Strategic Consulting
Bangkok, Thailand
Our Platform
Our platform automates the entire journey from competency assessment to personalized learning. A multi-agent AI pipeline delivers results in minutes, not weeks.
How It Works
Our platform consists of three interconnected systems that work together to create a complete competency development lifecycle — from assessment through AI analysis to personalized learning.
System A — Competency Evaluation
Employees complete a structured competency assessment aligned to their position requirements. Scores are mapped against a Position-Competency Matrix to quantify skill levels across all relevant dimensions.
Define required competency levels for every position in your organization. The matrix serves as the benchmark against which all assessments are measured.
Employees can self-assess, or administrators can conduct assessments directly. Both modes produce quantitative scores on a 1-5 scale across all relevant competencies.
Create and customize competency frameworks that match your organization's specific needs — from AI foundations to strategic leadership capabilities.
Automatically calculate the difference between required and actual competency levels. Visual gap analysis highlights priority areas for development.
System C — Multi-Agent AI Pipeline
Four specialized AI agents work in sequence: the System Analyst identifies gaps, the Learning Designer creates modules, the Resource Researcher matches courses via RAG search, and the Project Manager validates the final plan.
Analyzes assessment scores against position requirements, identifies and prioritizes skill gaps, and generates a structured gap report for the downstream agents.
Takes the gap analysis and designs tailored learning modules — each with specific objectives, recommended duration, difficulty level, and learning outcomes.
Uses semantic search (RAG) against a ChromaDB vector database of courses to find the most relevant learning resources for each module.
Orchestrates the entire pipeline, validates outputs at each stage, assembles the final learning path, and saves results to the database.
System B — Learning Platform
Each employee receives a personalized learning path with curated courses, progress tracking, and clear milestones. The platform closes the loop between assessment, AI analysis, and actual skill development.
Every learning path is unique — generated specifically for the individual based on their assessment results, position requirements, and skill gaps.
Courses are matched using semantic similarity via vector embeddings, ensuring relevance goes beyond keyword matching to true content alignment.
Track learning progress across all assigned modules and courses. Visual indicators show completion status, time invested, and competency improvement.
Reassess periodically to measure competency growth. The platform tracks improvement over time and adjusts learning paths as skills develop.
Under the Hood
Four specialized AI agents collaborate in a linear relay workflow, each contributing their expertise to produce a comprehensive, personalized learning plan.
Orchestrate & Validate
Initiates the pipeline, passes context to each agent, validates intermediate outputs, and assembles the final learning path. Acts as quality gate.
Identify Skill Gaps
Analyzes assessment scores against position requirements, calculates gap severity, and produces a prioritized list of development areas.
Design Modules
Creates structured learning modules with objectives, difficulty levels, estimated duration, and clear outcomes for each identified gap.
Match Courses (RAG)
Searches the vector database using semantic similarity to find the best courses for each module. Returns ranked recommendations with relevance scores.
Agent Framework
Orchestrates multi-agent workflows with state management, conditional routing, and tool integration.
Vector Database
Stores course embeddings for semantic similarity search. BGE-M3 embeddings enable multilingual course matching.
LLM Providers
Local models for fast tasks, cloud APIs for complex reasoning. Automatic fallback ensures reliability.
Relational Database
Stores all structured data — users, assessments, competencies, learning paths, and progress tracking.
Start your AI competency assessment and see how our multi-agent pipeline creates a personalized learning path in minutes.