The modern enterprise has fallen in love with AI. Billions are spent each year on generative models, machine-learning pipelines and AI-driven analytics. Yet most organisations treat the results these systems produce as disposable. A customer email yields a sentiment score, a support call is classified, a contract is entity-tagged—and the output disappears into log files or transient tables, never tracked, audited or reused.

That is more than a missed opportunity; it is an architectural gap. AI outputs are data. Like any enterprise data they need durable storage, lineage, versioning and governance. The remedy is to recognise AI as a data source in its own right. When the model feeds its insights back into your Data Vault, you create a bidirectional flow where every prediction becomes a first-class, governed asset.

This article walks through how to build that loop with beVault, integrating AI outputs as satellites in your Data Vault and extracting far greater value from your AI investments.

Today’s enterprises sit on vast repositories of unstructured data like PDFs, images, videos, audio recordings, and text documents. While Data Vault architectures traditionally focus on structured data, there’s a well-established pattern for handling these files: store the physical files in object storage systems (Amazon S3, Azure Blob Storage) and maintain metadata plus file references in your Data Vault. This approach balances efficient storage with proper governance and lineage tracking.

The breakthrough comes when you add AI to this equation. Instead of just storing metadata, AI can extract structured, actionable insights from the content itself. Scanned contracts become structured databases of terms and parties. Support call transcripts become sentiment datasets with identified issues. Product images become catalogues with automatically detected attributes.

Common AI extraction use cases include:

• Entity extraction from documents (names, dates, amounts)
• Sentiment analysis from text or audio
• Object detection and classification in images
• Document categorization by type or urgency
• Pattern detection across large text collections

But here’s the challenge: AI models naturally generate conversational text responses. Ask an AI to analyze a document, and you’ll get a paragraph. It’s perfect for humans but useless for systematic data integration. The solution requires two elements: prompt engineering that forces AI to output structured JSON, and Data Vault modeling that treats those outputs as new data sources.

The principle is simple: an insight produced by AI is a business event, so handle it with the same rigour you apply to data from your ERP or CRM. When the model extracts information, record that fact, historise it and govern it.

This mindset unlocks four capabilities most AI roll-outs overlook:

• Auditability – Each AI output is stored with full context: timestamp, model version, prompt, confidence score. Questions later? The evidence is there.
• Versioning – Persist results from successive model versions side-by-side and measure which performs best on identical data sets.
• Reprocessing – Keep original file references and prompts so you can re-run historical data through updated models whenever needed; insights improve as your models do.
• Governance – AI outputs pass through the same quality checks, access controls and business rules as any other data. Set confidence thresholds, flag low-quality extractions, apply role-based security.

Data Vault 2.0 fits this pattern naturally. Automatic historisation tracks how AI outputs evolve. Source-system tracking lets you treat Anthropic, OpenAI or Azure AI like any other provider, preserving lineage. And the model’s flexibility means new AI attributes slot into fresh satellites without disturbing what’s already in place.

Let’s walk through the complete technical implementation of integrating AI outputs into beVault. This isn’t theoretical, it’s the exact process you’ll follow to make this work in your environment.

Step 1: Establish your foundation
Begin with your current beVault landscape. Your Raw Vault and Business Vault are already ingesting structured sources; now look for unstructured assets that AI could enrich.

Maybe you have a hub for customers with structured data from your CRM, but you also have thousands of customer support emails sitting in file storage. AI could extract sentiment, identify mentioned products, and detect customer issues, valuable attributes that could become new satellites on your customer hub.

Step 2: Create the AI consumption layer
With beVault’s Distribute module, create an Information Mart specifically designed for AI consumption. This mart serves as the AI’s input, it should contain everything the AI needs to process your data effectively.

For our example, your Information Mart might include:

• Customer business keys and identifiers
• File references (S3 paths) to support emails or call transcripts
• Contextual information (customer tier, product owned, account age)
• Metadata about the files (date received, channel, length)

Structure this mart thoughtfully. Label columns clearly, your AI will perform better with well-named fields. Keep individual data payloads reasonable, if you’re processing documents, you might batch them in groups rather than sending thousands of file references in a single API call. Consider performance: this mart will be queried by your orchestration layer, so proper indexing on key columns is important.

Step 3: Engineer your prompts for structured output
This is the critical success factor that most AI implementations get wrong. Out of the box, AI models are conversational, they want to give you thoughtful, nuanced, human-readable responses. But you need machine-readable, structured output that can be programmatically inserted into database tables.

The solution is explicit prompt engineering that forces the AI to respond in JSON format with a well-defined schema. Here’s a real-world example we use at dFakto for extracting destination names from conversations :

Notice the key elements that make this prompt effective:

• Clear role definition: The AI understands it’s analyzing destination management conversations, giving it context for ambiguous terms.
• Explicit format specification: “Return ONLY a JSON array” leaves no room for conversational responses.
• Schema definition: The AI knows exactly what structure to produce, an array of strings.
• Few-shot examples: Three examples show the AI exactly what you expect, including edge cases like no destinations found.
• Constraints: Repeated emphasis on “ONLY the JSON array, nothing else” prevents the AI from adding explanations or commentary.
• Clear placeholder: The prompt indicates where dynamic content will be inserted, making it easy to template.

When crafting your own prompts, invest time in testing and iteration. Start with a small dataset, examine the outputs, and refine your prompt until you get consistent, parseable JSON. This upfront investment pays enormous dividends, a well-engineered prompt produces reliable data; a poorly crafted one produces garbage that breaks your integration pipeline.

Step 4: Orchestrate with beVault’s States
beVault’s orchestration engine, States, handles the integration between your Data Vault and AI provider through a configuration-driven approach.

States manages the following workflow:

1. Queries your Information Mart: States pulls the data you prepared using standard SQL queries.
2. Constructs the API call: It packages your data into your prompt template, replacing placeholders with actual values, and prepares the API request to your AI provider (Anthropic’s Claude, OpenAI’s GPT models, Azure AI, etc.).
3. Executes the AI call: States handles the actual API interaction, sending your prompt and receiving the AI’s JSON response.
4. Stores the result: The JSON response is automatically written to your designated staging area in beVault, ready for modeling.

Configuration in States includes:

• API credentials: Securely storing your AI provider’s API keys
• Error handling: Retry logic, logging, and alert mechanisms for failed API calls
• Rate limiting: Respecting your AI provider’s API limits to avoid throttling
• Batch processing: Processing records in groups for efficiency

States centralizes orchestration logic in a maintainable, configuration-based approach rather than requiring custom scripts, API management code, and integration pipelines maintained by development teams.

Step 5: Model the AI output as a source
In beVault’s Source module, add a new source for your AI provider (e.g. “Anthropic_Claude” or “OpenAI_GPT-4”) to define how the JSON produced in Step 4 enters your Data Vault.

Use a two-table approach:

Table 1: Raw Staging Table

This table stores the complete JSON response from the AI exactly as received. Include columns like:

• request_id: Unique identifier for this AI call
• timestamp: When the AI response was received
• model_version: Which AI model produced this output
• prompt_version: Which version of your prompt was used
• input_reference: Reference back to the Information Mart record
• raw_json: The complete JSON response
• api_latency: How long the API call took
• api_cost: Estimated cost of this call

Preserve everything. This raw table serves as your audit trail and enables reprocessing if you later change your parsing logic.

Table 2: Staging View

Create a view that parses the JSON and pivots it into a relational format ready for Data Vault modeling. If your AI extracted destinations as a JSON array, your view might unnest that array into rows. If it extracted multiple fields (sentiment score, confidence, extracted entities), your view presents those as columns.

For example, parsing our destinations JSON:

Now map these parsed outputs to Data Vault structures to integrate the AI output into your data model.

Always include AI-specific metadata as satellite attributes: model version, confidence scores, prompt version, and processing timestamp. This metadata is crucial for understanding and trusting your AI-generated data.

Step 6: Close the loop, use AI-enriched data
The final step is bringing your AI insights into your analytical Information Marts. In the Distribute module, create or update Information Marts that combine traditional structured data with your new AI-extracted insights.

For example, a customer analytics mart might now include:

• Traditional demographic and transactional data from your CRM and billing systems
• AI-generated sentiment scores from support interactions
• AI-extracted topics and pain points from customer emails
• AI-classified customer tier predictions based on communication patterns

This creates a unified analytical environment where AI insights have the same reliability, accessibility, and governance as any other data. Business users querying your Information Marts don’t need to know or care that some attributes came from AI, they just see complete, enriched data ready for analysis, reporting, and decision-making.

Follow these guidelines to integrate AI output smoothly and cost-effectively.

Prompt versioning
Treat prompts like code. Any tweak—extra examples, new instructions, altered schema—changes the extraction logic, so store each prompt as metadata in your Data Vault and version it explicitly.

Cost management
API usage can add up. Control spend with a staged approach:

1. Start with a subset: Pick 10–50 representative records, including edge cases.
2. Use smaller models first: Test with Claude Haiku or GPT-3.5 Turbo; they’re far cheaper per call.
3. Evaluate quality: Check accuracy, confidence and edge-case handling.
4. Move up only if needed: If accuracy falls short, try Claude Sonnet or GPT-4/5; reserve premium models (Claude Opus, GPT-5 Turbo) for use cases that truly demand them.
5. Scale to full volume: After the smaller test succeeds, expand to the entire dataset.

Additional optimisation ideas:

• Batch processing: Queue non-urgent records and run them during off-peak hours.
• Incremental processing: Use Data Vault change detection to handle only new or updated files.
• Confidence thresholds: Discard results below a set confidence level to avoid paying for low-quality extractions.

Quality assurance
Never assume AI outputs are perfect. Put robust checks in place:

• Business-rule validation: Apply domain rules: dates must fall in valid ranges; classification labels must match your approved taxonomy.
• Statistical monitoring: Track accuracy and confidence over time; watch for shifts that signal model drift or degradation.
• Human-in-the-loop review: For critical use cases, send a sample to human reviewers; their feedback becomes ground truth and flags edge cases your rules miss.

beVault’s Verify module simplifies this work with built-in validation rules, automated checks and dashboards that continuously monitor AI output quality.

The core idea is straightforward: AI outputs are data. They aren’t fleeting insights to use once and forget; they are valuable, auditable assets that deserve the same rigour and governance as any traditional source.

Enterprises that build bidirectional pipelines—AI both consumes data and feeds new data back—gain a clear edge. Their datasets become richer by combining structured sources with AI-extracted intelligence from unstructured content.

beVault is designed for this model. Data Vault 2.0’s historisation and source-system tracking make AI outputs easy to integrate. Distribute supplies the AI consumption layer, States orchestrates model calls with no custom code, and Source models the results with full lineage.

Start small: pick one high-value case where AI can mine unstructured files, craft and test your prompt on a sample set with a cost-effective model, validate quality, then scale. As you add use cases, you’ll create a true bidirectional ecosystem where AI is both consumer and contributor.

The future of enterprise data is bidirectional AI integration. Organisations that treat AI outputs as first-class, governed data sources today will turn their AI spend into lasting competitive advantage.