Every CxO knows the number—and many have felt it firsthand: 70 – 85 % of AI projects die between proof-of-concept and production. People blame immature tech, scarce talent or inflated expectations. The real issue? Your data infrastructure.

Modern models are powerful, but when they collide with an enterprise landscape of fragmented systems, inconsistent quality and missing business context, they stall. Even the smartest agent can’t outrun a weak foundation.

beVault shifts that equation. As the first Data Vault 2.0-certified platform, it turns the three data issues that sink AI projects into competitive advantages. In the sections that follow, we’ll look at each problem and show how beVault resolves it step by step.

Let’s examine the three data problems that consistently derail AI initiatives

Scattered data across multiple systems
AI agents work best with a handful of clear tools—research shows performance peaks around three to five. Enterprises, however, spread customer data in Salesforce, finance in SAP, operations in legacy apps and product specs in PLM, forcing an agent to juggle dozens of connectors. The result is slow, indecisive logic and a maintenance nightmare each time a new AI platform appears. Scattered data isn’t just inconvenient; it’s an architectural weakness that makes projects costly to build and harder to keep alive.

Uncertain data quality
AI accepts data at face value. Missing values, inconsistent formats and duplicates don’t just skew results; they erode executive trust. Worse, quality decays continuously as systems and processes change. One-off cleansing won’t cut it—AI needs ongoing validation and correction to stay credible.

Missing business context
To an agent, a table is just names and numbers unless metadata explains what they mean. “Active customer,” “lifecycle stage” or any domain term varies by company, yet most warehouses store only raw columns. Without explicit definitions and relationships, the model can return answers that are technically correct but wrong for the business. Well-contextualised data consistently beats larger, context-free datasets.

Consolidation: One business-oriented data model
beVault funnels every source system into a single Data Vault 2.0 model. AI agents no longer juggle 50 connectors; they query one endpoint with a consistent structure. Add a new source once, and every agent sees it. When a source changes, beVault absorbs the complexity, keeping the agent’s toolset simple and fast.

Built-in business context through metadata
beVault’s standout feature is the depth of its metadata. Every hub, link and satellite is documented with business definitions, relationship semantics and usage rules. When an AI agent queries “LinkCustomerPurchase”, it receives metadata explaining this is “the business relationship between customers and their purchasing behaviour,” plus which attributes matter and how to interpret them. Your own concepts—“active customer,” product hierarchy, lifecycle stages—are captured the same way. Because the agent can read that metadata directly, raw tables turn into business-aware intelligence without prompt workarounds.

Quality assurance: The Verify module
The beVault Verify module applies completeness, consistency and anomaly checks on every load, delivering continuous data quality. Executives can trust AI outputs because they rest on data that has cleared Verify’s validations, with full lineage showing each rule applied.

The architecture follows a clear flow:

Source Systems → beVault → Vector Database ← AI Agent.

beVault sits at the centre, ingesting data from every source—databases, APIs, structured files, even unstructured content. The beVault Verify module validates each load, while metadata enrichment adds the needed business context. From there the platform feeds AI through two dedicated pathways, giving models clean, well-described data to consume.

Handling Qualitative and Quantitative Data

For qualitative information: product descriptions, venue traits, customer feedback and other unstructured text — is loaded by beVault into a vector store such as Chroma, Pinecone, pgvector or Qdrant. AI agents then run semantic search: instead of keyword hits they retrieve passages with similar meaning. Ask, “Which destinations work well for technical conferences?” and the agent finds venues whose descriptions align conceptually.

For quantitative information: sales figures, customer counts, performance KPIs — is exposed through dedicated information marts. An agent calls the mart tool and receives structured results, e.g., “What’s our revenue trend over the past six quarters?”.

Separating vectors for words and marts for numbers gives agents both semantic recall and precise analytics.

Orchestration with States: beVault’s States engine schedules every step: extract from sources, load to the vault, run Verify checks, and push cleansed data to the vector store and marts. The flow is fully automated, so AI always works with fresh, validated data.

On-Premise Deployment: Need full data sovereignty? Run the whole stack locally: beVault on-prem, Qdrant or pgvector for vectors, Ollama for the LLM, and n8n for agent logic. No data leaves your infrastructure, ideal for organisations with strict governance rules while still harnessing modern AI.

The challenge
PCMA and dFakto wanted to reinvent event planning with AI. Planners must assess thousands of venues and destinations spread across venue-management tools, hotel platforms, tourism databases and partner files.

Simply querying an off-the-shelf LLM wasn’t enough. Public models hold generic tourist facts; they miss the pro-grade data planners rely on—up-to-date capacities, event pricing, partner-only amenities and specialised capabilities that never appear in open training sets.

The beVault solution
beVault pulled every partner feed into a single Data Vault model, while PCMA’s community curated the records. This human-in-the-loop process produced a dataset no pre-trained LLM can match.
Two data paths were then prepared:

• Qualitative content: venue descriptions and destination traits—flows to a vector store for semantic search;
• Quantitative facts: capacity, price, availability—stay in information marts for precise queries. Metadata links it all, so the AI understands how destinations, venue types and event needs relate.

The results
Planners now receive tailored recommendations in minutes, not hours. The AI can say, “This city fits technical conferences thanks to its high-speed infrastructure and interactive venues,” and back it up with verified, proprietary data.

When the foundation is consolidated, quality-assured, contextualised and enriched with exclusive intelligence, AI delivers. See it live at destinaitor.com. The same blueprint applies to retail, finance, healthcare and any sector wrestling with fragmented data.

Speed to production

Deploy new AI use cases in weeks, not months, by leveraging your unified data foundation

Stakeholder trust

Complete data lineage and continuous quality validation build confidence in AI outputs

Higher accuracy

Quality-assured data plus business context means AI agents make better decisions

Effortless scaling

Add new data sources and AI use cases without rebuilding your architecture

The AI revolution demands a data revolution first. Firms that pour money into algorithms while neglecting data foundations see the same result: 70 % of projects stall. The winners all share one trait—they built a systematic data architecture before scaling AI.

The shift is to view data architecture as a strategic AI enabler, not just plumbing. beVault combined with Data Vault 2.0 delivers an “AI-ready by design” foundation through consolidation, continuous quality checks, rich business context and dual pathways for qualitative and quantitative analysis.

That foundation is your edge. While competitors wrestle with messy integrations and dubious quality, you deploy AI agents with confidence. Projects succeed because the data layer is right, and they scale because the architecture was designed for the enterprise from day one.

The technology exists, the methodology is proven, and the outcomes are visible in solutions like destinaitor.

Start building your AI-ready foundation today. Cloud or fully on-prem for data sovereignty—the path is clear. Contact the beVault team to discuss your data challenges and see how Data Vault 2.0 can fit your architecture.