FDE Academy Education Gets Operationalized Through Yabouze Platform's Ontology-Based Execution Assets

As Forward Deployed Engineering (FDE) becomes established as the execution standard for global AI transformation, the role of corporate training platforms is fundamentally changing. This article analyzes industry-noted changes following SB Consulting's CEO Shim Jae-woo, who is developing an FDE strategy operations platform in Jung-gu, Seoul, publicly disclosing a 2-day intensive practice-based Yabouze platform architecture. Unlike conventional online education systems, this article examines how the Yabouze model—equipped with an asset transformation pipeline that connects "tacit knowledge → ontology → AI Agent → governance → pitch deck"—is redefining corporate training effectiveness.

Following Accenture and Microsoft's launch of their FDE Practice in March 2026, global IT companies including Salesforce, EY, and AWS are successively expanding their FDE organizations and partner networks. This represents a paradigm shift from "AI adoption" to "AI design, build, and operation internalization." Within this flow, corporate training platforms are evolving beyond mere content repositories or Learning Management Systems (LMS) to the level of a "strategic operations OS" capable of structuring on-site problems and immediately converting them into execution assets.

The core of the Yabouze platform is that it "positions ontology as the central axis of the educational process." While conventional Learning Management Systems (LMS) tracked learning progress by focusing on content formats such as videos, PDFs, and quizzes, Yabouze converts on-site data input by learners in real-time through "concept extraction → relationship definition → structuring into ontology's seven-element structure." In this process, AI detects incomplete inputs and presents guiding questions, allowing learners to clearly redefine their own field problems.

An ontology is a system that defines the semantic structure of data. Historically, this was often constructed by data engineers or dedicated knowledge management personnel as separate projects. Yabouze has integrated this into the educational process itself. For example, when a manufacturing company's production efficiency problem appears on the surface as "equipment failure," the learner articulates "which variables interact, when, and in what sequence" in ontology form. This structured data becomes the input for the next stage—AI Agent design and workflow governance—enabling education to directly connect to on-site operational design.

Distinguishing characteristics provided by ontology-based education:

•Extract 20 or more instances of tacit knowledge (knowledge only discernible from field experience) through question-based methods, and immediately convert them into machine-readable structures

•Data generated during the learning process accumulates to form an independent repository per team, enabling use as foundational assets for on-site execution even after training concludes

•During the ontology validation phase, "flat concept enumeration" transforms into revealed "mutual relationships and priorities," enabling AI Agents and workflows to possess more sophisticated decision-making structures

The Yabouze platform is designed with 46 modules and a 5-team independent operations system, with the entire training compressed into a 2-day program. On Day 1, learners redefine field problems and complete the ontology's seven elements; on Day 2, these are converted into AI Agent design and KPI models, with final 8-slide pitch decks automatically generated. This compressed process is possible because each module is designed not for linear progression but rather for "automatic handoff where the output of the previous module becomes the default input for the next module."

The Day 1 workflow proceeds as follows:

Problem Redefinition: Reconstruct surface problems ("declining sales," "reduced customer satisfaction") as genuine bottlenecks ("decision-making delays," "insufficient data consistency")

Tacit Knowledge Question Extraction: Present 20+ semi-structured questions to learners, collecting responses that can only emerge from field experience

Concept Extraction and Ontology Design: Extract key concepts and relationships from answer responses, formalize them into seven-element structure (nodes, relationships, attributes, constraints, events, workflows, KPIs) based on the Palantir FDE model

Day 1 Checkpoint: AI and instructors jointly evaluate ontology completeness, supplementing any gaps or contradictions

Day 2 focuses on converting ontology into execution assets:

AI Agent Design: Design the decision-making logic, input/output data specifications, and failure case responses of the AI agent to be deployed on-site based on the ontology

Workflow and Governance: Define human intervention points where the agent operates, approval structures, and monitoring cycles

KPI Design: Define how each element of the ontology maps to actual business metrics (profitability, efficiency, quality)

Automatic Pitch Deck Generation: Automatically convert all above data into 8 slides of final presentation materials, ready for immediate use in executive reporting and investor pitching

Execution effects of the 2-day intensive program:

•Complete on-site problem definition → structuring → execution design within 48 hours, compressing traditional 3-6 month consulting processes

•Module outputs accumulate, creating "living assets" that enable teams to continue pursuing ontology updates and Agent enhancement after completion

•While five teams operate independently, the common app shell standardizes final outputs, accelerating organizational dissemination speed

The key element enabling the Yabouze platform to function as an "FDE execution system" beyond a mere educational tool is that Palantir's five global FDE principles are embedded throughout all stages of the educational process. These principles are not abstract ideologies but are directly reflected in module design and AI guidance logic.

Outcome Ownership: From the ontology design stage, "business result improvement" rather than "feature completion" is set as the top-level objective. For instance, rather than "build an AI chatbot," the performance metric "reduce customer response time from 48 hours to 4 hours" is explicitly specified in the ontology's KPI layer.

Embedded FDE: Because learners participating in Yabouze's 2-day program address their actual on-site problems rather than hypothetical scenarios, they themselves become "embedded FDE." The ontology construction process directly becomes on-site workflow redesign, and agent design becomes preparation for on-site deployment.

Ontology as Operational Layer: Ontology becomes "operational rules" referenced daily by on-site AI agents rather than a recordkeeping device like a data catalog. When each element of the ontology changes, the agent's decision-making logic automatically updates accordingly.

Iterate: A distinct "ontology modification and refinement" module exists within the educational process, and during the post-2-day field expansion stage (9 modules), "agent re-evaluation and retraining through optimized ontology" continues.

Methods through which these principles are reflected in the educational process:

•In the problem redefinition module, distinguish "surface symptoms" from "genuine bottlenecks" and clarify responsibility for results

•Each team builds its ontology using its own field data, driving "embedded" change

•Since pitch deck generation is based on ontology, changes in field data are immediately reflected in executive communications

Another distinguishing characteristic of the Yabouze platform is that it explicitly designs the phase after the 2-day training for on-site application. While conventional corporate training followed "training completion = project completion," Yabouze shifts to "training completion = field expansion begins." To achieve this, it provides nine separate modules: Field Manual, Global Best Practices, Best Practice Compare, Platform Diagnosis, Vibe Coding Lab, MVP Sprint Planner, Agent Evals, Enablement, and Know-how Library.

The Field Manual module converts the ontology and agent design generated during training into field guides. For example, if there is manufacturing sector ontology, it is reconstructed as "decision-making manuals for production line engineers." The Global Best Practices module encourages reflection by comparing one's ontology against global cases in the same industry, revealing how one's ontology differs from industry standards. This serves as a mechanism for continuously injecting external perspectives, preventing the learning organization from becoming trapped in "our way of doing things."

The Platform Diagnosis module diagnoses "discrepancies between ontology and field operations" at 3-month, 6-month, and 12-month intervals post-training. It measures whether AI agents function as expected, identifies where workflow bottlenecks emerge, determines whether KPIs have actually improved, and recommends ontology updates. The MVP Sprint Planner automatically generates ontology-based MVP (Minimum Viable Product) development schedules.

Cumulative effects of field expansion modules:

•The 2-day training outputs (ontology, agent, pitch deck) continue to be validated and enhanced on 6-month to 2-year cycles

•Each organization's ontology and performance data accumulate at the platform level, cycling as learning materials for subsequent learners

•On-site feedback is reflected back into module design, forming a "living learning ecosystem"

The most innovative aspect of the Yabouze platform is its "asset transformation pipeline." Through the process where input assets (on-site scenarios, 20+ tacit knowledge items) transform into structured assets (ontology's seven elements), which then transform again into execution assets (AI Agent, Workflow, KPI models), "bottlenecks and opportunities the organization had not yet recognized" become visible at each transformation stage.

For example, consider designing a bank's loan approval process as an ontology. On the surface, there is dissatisfaction that "evaluation criteria are unclear." However, through the tacit knowledge extraction process, 20+ specific variables emerge: "different data interpretation standards among credit rating agencies," "intervention points where risk managers apply intuitive judgment," and "processing speed variations depending on customer influx timing." When structured into an ontology, it becomes clear "which information combinations are the true cause of approval delays." Based on this ontology, when AI Agent design is implemented, the agent performs "decision-making that prioritizes removing bottleneck variables" rather than "mechanical assessment."

The advantage obtained through this process is not merely "efficiency improvement" but rather "transparency of decision-making principles." Executives can examine the ontology's KPI mapping and clearly judge "will this process improvement actually deliver 10% revenue growth?" Field staff can read the AI agent's governance rules and know "exactly when I need to intervene." Thus, the organization's "implicit knowledge" converts into "explicit strategy."

Organizational effects of accumulated structured assets:

•Executive decision-making cycles accelerate from monthly (based on conventional reports) to weekly

•New team member onboarding time shortens from 3 months to 2 weeks (rapid learning through ontology and agent rules)

•During inter-departmental collaboration, the process of first aligning "each department's ontology" proactively removes anticipated conflict points before project initiation

As global IT companies announce FDE organization expansions, the issue of "FDE workforce development" has emerged simultaneously. Accenture operates an internal FDE bootcamp concurrent with its 2026 FDE Practice launch, and Microsoft's Azure team is strengthening engineer training for field deployment to customers. AWS's $1 billion embedded AI engineer investment also centers on "FDE talent acquisition and training."

Meanwhile, existing online education platforms (Coursera, Udacity, LinkedIn Learning, etc.) are rapidly adding "AI implementation" content, but most remain at the level of "coding skills" or "AI tool usage." That is, they can enhance "individual capabilities" but cannot reach the stage of "structuring and solving organizations' on-site problems"—a fundamental limitation.

In contrast, the Yabouze platform's position is converting "specific corporate field problems" into "ontology-based execution assets" within 2 days. This is neither conventional LMS (learning progress tracking) nor typical online bootcamps (skills training). Rather, it is creating a new category that "compresses strategic consulting (3-6 months) and AI implementation (6-12 months) into an educational process."

Current market differentiators of Yabouze:

•Conventional LMS: Focus on content distribution and progress tracking → Disconnected from field application

•Conventional online bootcamps: Focus on individual skill improvement → Disconnected from organizational problem-solving

•Yabouze: Completely connects field problem → ontology → agent → organizational operational change

Q1: Can ontology really be completed in 2 days? Conventional data governance projects took 6+ months.

A: Yabouze does not aim for "perfect ontology." Instead, it completes "minimum sufficient ontology required for solving field problems" in 2 days and continuously enhances it through 9 months of field expansion modules. At the Day 2 checkpoint, instructors and AI jointly validate "can we build an agent with this ontology," and upon reaching that level, the execution phase immediately begins. If conventional governance projects aimed at "organization-wide standardization," Yabouze focuses on "specific field problem-solving," resulting in much smaller scope.

Q2: What happens if ontology and field operations diverge after training?

A: The Platform Diagnosis module operates at 3-month, 6-month, and 12-month intervals. This module measures differences between "what actually happens in the field" and "what the ontology predicted," recommending ontology updates to narrow the gap. For instance, if an AI agent operated according to "customer request classification" ontology but 3 months later 20% of requests were a new type, the ontology's classification criteria are enhanced. As this feedback loop continues, the ontology becomes "living operational rules."

Q3: Won't organizational consistency break if five teams each create different ontologies?

A: Yabouze's common app shell and standardized pitch deck template ensure consistency. Although five teams address different field problems, the ontology's seven elements (nodes, relationships, attributes, constraints, events, workflows, KPIs) use the same framework. The final pitch deck of eight slides also uses the same template, enabling executives to compare teams using standardized metrics like "Team A achieved 30% efficiency improvement, Team B achieved 45% response time reduction." Additionally, the Global Best Practices comparison module includes a step for aligning ontologies among teams, allowing discovery and integration of common elements when necessary.

Q4: Will learners have sufficient competency to properly operate ontology after 2 days of intensive training?

A: Yabouze designs the 2-day training as a "learner's starting point for independent operations," not as "completion." The Enablement module is separately provided, enabling learners to perform continuous support in ontology operations, agent monitoring, and KPI tracking after training. Additionally, the Know-how Library module accumulates ontology examples from the same industry, allowing new team members to rapidly learn by referencing existing ontologies. When this occurs, ontology maintenance no longer depends on 1-2 "experts" but becomes a structure where the entire team understands and updates it.

Q5: Does the Yabouze platform support integration with global platforms like Salesforce or Microsoft?

A: Ontologies and API specifications generated in Yabouze can be downloaded in JSON format and integrated with existing enterprise systems (Salesforce, SAP, Azure, AWS). For instance, if you created an ontology for customer onboarding processes in Yabouze, you can map it to Salesforce Flow for automation. Additionally, the Vibe Coding Lab module supports converting generated ontologies into "production code," enabling rapid implementation of AI Agent decision-making logic on top of existing systems.

The emergence of the Yabouze platform fundamentally redefines the role of corporate training. While conventional corporate training was "competency development," Yabouze aims for "field problem structuring and immediate execution." The benefits companies gain through this are as follows.

First, decision-making speed accelerates. Where executive reporting occurred on a monthly basis, it now occurs on a weekly basis due to ontology-based KPI updates. Second, organizational scaling becomes possible. New teams can reference proven ontologies from Know-how Library and complete field problem structuring in 2 days rather than 3-6 months, enabling rapid organizational expansion. Third, the cost of implementation decreases. By converting training outcomes directly into execution assets, the gap between traditional "learning" and "implementation" phases is eliminated, reducing overall project duration and cost.

The most significant change, however, is in organizational maturity. Companies transform from operating based on "implicit knowledge held by key individuals" to operating based on "explicitly structured, continuously validated, and improved operational rules"—the essence of FDE-driven transformation.