Scale and scope

5,000+
Decision Episodes
Covering carve-outs, corporate turnarounds, market-entries, CEO succession and many more
1,390+
Organizations
Public, Private, Family-Owned, from mid-cap to Fortune 500
27
Sectors
Covering major sectors such as Technology, Energy, Manufacturing and Media, and also government institutions and NGOs
240+
Years (1784–2026)
Enabling cross-era validation intra- and intercompany
R² = 0.83–0.94
Predictive Power
Outcome prediction accuracy
152,600+
Individual Dimension Scores
5 independent rater models applying the same scoring rubric
58,600+
Source Documents Processed
Inputs from pre-validated quality sources, including 10-K, Official Earnings Transcripts, premium journalism coverage (e.g. FT, Bloomberg, WSJ) and Academia
285
Unique Signal Types Extracted
Across 49 distinct extraction categories
5
Continents
NA 70%, EU 16%, APAC 9%, Global/Other 5%
κ = 0.81
Inter-Rater Reliability
Consensus between 5 rating models, indicating very high agreement

Multi-sector coverage

Technology, financial services, healthcare, energy, consumer, industrials, media, telecommunications, professional sports, and 18 additional sectors. Each case scored independently across all six dimensions.

Source document taxonomy

Board minutes, ExCo records, strategy memos, due diligence reports (FDD, CDD, ODD), management presentations, initiative tracking logs, operating plans, governance frameworks, risk registers, and investor communications. Average 7.3 unique source documents per scored episode.

Signal extraction depth

285 unique signal types across 49 distinct extraction categories: dimension polarity signals (density, velocity, vitality, MDC, selection, execution), rhetoric markers (hedge, passive, promissory, complexity), absence signals, episode-derived metrics, and calibration indicators. Each signal mapped to one or more of the six scoring dimensions.

Scoring granularity

Each episode generates 6 dimension scores (1–10), 18 sub-dimension indicators, confidence intervals per dimension, and composite MR/DA/Performance indices. Total: 42 unique data elements per scored episode, 213,600+ data elements across the repository.

Longitudinal depth

Cases span from 1784 to 2026, enabling cross-era validation. The scoring methodology produces consistent results across regulatory regimes, technology cycles, and market structures.

Independent validation

Each episode scored by 5 independent rating models applying an identical rubric. Inter-rater reliability (Cohen’s κ = 0.81) measured and reported. Consensus scores derived from calibrated aggregation with outlier exclusion, not averaging.

Geographic distribution

North America (70%), Europe (16%), Asia-Pacific (9%), Global/Other (5%). Coverage includes both developed and emerging market organisations, from <€500M to >€200B in enterprise value.

Decision type classification

Episodes classified across 11 decision types: M&A integration, organic expansion, restructuring, leadership transition, market entry, product launch, capital allocation, governance redesign, digital transformation, crisis response, and portfolio rebalancing.

How the diagnostic works

01

Data Input

Board packs, strategy memos, ExCo records, due diligence reports, operating plans, governance frameworks, initiative tracking logs.

02

DecisionDNA Engine

Signal extraction across 285 signal types, pulse calibration, 5-model consensus scoring across all six dimensions against the 5,000+ case repository.

03

Key Artefacts

Archetype classification, load absorption capacity, forward degradation signals, intervention window and timing.

Existing measurement blindspots

Current due diligence and assessment methods each have structural blindspots. DecisionDNA measures what they cannot.

Assessment Type What It Measures Structural Blindspot
Financial DD Historical financial performance, accounting quality Cannot predict whether the organization can execute forward plans
Commercial DD Market position, competitive dynamics, customer base Cannot assess whether the organization can digest market shifts
Operational & Tech DD Systems, processes, technical debt Cannot measure coordination capacity or decision throughput
Management Assessment Individual leadership capability Cannot measure the system’s metabolic capacity independent of individuals
Readiness & Culture Employee sentiment, cultural alignment Cannot quantify decision velocity, selection accuracy, or metabolic load limits

See it on your own organization

The alpha plugin lets you run the diagnostic locally. No document content leaves your machine.

Try the Alpha How It Works