What is Nash AI?

Nash AI is Astrato's built-in AI assistant. It works directly inside the platform so you don't need to switch tools or export data to get help. Nash understands your semantic model, your data, and what you're trying to build.

Nash is available in the following places:

Workbook Editor
Semantic Layer

Each brings a different kind of help, tuned to what you're doing right now.

Powered by your semantic model

Nash knows your business. Every suggestion, every chart, every measure description it produces is grounded in the data model you have already defined. That means it stays accurate and stays in sync.

Nash in the Workbook Editor

Building a dashboard used to mean starting from a blank canvas and working through every chart one by one. Nash changes that. Open the AI panel in any workbook and tell Nash what you need. It reads your semantic model, asks a couple of focused questions, then builds the whole thing.

What Nash Can Do in the Workbook Editor

Capability	Detail
Generate dashboards from scratch	Ask Nash to build a dashboard and it handles layout, charts, filters, and KPI cards based on your semantic model
Guided intent questions	Nash asks up to two focused questions to understand your goal before it builds
Transparent step-by-step plan	Every action is listed in the panel so you can see exactly what Nash is doing and why
Update existing dashboards	Ask Nash to change charts, measures, formatting, or layout on any existing workbook
Add new chart types	Request specific visuals like Sankey diagrams or heatmaps by name and Nash places and configures them
Cross-visual formatting changes	Ask Nash to update formatting (e.g., currency) and it applies the change across all relevant components at once
Semantic model awareness	Nash reads your live semantic model and suggests appropriate measures and dimensions for every visual
Context-aware relayout	Ask Nash to reorganise the dashboard and it rearranges components to accommodate new additions

Opening Nash

Click the AI icon at the bottom of the left toolbar to open the Nash panel. It slides in alongside your canvas. You can keep it open while you work.

Nash AI panel open in the Workbook Editor, ready to receive a prompt

Creating a Dashboard from Scratch

Type your goal in plain language. Nash uses your semantic model to understand what data is available, then it asks two short questions to make sure it builds the right thing. It recommends a focus area and shows you the options so you stay in control.

Nash analyses the semantic model and recommends a dashboard focus, with selectable options

Once you confirm your answers, Nash builds a full execution plan and begins placing components. You can watch each step run in the side panel. Filters, KPI cards, trend lines, and detail tables appear on the canvas as Nash works through them.

Nash builds a filter component as part of its step-by-step plan

A full dashboard taking shape: KPI cards, trend chart, category bar, and detail table

The completed plan shown alongside the finished dashboard, including filters and all visuals

From blank canvas to full dashboard in under 3 minutes

The example above, using the Bike Sales model, produced a sales performance dashboard with 5 filters, 4 KPI cards, 2 charts, and a detail table without a single drag-and-drop.

Updating an Existing Dashboard

Nash is not only for new builds. On an existing dashboard you can ask it to change chart types, swap measures, update formatting, or add entirely new components. Type what you want changed and Nash handles the rest.

Example prompts that work today:

Please update $ into EUR, we're a European business
Add a Sankey diagram for category flow and a heatmap for gross profit
Relayout the dashboard and populate the new charts with suggested dimensions and measures

Nash updating currency formatting across all visuals in a single action

Nash adding a heatmap after a natural-language request, alongside existing charts

Nash building a Sankey diagram and heatmap in parallel, with a clear step-by-step plan

Final dashboard with Sankey and heatmap added, data mapped and layout adjusted

Nash understands context

When you ask Nash to add a Sankey diagram, it does not ask which data to use. It reads the existing model, picks appropriate dimensions, and builds the chart. You can always adjust after, but most of the time you won't need to.

Nash in the Semantic Layer

The semantic layer is where data models live. It is also where a lot of time gets lost. Understanding a new model, checking what measures exist, spotting gaps, knowing which tables are connected and how. Nash in the semantic layer makes all of that fast.

Open Nash inside the semantic layer and you have a conversational interface on top of your data model. Ask it anything about the model and get a clear, structured answer.

What Nash Can Do in the Semantic Layer

Capability	Detail
Model description	Full structured summary of tables, joins, dimensions, measures, and relationships in plain language
Relationship mapping	Nash reads every join and explains how tables connect, including where the join web is incomplete
Measure catalogue	Asks Nash to list all measures with their formula, source table, and what the metric means
Deep measure detail	Ask for full detail on any measure or set of measures and Nash explains the logic, filters, and time intelligence applied
Quick observations	Nash flags potential issues, such as duplicate tables, non-standard aggregations, or incomplete joins
Conversational follow-up	Ask follow-up questions in plain language. Nash keeps context across the conversation
Model health check	Nash assesses overall model completeness and suggests where further work might be needed
AI suggestions	Measure Suggestions, Dimension Suggestions, Field Alias Suggestions, and Join Suggestions available from the model canvas

Nash panel open in the Semantic Layer, with the full data model visible alongside

Describe My Semantic Layer

Click "Describe my semantic layer" and Nash reads the entire model. It produces a structured summary covering tables, joins, relationships, dimensions, measures, and observations about the model's health and completeness.

Nash summary showing table groups, key relationships, and full measure catalogue

The description is not just a list. Nash reads the relationships and highlights things worth knowing, such as which tables connect to which, where the join web has gaps, and which measures use time intelligence.

Nash surface quick observations and recommendations, including a suggestion for further queries

Onboarding made fast

A new team member or analyst joining a project can ask Nash to describe the model and have a working understanding of the data structure in under a minute. No documentation required.

Deep Measure Detail

Ask Nash to explain a specific measure or give you detail on all measures. It breaks down the formula, the source table, the logic, and flags anything worth knowing.

Nash listing measures with formulas, source tables, and key observations about each

Nash also surfaces patterns across the measure set, grouping them by type: revenue metrics, product metrics, order metrics, time intelligence measures, and more. This makes it easy to spot what you have and what might be missing.

Advanced Use Cases

Nash AI is designed to handle more than day-to-day dashboard and model work. The following use cases show how Nash can support larger data modernisation, governance, and migration initiatives across an enterprise.

Cross-Platform BI Migration and Logic Translation

Moving from one BI platform to another is rarely straightforward. Years of logic built in Qlik Set Analysis, Power BI DAX measures, Tableau calculations, SQL business logic, or Excel-based reporting do not translate automatically. Teams are often left rebuilding from scratch, which is slow and introduces inconsistency.

Nash AI can read & migrate existing BI logic and translate it into reusable, governed semantic assets inside Astrato. Instead of manually recreating every measure and calculation, teams can describe or paste existing logic and Nash will produce the equivalent semantic definition. Those definitions become part of the governed layer, not buried in a single workbook.

This accelerates migration and modernisation initiatives while preserving the trusted business definitions teams have built over years.

Example prompts:

“Translate this DAX measure into a semantic metric”
“Here is a Qlik Set Analysis expression. What is the equivalent in Astrato?”
“Convert this Excel formula into a governed calculation”

Looker's LookML

Qlik Set Analysis

PowerBI DAX

measure: ticket_count {
  type: count
  drill_fields: [id]
}

measure: share_of_tickets_by_customer {
  type: percent_of_total
  sql: ${ticket_count} ;;
}

Count({<CUSTOMER=>} ID)
/
Count({<CUSTOMER=> TOTAL ID)

Share of Tickets by Customer =
DIVIDE(
    [Ticket Count],
    CALCULATE(
        [Ticket Count],
        ALL('Tickets'[Customer])
    )
)

Example of Nash creating measures from Power BI's Dash Expressions.

Turn Warehouse Tables Into Governed Business Models

Raw cloud warehouse schemas rarely arrive ready for business use. Tables need to be understood, relationships need to be defined, and measures need to be created before analysts can start answering questions.

Nash AI can analyse enterprise warehouse schemas and help generate the semantic foundations needed for business-ready analytics. This includes:

Dimensions - what business entities are represented
Metrics - what the business wants to measure
Relationships - how tables connect to each other
Hierarchies - how data rolls up (region > country > city, for example)
Drill paths - the ability to click into a number and see what is behind it
Aliases - cleaner, business-friendly names for technical field names
Reusable calculations - common expressions defined once and shared across the model

This dramatically reduces the time required to move from raw cloud warehouse structures into business-ready analytics systems.

Continuously Improve Semantic Consistency

As semantic layers grow over time, they accumulate inconsistencies. Duplicated metrics appear with slightly different definitions. KPIs are named differently across teams. Business logic fragments across multiple models. Standards drift.

Nash AI helps identify and fix these issues before they cause problems downstream. It detects overlapping logic, recommends reusable KPI structures, and highlights opportunities for standardisation. Teams can use Nash to run a consistency review across the full semantic layer and surface exactly where definitions conflict or overlap.

This is particularly valuable at scale, where a manual review of hundreds of measures and dimensions would take weeks.

Business Metric Creation in Natural Language

Not everyone who needs to define a metric knows SQL or BI-specific calculation languages. Business users and analysts often have a clear idea of what they want to measure but need a technical resource to translate it into something the platform can use.

Nash AI bridges this gap. Users can define governed metrics using plain language and Nash will produce the semantic definition. The result is a reusable, consistent measure that lives in the governed layer, not in a one-off workbook formula.

Example prompts:

“Create a churn rate metric”
“Define active customers as customers purchasing within the last 90 days”
“Create gross margin by product category”

Nash translates business intent into reusable semantic definitions. This means metric creation becomes a conversation, not a development ticket.

Explain and Discover Enterprise Data

Enterprise data environments are large, complex, and often poorly documented. New team members spend days trying to understand what data is available, how it is structured, and which metrics are trusted. Even experienced analysts waste time navigating schemas they rarely use.

Nash AI makes enterprise data navigable for everyone. Ask Nash about the data model in plain language and get clear, structured answers about:

Data models and how they are structured
Relationships between tables and entities
Trusted metrics and what they measure
Semantic definitions and how fields are defined
Available business entities and what each one represents

Example prompts:

“What tables contain customer lifecycle data?”
“Which metrics are finance-approved?”
“What is the difference between bookings and revenue?”

This reduces onboarding friction, speeds up self-service analytics, and makes enterprise data more accessible across the organisation.

Prompts That Work Well

Nash works best when you are direct about what you want. You do not need special syntax. Write it the way you would explain it to a colleague.

Workbook Editor Prompts

Build a dashboard based on the current semantic model, with the most important KPIs and filters at the top
Add a Sankey diagram and a heatmap, relayout the dashboard, and populate with suggested dimensions and measures
Please update $ into EUR, we're a European business
Show me sales performance by region over time
Create an executive summary sheet with a revenue trend and top 10 customers

Semantic Layer Prompts

Describe my semantic layer
Give me an overview of all measures with all necessary detail
Which tables are connected to Orders and how?
What time intelligence measures do I have?
Are there any gaps in my join web?
Explain the Revenue MTD measure

Use follow-up questions

Nash keeps context within a session. After it describes the model, ask "what measures are missing for a sales pipeline view?" and Nash will answer in context. You do not need to re-explain the model.