Our research comes from patents, journal papers and large enterprise projects, and falls into six themes. Each sits where people, team, organization and business data meet.
Combines structured data such as financial statements and management indicators with unstructured data such as meeting minutes, reports, surveys and online articles to predict company performance, then maps the results to business indicators on predefined statistical fields.
BasisUS 12,591,902 B2 · KR 10-2573799
Applies machine learning to the assessment, interview and evaluation data behind hiring, promotion and placement decisions to predict job fit and growth potential. Text such as cover letters and interview notes is part of the analysis.
BasisHyundai Motor HR Analytics ML R&D; recruitment text analytics
Defines measurable criteria through competency modeling, then statistically tests the factor structure, reliability and validity of leadership, team and personality assessments. Korean industry norm scores allow relative comparison.
BasisKJIOP 39(2), n = 5,137; Journal of Creativity and Innovation 19(3)
Designs indices for organizational culture, ways of working, safety culture and compliance sensitivity, and measures them across large multinational organizations, analyzing results by unit, level and region over time.
BasisWSI for 100,000 staff in 200 subsidiaries; safety culture for 40,000
Rather than fine-tuning models on client HR data, we structure each person's actual results, competency hierarchy and interpretation rules and give them to the AI as context. Results turn into conversational feedback and action plans.
BasisGAX architecture, AI One-Flow; AI coaching agents for Hyundai Capital and Shinsegae Group
Diagnoses the current state of people, teams, organizations and work, turns the findings into priorities and designs AI transformation plans. Also studies PM AI competency, project outcome indicators and AI project frameworks such as CPMAI.
BasisIPMA 2026, ProjMAN 2026, Procedia CS 2026, SNU AI4PM
Standard assessments run on the Global Assess platform are DAL's primary data source. They let us follow the same people and organizations over time at individual, team and organization level.
We connect psychometrics, statistical modeling, machine learning and generative AI in one analytics pipeline.
Internal (financials, KPIs, minutes, surveys, assessments) and external (articles, papers, patents, communities) data
Split into structured and unstructured data
Cleaning, tokenizing, word extraction and integer encoding turn text into structured data
Frequency-weighted vectorization, then rescaling and standard normalization
An ensemble model predicts performance and maps indicators to statistical fields
Gate 1 (4 weeks): value, owner and data are ready → Gate 2 (4 months): less work at the same quality → Gate 3 (8 months): operations and economics hold at scale