# Context & Catalogue Awareness — How Fovea Understands Your Data

Fovea's intelligence starts with your data catalogue. Before answering any question, it reads your schema, business glossary, metadata classifications, and governance rules. That's what makes every AI interaction accurate, contextual, and trusted.

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### Schema & Structural Awareness

Fovea automatically reads the schema of every connected data source: tables, columns, data types, primary keys, and foreign key relationships. It builds an internal map of your entire data estate so AI-generated queries are structurally correct from the first attempt.

### Business Glossary & Semantic Layer

Business users think in terms of 'revenue', 'churn', or 'active customers'. Not column names. Fovea maps these business terms to the underlying technical fields, so natural language questions resolve to the right data every time, regardless of how your columns are named.

### Column-Level Metadata & Tagging

Each column in your catalogue can carry rich metadata: descriptions, sensitivity classifications, ownership, and quality scores. Fovea reads this metadata before generating any query, ensuring it never exposes PII fields or queries deprecated columns.

### Data Lineage Awareness

Fovea understands where data comes from. When you ask a question about a derived metric or transformed column, Fovea traces it back to its source, letting you trust the answer and audit the logic behind it.

### Multi-Turn Context Memory

Every conversation with Fovea builds on the last. When you say 'break it down by region' after asking about revenue, Fovea knows exactly which dataset, filters, and time window you mean. No need to repeat yourself.

### Semantic Search Across Data Assets

Fovea goes beyond keyword matching. It uses semantic understanding to navigate assets by meaning, relevance, lineage, and intent, even when naming conventions don't match your terminology.

### Live Catalogue Synchronisation

Schemas evolve, definitions drift, and new assets appear. Fovea monitors changes and surfaces update opportunities automatically, keeping your catalogue current without manual cleanup.

### Cross-Source Catalogue Federation

Infoveave connects to databases, data warehouses, flat files, APIs, and streaming sources. The catalogue federates all of these into a single unified view, so Fovea can answer questions that span multiple systems without you having to know where the data lives.

Agentic AI Documentation

## AI-Generated Documentation, Instantly

Documenting data assets has long been tedious, inconsistent, and time-intensive. Fovea analyses metadata, usage patterns, schema structures, and contextual relationships to generate documentation that's both accurate and meaningful. It shifts catalogue creation from a manual task into an intelligent, assisted process.

### Replace Description with AI

Generate a fresh, business-friendly description for any data asset: table, column, KPI, or dataset. Fovea analyses schema, usage context, and relationships to produce documentation that's accurate and immediately useful.

* Generates descriptions for tables, columns, KPIs, and datasets
* Business-friendly language, not just technical field names
* Analyses schema structure and usage patterns
* Instant output, no manual authoring required

### Enhance Current Description with AI

Already have documentation? Fovea refines it. Choose this mode to improve existing metadata: filling gaps, sharpening definitions, correcting ambiguities, and bringing inconsistent documentation up to a consistent standard across your data estate.

* Refines and improves existing documentation
* Fills gaps and corrects ambiguities
* Improves consistency across all assets
* Builds on your existing context rather than replacing it

## Agentic Metadata Enrichment

Fovea's agents continuously enrich catalogue context, so the AI always has accurate, complete metadata to work with.

Auto-generate missing documentation

Agents identify undocumented assets and generate descriptions and annotations without waiting for manual input.

Suggest business definitions

Technical field names get mapped to plain-language business terms so every user can understand what the data means.

Identify related assets and relationships

Agents surface semantic connections between datasets, columns, and business concepts to enrich the catalogue graph.

Recommend tags, classifications, and ownership

Sensitivity labels, domain classifications, and stewardship assignments are suggested intelligently based on content and context.

Surface inconsistencies and gaps

Incomplete metadata, conflicting definitions, and stale documentation are flagged for review before they cause problems downstream.

Governance Integration

## Catalogues That Help Govern Themselves

Cataloguing and governance are increasingly intertwined. Fovea doesn't just record governed data. It makes the catalogue an active participant in governance. Agents identify undocumented assets, flag metadata anomalies, suggest policy classifications, and support stewardship workflows through guided recommendations. Every AI interaction is bounded by the rules you set.

### Governance-Aware Queries

Fovea respects row-level security, column masking, and access policies defined in your governance layer. Catalogue metadata tells it which data a user is permitted to see, and it never generates a query that bypasses those rules.

### Certified & Trusted Datasets

Data assets in the catalogue can be marked as certified. Fovea prioritises certified datasets over uncertified ones when resolving ambiguous queries, so business users always get answers from trusted data.

### Deprecation & Lifecycle Management

When a column or table is deprecated in the catalogue, Fovea stops using it automatically and guides users toward the recommended replacement. No stale queries. No outdated answers.

Connected Capability

### Every Chat Answer Starts with the [Catalogue](/platform/fovea-agentic-ai/conversational-insights)

Before answering any chat question, Fovea reads your schema, business glossary, lineage, and governance rules. That context is what turns a plain-English question into an accurate, trusted response. Not a generic AI guess.

[Explore Conversational Insights](/platform/fovea-agentic-ai/conversational-insights)

Connected Capability

### The [Catalogue](/platform/fovea-agentic-ai/context-catalogue) Is What Makes the AI Query Builder Accurate

When you ask "Show top customers by revenue this quarter", Fovea doesn't guess which table holds revenue or which column maps to customers. It looks it up in the catalogue, finds the right schema, applies your business glossary, checks governance rules, and generates a query that runs correctly the first time.

[Explore AI Query Builder](/platform/fovea-agentic-ai/ai-query-builder)

See how Fovea's catalogue awareness transforms data questions into trusted answers.

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## Frequently Asked Questions

What is a data catalogue and why does Fovea need one?

A data catalogue is a structured inventory of your data assets: tables, columns, relationships, business definitions, and metadata. Fovea uses it to understand what your data means, not just what it contains. Without it, AI-generated queries would be generic guesses. With it, every answer is grounded in your specific data model and business context.

Does Fovea automatically build the catalogue, or do I need to set it up manually?

Fovea automatically reads schema information from connected data sources on connection. You can enrich it with business glossary terms, column descriptions, sensitivity tags, and ownership metadata. The structural layer (tables, columns, relationships) requires no manual setup.

How does Fovea handle business terms like 'revenue' that map to different columns in different systems?

The business glossary layer lets you define canonical terms and map them to the correct columns per data source. When a user asks about 'revenue', Fovea resolves it to the right field in the right table, even when different systems use different column names for the same concept.

Can Fovea access sensitive or restricted data columns?

No. Fovea respects column-level sensitivity classifications and governance policies defined in the catalogue. If a column is marked as PII or restricted, Fovea will not include it in generated queries or surface it in answers — enforcing your access rules automatically.

What happens when the schema changes — does the catalogue stay up to date?

Yes. Fovea monitors connected sources for schema changes and refreshes the catalogue automatically. New tables, added columns, or modified data types are picked up without manual intervention, keeping the AI's knowledge of your data estate current.

How does catalogue awareness improve the AI Query Builder?

The catalogue is what makes the AI Query Builder reliable. When you describe a query in plain English, Fovea looks up the schema to find the exact tables and columns, checks the business glossary to resolve terminology, applies governance rules to filter out restricted fields, and then generates SQL that runs correctly the first time.

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