Visokio · Product history
One product idea, expanded over two decades.
Omniscope has changed substantially since its first release in 2005, but its direction has remained recognisable: let people work across the complete data problem in one coherent environment.
Visokio is founded
The independent company begins in London. The early ambition is already broader than producing static charts: make large and complex datasets something people can explore and understand directly. See the current company history ↗
Omniscope 1.0
The first release brings multiple coordinated visualisations into one desktop application. Selections in one view flow through the others, turning data exploration into an interactive process. See the archived official history ↗
Classic grows beyond visualisation
Omniscope Classic develops into an integrated environment for data import, transformation, analysis, scripting, interactive reporting and browser publishing. The product establishes the principle that data preparation and presentation should remain connected.
The platform is rebuilt for the web
A multi-year engineering programme creates a new generation of Omniscope. Its web-based report architecture, workflow system and extension model provide the foundation for server deployment, collaboration and a much wider class of data applications.
Workflows and reports become one system
Parameters, localisation, JSON and XML handling, improved workflow and report design, and plans for custom Python and R blocks make the new architecture increasingly extensible.
Projects become operational services
Working Copies separate editing from live production. The Workflow Execution REST API and scheduler let other systems run parameterised workflows. Multi-tenancy, data editing and external storage support move Omniscope beyond interactive analysis into repeatable operations.
Governed projects and isolated extensions
OIDC authentication, publishing controls, project templates and performance work make deployment more systematic. Custom blocks can run Python and R in isolated Docker environments, combining no-code assembly with specialist code where it adds value.
The data-application platform becomes explicit
Mobile reports, streaming and capture, richer connectors, profiling and reusable blocks support a clear proposition: build complete data web applications by combining ETL, analytics, visualisation, automation and optional Python, R or JavaScript in one product.
From deployed project to managed product
Improved data editing, templates, automated diagnostics, publishing, revision history, release management, staging-to-production synchronisation and multi-project scheduling support longer-lived operational solutions. The first AI integrations arrive inside that established platform.
Models start operating real platform tools
Report Ninja, Instant Dashboard and Data Q&A let models construct reports and analyses using visible Omniscope operations. Bring-your-own-model support, private models, white-label AI applications, schema resilience and DataOps work broaden where the platform can be used.
Verifiable, model-neutral AI
Insight Explorer, Workflow Ninja, AI Request and AI Insights expand the model’s reach across exploration, preparation and reporting. Support for multiple providers and local models keeps deployment flexible. Visible transformations, validation and human review keep the answer grounded in a deterministic platform.
The thread that connects the releases
Each generation has reduced a boundary. Coordinated views reduced the gap between datasets and visual understanding. Workflows joined preparation to analysis. Publishing and APIs joined projects to operations. Data applications joined analytical logic to a focused user experience. AI now offers a new interface to the same platform.
That is why Omniscope’s current AI work cannot be understood in isolation. A model is useful because it can operate years of accumulated analytical, visual and operational capability. Verification is possible because the platform already knows how to represent that work visibly.
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