An Operating System for Governing How Work Is Done
Most organisations rely on Standard Operating Procedures to manage risk, safety, and compliance. But SOPs are documents. They describe what should happen, not what actually happens.
In practice, SOPs are applied through thousands of everyday decisions and actions.
Df2020 is an SOP Governance Operating System.
It converts standards, policies, and procedures into deterministic, executable decision systems that govern those decisions at the point of work, guiding people step by step through compliant execution and generating evidence automatically as work is done.
Different standards. Same governed execution model.
Smart Operating Procedures: The Execution Layer of Governance
Standard Operating Procedures are not applied by reading documents. They are applied through decisions and actions taken under real conditions.
Smart Operating Procedures replace static SOP documents with executable, step-by-step decision systems that govern how work is actually carried out.
They:
Guide people through what to do, when to do it, and why
Remove ambiguity at the point of decision
Ensure consistent execution across roles, sites, and conditions
Generate evidence automatically as work is executed
The result is governed execution in practice, not retrospective explanation.
Deterministic by Design
Governance cannot tolerate probability, inference, or interpretation.
Decisions that control safety, compliance, and risk must behave predictably under the same conditions, every time.
Df2020 does not use generative AI.
Its decision systems are deterministic and rule-based.
This means:
The same conditions always produce the same outcome
Every decision path is explicit, explainable, and auditable
No probabilistic behaviour or hallucinations
Human accountability remains central, supported by governed execution
Determinism is what makes governed execution possible in regulated and safety-critical environments..
The Df2020 Foundry: The Product Factory of the SOP Governance Operating System
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Standards and subject-matter expertise are authored and maintained without traditional software development, allowing governance logic to evolve as standards change.
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Decision logic is built from small, composable modules, allowing complexity to increase without creating fragile or monolithic systems.
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Risk-relevant decisions, conditions, and outcomes are explicitly defined and classified, creating a shared operational structure across products, domains, and organisations.
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Once deployed, governance logic executes deterministically in response to human interaction, guiding decisions at the point of work while accountability remains with the individual.
The Df2020 Foundry is the production system that turns standards, regulations, and expert knowledge into deployable governance products.
It does not automate workflows or digitise documents.
It captures rule-based knowledge and decision logic, converting them into deterministic, executable systems.
The Foundry enables:
Data Generated Through Execution, Not Dependency on Data
The system does not depend on historical datasets to determine behaviour, protecting governance logic from external influence, drift, or contamination.
Decision logic is defined intentionally and remains stable unless explicitly changed through governed updates. This ensures that how standards are applied in practice is always attributable to defined rules, not inferred patterns.
Product-Defined KPIs and KRIs
Each governance product defines its own Key Performance Indicators (efficiency) and Key Risk Indicators (effectiveness), derived directly from governed execution at the point of work.
This provides continuous visibility into how well procedures are being applied, where risk is emerging, and where intervention is required, without additional reporting burden.
Proof Is Continuous – Evidence-Grade Data as a By-Product of Work
As governed execution takes place, the system generates high-integrity, evidence-grade data automatically.
This data provides an attributable record of decisions, actions, and risk conditions as they occur, without additional reporting burden.
The result is a repeatable way to produce governance products that scale across domains while remaining explainable, auditable, and controllable.
