Step 1/5

Context Gap Calculator

How big is your context gap?

Your AI needs to know what your business means. This quick calculator shows how much it doesn’t understand yet.

Identify your missing context

12 questions will show you what your AI agents can’t see.

Size your gap

Get a score for how much your agents are currently guessing.

Get your first fixes

See a simple plan focused on closing the biggest gaps first.

Takes about 3 minutes · Results shown instantly

Calculate my gap →

Step 1

Business meaning

Does your data carry the terms and concepts your business actually uses?

Glossary

Are key business terms defined consistently and available for both people and AI to understand?

Don't have

Terms are undefined or vary by team.

Somewhat have

Some important terms are defined, but coverage or adoption is inconsistent.

Have

Business terms are consistently defined, governed, and broadly accessible.

Entities

Are your important business concepts, such as Customer, Product, Order, Location, or Asset, explicitly defined?

Don't have

Business entities are not formally defined.

Somewhat have

Some entities are defined for certain domains or use cases.

Have

Core business entities are consistently defined across domains.

Relationships

Are relationships between your business entities, such as which customers buy which products at which locations, explicitly modeled and understandable?

Don't have

Relationships exist only implicitly in tables or application logic.

Somewhat have

Important relationships are documented or modeled in some areas.

Have

Relationships are systematically modeled and maintained.

Back

Press Enter

Step 1/5

Step 2

Metrics & mapping

Can an agent calculate the right number and find the data behind it?

Metrics

Are important business metrics defined once with consistent calculation logic?

Don't have

Definitions and calculations vary by team or dashboard.

Somewhat have

Some key metrics are standardized, but inconsistencies remain.

Have

Important metrics have governed definitions and reusable logic.

Dimensions

Are standard dimensions and hierarchies available for analyzing data consistently?

Don't have

Dimensions are recreated for individual reports or use cases.

Somewhat have

Common dimensions exist but are not consistently standardized.

Have

Shared dimensions and hierarchies are defined and reusable.

Mapping

Can business concepts be reliably mapped to the physical tables, columns, and data sources that represent them?

Don't have

Users must manually work out which data represents a concept.

Somewhat have

Mappings exist for some data but are incomplete or manual.

Have

Business concepts are systematically mapped to physical data.

Back

Press Enter

Step 1/5

Step 3

Governance & delivery

Is context enforced, traceable, and the same in every tool?

Access Control

Are data-access and sensitivity rules consistently enforced when data is consumed by users, applications, and AI?

Don't have

Access is handled manually or separately by each system.

Somewhat have

Access controls exist but differ across tools or interfaces.

Have

Governed access policies are enforced across every consumption method.

Lineage

Can users and AI trace important data and metrics back to their sources and transformations?

Don't have

Origins and transformations are difficult to determine.

Somewhat have

Lineage exists for some systems or critical datasets.

Have

End-to-end lineage is readily available for important data and metrics.

Multiple Interfaces

Can the same governed data and context be consumed consistently through BI, SQL, APIs, notebooks, applications, and AI?

Don't have

Data must be separately prepared for each tool.

Somewhat have

Several interfaces exist but may use different logic or datasets.

Have

The same governed data and context work across every interface.

Back

Press Enter

Step 1/5

Step 4

Metadata & AI readiness

Is your data described, trustworthy, and reusable for AI?

Descriptions

Do data assets include useful business descriptions, ownership, tags, and other metadata?

Don't have

Most data assets have little or no business metadata.

Somewhat have

Important assets have metadata, but coverage or freshness varies.

Have

Descriptions, ownership, tags, and metadata are systematically maintained.

Data Quality

Are data-quality expectations defined, monitored, and visible for important business data?

Don't have

Quality issues are mostly found after they affect users.

Somewhat have

Some datasets have checks or monitoring.

Have

Important data has quality rules, monitoring, alerts, and ownership.

Features

Are reusable derived attributes and AI/ML features available as governed data assets?

Don't have

Features are built separately for each model or application.

Somewhat have

Some reusable derived attributes or features exist.

Have

Governed, reusable features serve training, inference, and analytics.

Back

Press Enter

See my score →

Step 1/5

Almost there

Your score is ready.

Tell us where to send your full results and we'll tailor the action plan to your role.

First name

Work email

Please use your corporate email.

Company

Your role

← Back

No spam. Your results are generated instantly.

How big is your context gap?

Business meaning

Metrics & mapping

Governance & delivery

Metadata & AI readiness

Almost there

Your context gap

Close the gap on your own stack