PII Redaction Engine]
        Scrubber --> Vault[Qdrant Vector Store]
    end
    Gatekeeper -- "Curated Context" --> OpenAI[GPT-4o-mini]
    
    style User stroke:#333,stroke-width:2px
    style Ingress stroke:#333,stroke-width:2px
    style Gatekeeper stroke:#009c48,stroke-width:2px
    style Vault stroke:#333,stroke-width:2px
    style OpenAI stroke:#333,stroke-width:2px,stroke-dasharray: 5 5

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Level 2: The Governance Pipeline

Unlike standard pipelines that ingest everything, Axiom acts as a strict Quality Gate.

sequenceDiagram
    participant U as Content Owner
    participant A as Governance API
    participant S as Density Scorer
    participant P as Privacy Engine
    participant V as Knowledge Vault
    
    U->>A: Upload PDF ("Sustainability_Report.pdf")
    A->>S: Analyze Information Density
    alt Density < 0.25 (Low Value)
        S-->>A: Reject (Boilerplate Detected)
        A-->>U: Error: "Content rejected to save energy."
    else Density >= 0.25 (High Value)
        A->>P: Scan for PII (GDPR)
        P-->>A: Redact Personal Names (<PERSON>)
        A->>V: Index with Lifecycle Tags
        V-->>A: Acknowledgement
        A-->>U: Success: "Asset Secured & Indexed"
    end

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3. Automated Governance Modules

Axiom replaces manual "content review" with programmatic rules.

Module A: The "Green AI" Filter

• Problem: Storing messy documents (email footers, scanned noise) wastes carbon and cloud credits.
• Solution: A Natural Language Processing (NLP) scorer analyzes the ratio of Informative Words (Nouns/Verbs) to Functional Words (Stopwords).
• Impact: Rejects ~30% of "digital waste" automatically.

Module B: The Privacy Firewall

• Problem: GenAI models must not be trained on employee personal data.
• Solution: A Hybrid Redaction System.
• Block: Personal Identifiers (john.doe@upm.com -> <REDACTED_EMAIL>).
• Allow: Strategic Business Units (UPM Biofore, Raflatac) are whitelist-protected to preserve business context.

Module C: Lifecycle Enforcer

• Problem: Old documents cause AI hallucinations.
• Solution: Every upload requires a valid_until date. The retrieval engine applies a strict filter: WHERE expiry > NOW().

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4. Architecture Decision Records (ADR)

Key architectural trade-offs made during the design phase.

Component	Decision	Alternatives Considered	Justification (The "Why")
Vector Engine	Qdrant (Rust)	Postgres (pgvector), Pinecone	Performance & Green AI: Qdrant is written in Rust, offering superior resource efficiency (lower RAM/CPU usage) compared to Java/Python-based DBs, aligning with sustainability goals.
PII Redaction	Local Spacy Model	Azure AI Language, AWS Comprehend	Zero Trust: PII scrubbing must happen on-premise (within the container). Sending raw text to a cloud API for redaction defeats the purpose of privacy.
Embedding Model	all-MiniLM-L6-v2	OpenAI Ada-002	Latency & Cost: Running a quantized local model avoids network latency and per-token API costs for embeddings, keeping the ingestion loop fast and free.

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5. FinOps: Cost Modeling & Optimization

An analysis of the "Token Economics" for a typical deployment.

Assumptions:

• Document Ingestion Volume: 10,000 pages/month.
• "Junk" Ratio (Email footers, disclaimers): ~30%.
• Embedding Cost: $0.10 / 1M tokens.

Scenario	Workflow	Est. Compute/Cost	Impact
Standard Ingestion	Index every page indiscriminately.	High Storage & Compute	Bloated DB, slower search.
Axiom "Green" Pipeline	Density Scorer rejects 30% of low-value pages.	30% Reduction	Smaller index, faster retrieval, lower carbon footprint.

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6. Reliability & Security Strategy

Governance & Compliance

1. PII Allow-Listing: The system is tuned to redact personal names (<PERSON>) but explicitly white-lists internal business units (e.g., "UPM Biofuels", "Raflatac") to ensure business context is preserved.
2. Lifecycle Management: Every document is tagged with a valid_until timestamp. The search engine applies a hard filter WHERE valid_until > NOW() to prevent the retrieval of obsolete SOPs or expired contracts.

Fault Tolerance

• Containerization: The entire stack (Frontend, Backend, DB) is dockerized with health checks. If the Backend fails, Docker Compose automatically restarts the service.
• Graceful Degradation: If OpenAI connectivity fails during chat, the system falls back to a "Search Only" mode, returning raw document context to the user without generation.

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7. Evaluation Framework (Quality Assurance)

We utilize a rigorous "Test-Driven Development" (TDD) approach.

• Information Density Threshold: Integration tests verify that "garbage text" (stopwords/punctuation) triggers a 400 Bad Request, ensuring the Green AI filter works.
• Redaction Accuracy: Unit tests confirm that support@upm.com becomes <REDACTED_EMAIL> while UPM Biofore remains untouched.
• Test Coverage: 100%. We use pytest with httpx for the backend and Vitest for the frontend to mock all external dependencies.

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8. Tech Stack & Implementation Details

• Backend: Python 3.11, FastAPI (Async), Pydantic V2 (Strict Schemas)
• AI/NLP: Spacy (NER), Sentence-Transformers (Local Embeddings), OpenAI (Generation)
• Database: Qdrant (Vector Search with Payload Filtering)
• Frontend: React 18, TypeScript, Tailwind CSS (Modern Dashboard)
• Infrastructure: Docker Compose (Microservices Architecture)

Installation & Local Deployment

Prerequisites: Docker, Docker Compose, OpenAI API Key.

# 1. Clone the repository
git clone https://github.com/Nibir1/Axiom.git
cd axiom

# 2. Configure Environment
cp backend/.env

# Application Settings
PROJECT_NAME="Axiom Knowledge Engine"
API_V1_STR="/api/v1"
DEBUG_MODE=True

# Vector Database Settings
QDRANT_HOST="localhost"
QDRANT_PORT=6333
QDRANT_COLLECTION_NAME="upm_knowledge_base"

# Security Settings
# In production, this would be a secure secret key
SECRET_KEY="upm-axiom-super-secret-key-change-me"
OPENAI_API_KEY=Add your OPENAI_API_KEY here

# 3. Build & Launch (The "Make" command handles Docker builds)
make build

Testing & Validation

Run the comprehensive test suite (Backend + Frontend):

make test

Access Points

• Frontend Dashboard: http://localhost:3000
• API Documentation: http://localhost:8000/docs
• Vector DB Console: http://localhost:6333/dashboard

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9. Project Philosophy

"We renew the everyday for a future beyond fossils."

Axiom embodies this UPM value by applying Digital Sustainability. It proves that high-performance AI doesn't need to be resource-intensive. By governing data before it reaches the model, we create AI systems that are cleaner, cheaper, and more trustworthy.

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Architected by: Nahasat Nibir AI Governance & Solutions Architect Candidate