How does the Hansen Fit Score, e.g. Metaprise, Agent-based, and Strand Commonality models use both Text-RAG and Vision-RAG?

EDITOR’S NOTE: Today’s post is my reply to the following LinkedIn post by IBM’s VP of AI Platform, Armand Ruiz.

***HERE ARE MY THOUGHTS ON TEXT RAG VERSUS VISION RAG***

The Hansen Fit Score framework—comprising the Metaprise, Agent-based, and Strand Commonality models—assesses and optimizes procurement technology solutions by leveraging advanced data retrieval and analysis techniques. As procurement challenges become more complex and data sources more varied, these models increasingly integrate both Text Retrieval-Augmented Generation (Text RAG) and Vision Retrieval-Augmented Generation (Vision RAG) to achieve comprehensive, context-rich insights.

How Each Model Leverages Text RAG and Vision RAG

1. Metaprise Model

• Purpose: Orchestrates human-AI collaboration across procurement functions, breaking down silos and fostering ecosystem-wide integration.
• Text RAG Use: Retrieves and synthesizes textual data from contracts, policies, supplier communications, and ERP records to inform collaborative decision-making and workflow automation.
• Vision RAG Use: Processes visual data such as scanned contracts, compliance certificates, invoices, and infographics. Vision RAG enables the model to extract and reason over information embedded in tables, signatures, diagrams, and non-textual layouts—critical for real-world procurement documents that often mix formats.
• Result: Ensures that both written and visual procurement artifacts are accessible and actionable, reducing implementation times and improving cross-functional alignment.

2. Agent-Based Model

• Purpose: Simulates dynamic, decentralized procurement environments using autonomous agents (people, bots, policies) that interact and adapt in real time.
• Text RAG Use: Agents retrieve and interpret textual signals from supplier performance logs, market reports, and regulatory updates to drive scenario planning and automated responses.
• Vision RAG Use: Agents analyze visual cues such as shipment photos, product images, scanned receipts, and visual dashboards. This is essential for detecting anomalies (e.g., damaged goods), verifying compliance (e.g., label checks), and integrating visual evidence into decision cycles.
• Result: Enables agents to make context-aware decisions by combining insights from both text and visuals, thus improving resilience and adaptability in volatile environments.

3. Strand Commonality Model

• Purpose: Identifies and leverages hidden connections across disparate data streams, allowing organizations to detect patterns and optimize outcomes.
• Text RAG Use: Discovers commonalities in textual data—such as recurring supplier risks, ESG disclosures, or policy overlaps—across multiple business units and geographies.
• Vision RAG Use: Finds patterns in visual data, such as similarities in scanned forms, layout structures, or recurring chart types, which may signal underlying operational or compliance trends.
• Result: By unifying textual and visual data, the model uncovers deeper, cross-modal insights that would be missed by text-only or vision-only approaches, supporting predictive analytics and early risk detection.

Why Both Modalities Matter

Practical Implications

• Resilience: Integrating both Text and Vision RAG allows organizations to respond faster and more accurately to disruptions, as all relevant information—regardless of format—is available for analysis and action.
• Transparency: The Hansen Fit Score’s transparency is enhanced when both text and visual evidence are considered, leading to more robust benchmarking and validation.
• Innovation: Providers that embed both RAG modalities into their solutions can demonstrate superior alignment with enterprise needs, especially where procurement data is fragmented across multiple formats.

Summary

The Hansen Fit Score’s Metaprise, Agent-based, and Strand Commonality models use both Text RAG and Vision RAG to create a holistic, adaptive, and future-proof procurement analytics and decision-making framework. By leveraging both modalities, these models ensure that no critical information—whether textual or visual—is overlooked, enabling more accurate, resilient, and strategic procurement operations in an increasingly complex world.

***BONUS COVERAGE***

Why Text RAG and Vision RAG Are Critical for Continuous Loop-Back Learning and Verification in Self-Learning Algorithms

The Role of Continuous Loop-Back Learning

Continuous loop-back learning—where algorithms repeatedly learn from new data, feedback, and their own outputs—relies on robust feedback cycles to drive improvement and ensure accuracy. In self-learning systems, this loop enables:

• Ongoing adaptation to new patterns and data.
• Immediate correction of errors or misinterpretations.
• Enhanced resilience through iterative refinement and verification.

Why Both Text RAG and Vision RAG Matter

Self-learning algorithms in procurement and other complex domains must process information from diverse sources—textual, visual, or mixed. Here’s why both modalities are especially important:

1. Comprehensive Feedback Integration

• Text RAG enables the system to retrieve and reason over written feedback, logs, and structured records.
• Vision RAG allows the system to interpret and verify information from images, diagrams, scanned documents, and complex layouts.
• Combined, they ensure no critical feedback is lost, regardless of format, making the learning loop robust and all-encompassing.

2. Accurate Verification

• Self-learning algorithms must verify their outputs against ground truth or human feedback.
• Visual data (e.g., scanned invoices, annotated diagrams) can contain essential verification cues that text-only systems would miss.
• Vision RAG supports the verification of outputs by enabling the system to “see” and cross-check against visual evidence, reducing the risk of undetected errors.

3. Closing the Performance Gap

• Continuous feedback loops help algorithms identify and address weaknesses.
• By leveraging both text and vision, self-learning systems can detect subtle discrepancies or anomalies that would otherwise go unnoticed, accelerating convergence toward optimal performance.

4. Enabling Self-Verification and Trust

• Advanced self-learning models increasingly incorporate mechanisms to prove or verify their own correctness.
• Integrating both text and visual feedback into the verification process strengthens the system’s ability to self-audit, explain its reasoning, and build user trust—especially in high-stakes or regulated environments.

Summary Table: Impact of Text RAG and Vision RAG in Continuous Learning Loops

Key Takeaways

• Continuous loop-back learning depends on capturing, interpreting, and acting on all available feedback—textual and visual.
• Text RAG and Vision RAG together enable self-learning algorithms to continuously verify, correct, and improve themselves, making the learning process more resilient, trustworthy, and effective.
• This dual-modality approach is essential for domains where information is fragmented across formats, ensuring that self-learning systems achieve true continuous improvement and reliable self-verification

***PRACTICAL APPLICATION IN PROCURETECH SOLUTION PROVIDER SELECTION (How does the Hansen Fit Score improve ProcureTech Solution Provider Selection?)***

Key Differences: Hansen Fit Score vs. Traditional Analyst Models

Traditional analyst models (such as those from Gartner, Spend Matters, Deloitte, McKinsey, and G2) typically focus on:

• Historical, transactional, and financial KPIs
• Feature/functionality checklists
• One-size-fits-all vendor rankings
• Departmental efficiency and cost control
• Limited adaptability to organizational change and readiness

The Hansen Fit Score (HFS), by contrast, is built on advanced models (Metaprise, Agent-based, Strand Commonality) and emphasizes:

• Organizational alignment and technology adoption readiness
• Practitioner-centric, real-world fit between provider and buyer
• Cross-functional integration and risk mitigation
• Adaptive benchmarking and continuous feedback loops
• Predictive foresight for future procurement success

Quantitative Improvements with Hansen Fit Score

Why Hansen Fit Score Delivers Superior Results

• Practitioner-Centric Design: HFS matches solutions to real organizational needs, increasing adoption and reducing costly missteps.
• Predictive, Not Just Historical: Focuses on future readiness, not just past performance, enabling better long-term outcomes.
• Adaptive & Evidence-Based: Incorporates practitioner feedback and adaptive benchmarking for continuous improvement.
• Operational Precision: Evaluates integration complexity and cross-functional alignment, leading to higher success rates.
• Risk & Cost Efficiency: Prioritizes solutions with lower implementation risk and higher ROI, essential for today’s dynamic procurement landscape.
• Transparency & Trust: Delivers clear, actionable insights trusted by practitioners, outperforming traditional models in transparency and measurable business value.

Summary Table: HFS vs. Traditional Analyst Models

Conclusion

The Hansen Fit Score delivers a step-change improvement in ProcureTech solution provider selection by aligning technology choices with organizational readiness, strategic goals, and real-world practitioner needs. Compared to traditional analyst models, HFS improves key outcomes—such as implementation success, ROI, cost savings, and risk reduction—by measurable margins, often 10–30% or more, and is recognized as a best-in-class, practitioner-trusted framework for modern procurement transformation.

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