Semantti turns the logs, events and transactional data your organization already produces into an explainable Semantic Model of its systems, processes, dependencies and behaviour.
Next come applications for technology, operations, business performance and management decision-making.
Semantti continuously learns the structure and meaning from operational data. The result is a shared Semantic Model that different applications can use to ask different questions of the same organization.
No manually maintained architecture model is required as the starting point.
The same approach can learn across different systems, schemas and information sources.
Intermediate models are inspectable and usable by both humans and AI agents.
Reveal unexpected behaviour, hidden dependencies, inefficiencies and optimization opportunities — with minimal manual configuration or upfront modelling.
Semantti for Cyber applies the Semantti Semantic Model to cybersecurity assessment, continuous monitoring, investigation and DFIR — turning existing telemetry into explainable understanding and action.
Turn existing telemetry into an evidence-based understanding of your environment, risks, dependencies and visibility gaps.
Continuously learns your environment and detects activity or relationships that do not make sense — with clear, explainable findings.
Built for investigation and DFIR workflows: reconstruct activity and timelines, gather relevant evidence and turn complex telemetry into explainable, actionable conclusions.
In a recent real-world assessment, heterogeneous operational and security telemetry was enough to reconstruct a meaningful Semantic Model of the environment without starting from a manually maintained architecture description.
The evidence already existed.
Semantti turned it into understanding.
Semantti combines existing algorithmic information theory methods with new proprietary strategies and heuristics to learn structure and meaning from messy real-world data.
Instead of asking an organization to fully describe its environment in advance, Semantti learns from the signals the environment itself produces.
The Semantic Model becomes an explainable context layer for analytics, expert workflows and autonomous agents.
Different applications can reason over the same organizational understanding rather than rebuilding context for every use case.