Stratify
A Method-Enabled Analytical System for Complex Decisions
Stratify helps organizations make better decisions in complex, dynamic environments —
where uncertainty, regulation, multiple stakeholders, and conflicting perspectives are the norm.
It is not a dashboard, not a black-box AI, and not a static simulation tool.
Stratify is a method-enabled analytical system with executable structure:
it turns complex reasoning into something explicit, traceable, and reusable.
The Challenge
Most strategic and policy decisions today suffer from the same structural problems:
- Fragmented qualitative input and expert knowledge
- Implicit assumptions that cannot be inspected or revised
- Heavy reliance on opaque ML models or rigid planning tools
- High analytical effort with limited transparency
- Poor adaptability to regulatory, organizational, or contextual change
As a result, decisions are often data-rich but understanding-poor.
What Stratify does differently
Stratify builds an explicit, human-readable model of the problem landscape — one that can be inspected, updated, and reused over time.
Instead of optimizing for prediction accuracy, Stratify optimizes for governability, adaptability, and decision quality.
Compared to conventional ML and modeling approaches, Stratify:
- makes analytical models transparent, explainable, and auditable
- allows direct updates when legal, organizational, or strategic conditions change
- preserves the model structure even as content and priorities evolve
- works with minimal input data once the model is established
- supports scenario-based reasoning instead of opaque predictions
- requires no coding and no specialized ML infrastructure
- uses only anonymized expert input — no customer or personal data
- ensures full ownership of all data and analytical results
- can be self-hosted or provided as a secure service
- drastically reduces analytical effort and coordination overhead
Stratify copes with complexity without hiding it.
How Stratify works
Stratify implements a structured analytical pipeline that turns qualitative input into reusable, decision-ready structures.
Starting from qualitative sources such as documents, interviews, and expert statements, Stratify creates structured items and categories that preserve meaning and context. These are inferentially synthesized into reusable Functional Modules, which capture key system dynamics across domains. The resulting structures are connected in knowledge graphs linking domains, strategies, actors, and use cases, and are then used for scenario-based analysis of dynamic effects, trade-offs, risks, and leverage points.
The outcome is not just a set of results, but an executable understanding of the system under analysis.
Stratify produces:
- structured representations of complex problem landscapes
- reusable Functional Modules describing key system dynamics
- knowledge graphs connecting domains, actors, strategies, and use cases
- scenario comparisons highlighting risks, leverage points, and trade-offs
- transparent reports suitable for executive, expert, and governance contexts
Stratify is developed method-first, not UI-first. User interfaces are introduced later and co-designed with clients, ensuring domain specificity without compromising analytical rigor. This keeps the system adaptable, auditable, and future-proof.
All analytical results remain traceable, revisable, and reusable over time.
Typical application fields
Stratify is designed for contexts where conventional analytics reach their limits, such as:
- corporate and platform strategy
- organizational transformation and governance
- urban development and public-sector decision-making
- sustainability, CSR, and regulatory environments
- innovation ecosystems and multi-stakeholder coordination
- research-driven consulting, foresight, and policy analysis
