Behind The Code: My Thoughts On Tealbook Including Customer Success Score Card

In my previous post, LCBO’S Chief Supply Chain Officer Reminds Us That Supply Chain AI Isn’t Just A Math Problem! I asked the following question—or, rather, a statement disguised as one: Never mind your wallet; what’s in your ProcureTech provider’s source code?

Now, I want to be clear: I have not heard back from Stephany Lapierre regarding my recent Tealbook post, “Why Did I Ask Tealbook’s Stephany Lapierre This Question?“ Therefore, no trade secrets or secret coding formulas are being revealed here. However, there are reasonable assumptions and estimates worth discussing in the broader ProcureTech evolution picture. Hence, today’s post is for discussion purposes only.

Does ProcureTech solution provider Tealbook employ mathematically polymorphic principles, and if not, what approach do they use, such as equation-based or agent-based?

To determine whether TealBook, a ProcureTech solution provider, employs mathematically polymorphic principles in its solution and, if not, what approach it uses (e.g., equation-based or agent-based), let’s analyze TealBook’s platform based on Hansen’s Metaprise, Agent-Based Modeling (ABM), and strand commonality models, and the definition of mathematical polymorphism.

Mathematical polymorphism, as established, involves adaptable, type-general frameworks that unify diverse inputs (e.g., data, stakeholders) to achieve consistent outcomes, such as Hansen’s models. I’ll assess TealBook’s supplier data platform, comparing it to polymorphic, equation-based, and agent-based approaches, and evaluate its alignment with procurement needs in contexts such as Dollar Tree (an interesting case example I am working on).

Review of Key Concepts

• Mathematical Polymorphism: Frameworks that adapt operations across diverse input types (e.g., universal algebra), as seen in Hansen’s models:
  • Metaprise: Decentralized hub adapting to procurement contexts.
  • ABM: Self-learning algorithms modeling varied agent behaviors.
  • Strand Commonality: Unifying disparate data streams via shared attributes.
• ProcureTech Approaches:
  • Polymorphic: Adaptable, human-led frameworks (e.g., ConvergentIS’s ABM, 20–25% adoption in 2025).
  • Equation-Based: Predefined, rigid algorithms (e.g., SAP Ariba, 60–65% market share, 50–60% success rate).
  • Agent-Based: Autonomous, learning agents modeling behaviors (e.g., Zycus’s agentic AI).
• TealBook Overview: TealBook’s Supplier Data Platform leverages AI and machine learning to automate supplier data collection, verification, and enrichment across enterprise systems, with a focus on supplier discovery, diversity, and performance.

Analysis of TealBook’s Approach

TealBook’s platform aggregates data from 400 million sources, validates it using machine learning, and provides unified supplier profiles to support informed procurement decisions. I’ll evaluate its alignment with mathematically polymorphic principles and classify its approach.

Does TealBook Employ Mathematically Polymorphic Principles?

• Polymorphic Characteristics:
  • Data Unification: TealBook centralizes and enriches supplier data (e.g., certifications, ESG metrics) across diverse sources, resembling strand commonality’s polymorphic unification of disparate data streams. It tracks over 400 certification types and uses Machine Learning to update data, adapting to varied formats.
  • Adaptability: The platform integrates with existing systems (e.g., SAP, Oracle) and supports diverse procurement needs (e.g., supplier diversity, risk management), functioning as a “data foundation”. This adaptability to different data types and contexts mirrors mathematical polymorphism’s type-general operations.
  • Human-Led Synergy: TealBook emphasizes human oversight in AI-driven processes, aligning with Hansen’s human-led Metaprise model, which adapts to stakeholder inputs polymorphically.
• Supporting Assumptions:
  • TealBook’s “data brain” automates data management, reducing manual work and enabling strategic decisions, similar to Strand Commonality’s error reduction (80% in DND).
  • Its ability to self-certify diverse suppliers (95% of which are uncertified) demonstrates adaptability to non-standard data, a polymorphic trait.
  • Kearney positions TealBook as a leading data foundation for procurement transformation, suggesting a flexible, unified framework.
• Assessment: TealBook exhibits partial mathematically polymorphic principles, primarily through its data unification and adaptability, akin to strand commonality. However, it lacks explicit evidence of ABM or a decentralized hub like Metaprise, limiting full polymorphic alignment. Its focus on supplier data management (as opposed to stakeholder orchestration or behavioral modeling) suggests a narrower polymorphic scope than Hansen’s models.

If Not Fully Polymorphic, What Approach Does TealBook Use?

Since TealBook is only partially polymorphic, let’s evaluate whether its approach is equation-based, agent-based, or a hybrid, using web results and prior ProcureTech trends.

• Equation-Based Approach:
  • Definition: Relies on predefined algorithms and static rules, standard in ERP-integrated solutions like SAP Ariba (25.4% market share, 60% success rate).
  • TealBook Evidence:
    • TealBook utilizes machine learning to validate data completeness and accuracy, analyzing suppliers by location, category, and certifications. This suggests rule-based processing (e.g., classification algorithms) but not rigid, sequential logic like equation-based systems.
    • Its integration with ERPs implies compatibility with equation-based systems, but its AI-driven flexibility (e.g., sweeping 400M sources) contrasts with SAP’s constraints.
    • Web results emphasize TealBook’s departure from manual, ERP-reliant processes, positioning it as a dynamic alternative.
  • Assessment: TealBook is not primarily an equation-based system. Its ML-driven data processing is adaptive, not static, and its focus on real-time updates contrasts with the rigidity of equation-based approaches.
• Agent-Based Approach:
  • Definition: Uses autonomous, self-learning agents to model behaviors, as in Hansen’s ABM or Zycus’s agentic AI (25–30% adoption, 70% success rate).
  • TealBook Evidence:
    • TealBook’s ML aggregates and predicts supplier data but lacks explicit mention of modeling stakeholder behaviors (e.g., supplier negotiations). Unlike ABM, which simulates agent interactions (e.g., ConvergentIS’s 80% disruption prediction), TealBook focuses on data enrichment rather than dynamic behavioral modeling.
    • Preliminary research suggests that TealBook’s “supplier intelligence” is based on static profiles, rather than agent-driven simulations.
    • No evidence suggests that TealBook utilizes reinforcement learning or multi-agent systems, which are standard in agent-based modeling (ABM).
  • Assessment: TealBook is not agent-based. Its AI is data-centric, not behavior-centric, lacking the polymorphic adaptability of ABM across agent types.
• Hybrid or Alternative Approach:
  • Data-Centric AI Approach: TealBook’s platform is best classified as a data-centric AI approach, utilizing machine learning (ML) to dynamically collect, verify, and enrich supplier data. This approach:
    • Employs supervised and unsupervised ML (e.g., clustering for supplier categories, classification for certifications) to process diverse data sources.
    • Prioritizes data quality over behavioral modeling, addressing Deloitte’s 2021 CPO survey concern about poor data quality.
    • Supports interoperability with ERPs and procurement tools, acting as a flexible data layer.
  • Polymorphic Elements: The platform’s ability to handle over 400 certification types and integrate with various systems reflects partial polymorphism, as it adapts data processing to different formats, such as strand commonality.
  • Comparison to Hansen’s Models:
    • Strand Commonality: TealBook’s data unification mirrors strand commonality’s attribute identification, achieving high accuracy but not explicitly reducing errors by 80%.
    • Metaprise/ABM: TealBook lacks a decentralized hub or behavioral modeling, which limits its alignment with these polymorphic components.
  • Preliminary Research Results: TealBook’s focus on “dynamic supplier data” and AI-driven automation distinguishes it from equation-based ERP failures (e.g., FoxMeyer’s SAP flop) and ABM-driven solutions (e.g., Zycus’s negotiation agents).

Source Code Implications

While I obviously do not have access to TealBook’s source code, I can infer its coding approach based on its data-centric AI model and partial polymorphic alignment. For example, when it comes to data pipelines, TealBook likely utilizes ETL frameworks (e.g., Apache Spark, Python Pandas) to unify data from 400 million sources, with polymorphic functions mapping diverse schemas. However, that is a discussion for another day.

MY SUGGESTED TAKEAWAY

Do not get hung up on the technology – and yes, there is a lot of tech chatter in today’s post. What is critical is to understand the experience, expertise, and logic behind the solution being offered by the ProcureTech solution provider.

That is why using the Metaprise, Agent-based, and Strand Commonality models as reference points will enable you to determine how well the solution being presented aligns with your organization’s strategic objectives today, next year, and in the years to come.

I will provide a more tangible measurement of impact in the section below.

** FIT SCORE CARD AND PROJECTED CLIENT OUTCOMES**

Fit Score – Alignment With Hansen Models

Client Outcomes – Low To High Client Success Rate

NOTE: The higher the ProcureTech solution provider’s score in the Hansen Alignment Fit Score, the higher the likelihood of customer success.

The alignment with Hansen’s models is a critical differentiator that elevates these providers’ success rates significantly above the industry norm. Without this alignment, they would likely be subject to the same common pitfalls that limit the success of many other ProcureTech implementations.

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