Connect to the semantic model
SeyWebDashboards is designed around direct semantic-model access so the experience starts from measures, relationships, and business logic that already govern your Power BI environment.
Turn semantic models into decision-ready dashboards.
SeyWebDashboards is Seyon Solutions' agentic framework for interactive analytics. It connects directly to Power BI semantic models, supports NL2DAX experiences, and helps teams move from trusted business logic to elegant reporting surfaces that drive action.
Semantic analytics workflow
Put natural language, DAX generation, and dashboard storytelling on top of one governed model layer.
SeyWebDashboards works best when business questions, semantic-model logic, and reporting surfaces stay connected instead of splitting into separate tools and definitions.
Start from governed Power BI semantic models instead of rebuilding KPI logic in disconnected dashboards.
Translate business questions into NL2DAX workflows while keeping measures, hierarchies, and definitions aligned.
Publish executive and operational views that stay traceable to the same DAX and model foundation.
Give analysts and stakeholders a cleaner path from exploration to action without report sprawl.
Why it matters
Dashboards become more useful when they start from the semantic model instead of another disconnected reporting layer.
Many dashboard programs slow down because business users ask questions in one place, analysts rewrite logic in another, and the final visuals drift away from the governed model. SeyWebDashboards closes that gap by working directly against the semantic model where definitions, measures, and relationships already live.
That makes it possible to combine natural-language exploration, governed DAX generation, and polished storytelling in a single decision workflow for leadership teams and front-line operators alike.
Core platform
An agentic analytics layer built around the model, not around fragments.
SeyWebDashboards brings together semantic-model access, NL2DAX acceleration, and dashboard publishing so stakeholders can move from a business question to an actionable view faster.
Direct semantic model connectivity through the Power BI external tools pattern and XMLA-based model access.
Natural language to DAX generation so business users and analysts can move from questions to governed answers faster.
Decision-ready reporting surfaces for executives, operators, and functional teams working from the same trusted metrics.
A structure that supports collaboration around semantic models, queries, and model definitions instead of disconnected report sprawl.
How it works
From business question to dashboard narrative.
Translate natural language into governed DAX
The framework turns analyst and stakeholder questions into NL2DAX workflows, helping teams move from intent to validated queries without losing control of definitions.
Publish dashboards that tell the story
The result is an interactive reporting layer with near real-time metrics, layered storytelling, and views that simplify decisions for executive and operational audiences.
Reference pattern
Powered by proven semantic-model and project workflows.
The SeyWebDashboards positioning is informed by the Power BI semantic model assistant pattern and by Microsoft's project-based semantic model guidance.
Grounded in working semantic models
The referenced Power BI Chat Semantic Model project demonstrates a practical pattern: connect to a live semantic model, inspect measures and metadata, and execute DAX from a guided interface instead of treating analytics as a black box.
Aligned to Microsoft project semantics
Microsoft documents how semantic model projects can be stored with PBIP and TMDL so teams can collaborate on readable model definitions, DAX query assets, and source-controlled analytics changes.
Built for governed iteration
That combination gives SeyWebDashboards a strong delivery pattern: agentic assistance on top of a governed model layer, with collaboration workflows that fit enterprise teams.
Microsoft + GitHub sources
The supporting details are visible, readable, and source-control friendly.
Microsoft's documentation shows how Power BI project semantic models can be managed through PBIP and TMDL structures, including model definitions, DAX query files, and collaboration-friendly project folders. That is the kind of governed model foundation SeyWebDashboards is meant to amplify.
The referenced GitHub project shows the complementary interaction pattern: connect to a live semantic model, inspect metadata and sample data, and let an AI-powered interface help generate and execute DAX against the model.
Microsoft Learn: Power BI Desktop project semantic model folder
Reference for PBIP semantic model structure, TMDL folders, DAX query files, and source-control-friendly collaboration.
Open resource
Reference for PBIP semantic model structure, TMDL folders, DAX query files, and source-control-friendly collaboration.
Open resource
GitHub: markusbegerow/powerbi-chat-semantic-model
Reference implementation showing direct semantic model access, DAX execution, metadata visibility, and external-tool integration patterns.
Open resource
Reference implementation showing direct semantic model access, DAX execution, metadata visibility, and external-tool integration patterns.
Open resource
Next step
Use SeyWebDashboards when you need natural-language analytics without giving up semantic-model discipline.
It is suited for organizations that want governed self-service exploration, executive dashboard storytelling, and operational analytics views built on top of the same semantic model foundation.