The 'last-mile' data problem is stalling enterprise agentic AI — 'golden pipelines' aim to fix it

Traditional ETL tools like dbt or Fivetran prepare data for reporting: structured analytics and dashboards with stable schemas. AI applications need something different: preparing messy, evolving operational data for model inference in real-time.

Empromptu calls this distinction "inference integrity" versus "reporting integrity." Instead of treating data preparation as a separate discipline, golden pipelines integrate normalization directly into the AI application workflow, collapsing what typically requires 14 days of manual engineering into under an hour, the company says. Empromptu's "golden pipeline" approach is a way to accelerate data preparation and make sure that data is accurate.

The company works primarily with mid-market and enterprise customers in regulated industries where data accuracy and compliance are non-negotiable. Fintech is Empromptu's fastest-growing vertical, with additional customers in healthcare and legal tech. The platform is HIPAA compliant and SOC 2 certified.

"Enterprise AI doesn't break at the model layer, it breaks when messy data meets real users," Shanea Leven, CEO and co-founder of Empromptu told VentureBeat in an exclusive interview. "Golden pipelines bring data ingestion, preparation and governance directly into the AI application workflow so teams can build systems that actually work in production."

How golden pipelines work

Golden pipelines operate as an automated layer that sits between raw operational data and AI application features. 

The system handles five core functions. First, it ingests data from any source including files, databases, APIs and unstructured documents. It then processes that data through automated inspection and cleaning, structuring with schema definitions, and labeling and enrichment to fill gaps and classify records. Built-in governance and compliance checks include audit trails, access controls and privacy enforcement.

The technical approach combines deterministic preprocessing with AI-assisted normalization. Instead of hard-coding every transformation, the system identifies inconsistencies, infers missing structure and generates classifications based on model context. Every transformation is logged and tied directly to downstream AI evaluation.

The evaluation loop is central to how golden pipelines function. If data normalization reduces downstream accuracy, the system catches it through continuous evaluation against production behavior. That feedback coupling between data preparation and model performance distinguishes golden pipelines from traditional ETL tools, according to Leven.

Golden pipelines are embedded directly into the Empromptu Builder and run automatically as part of creating an AI application. From the user's perspective, teams are building AI features. Under the hood, golden pipelines ensure the data feeding those features is clean, structured, governed and ready for production use.

Reporting integrity versus inference integrity

Leven positions golden pipelines as solving a fundamentally different problem than traditional ETL tools like dbt, Fivetran or Databricks.

"Dbt and Fivetran are optimized for reporting integrity. Golden pipelines are optimized for inference integrity," Leven said. "Traditional ETL tools are designed to move and transform structured data based on predefined rules. They assume schema stability, known transformations and relatively static logic."

"We're not replacing dbt or Fivetran, enterprises will continue to use those for warehouse integrity and structured reporting," Leven said. "Golden pipelines sit closer to the AI application layer. They solve the last-mile problem: how do you take real-world, imperfect operational data and make it usable for AI features without months of manual wrangling?"

The trust argument for AI-driven normalization rests on auditability and continuous evaluation. 

"It is not unsupervised magic. It is reviewable, auditable and continuously evaluated against production behavior," Leven said. "If normalization reduces downstream accuracy, the evaluation loop catches it. That feedback coupling between data preparation and model performance is something traditional ETL pipelines do not provide."

Customer deployment: VOW tackles high-stakes event data

The golden pipeline approach is already having an impact in the real world.

Event management platform VOW handles high-profile events for organizations like GLAAD as well as multiple sports organizations. When GLAAD plans an event, data populates across sponsor invites, ticket purchases, tables, seats and more. The process happens quickly and data consistency is non-negotiable.

"Our data is more complex than the average platform," Jennifer Brisman, CEO of VOW, told VentureBeat. "When GLAAD plans an event that data gets populated across sponsor invites, ticket purchases, tables and seats, and more. And it all has to happen very quickly."

VOW was writing regex scripts manually. When the company decided to build an AI-generated floor plan feature that updated data in near real-time and populated information across the platform, ensuring data accuracy became critical. Golden Pipelines automated the process of extracting data from floor plans that often arrived messy, inconsistent and unstructured, then formatting and sending it without extensive manual effort across the engineering team.

VOW initially used Empromptu for AI-generated floor plan analysis that neither Google's AI team nor Amazon's AI team could solve. The company is now rewriting its entire platform on Empromptu's system.

What this means for enterprise AI deployments

Golden pipelines target a specific deployment pattern: organizations building integrated AI applications where data preparation is currently a manual bottleneck between prototype and production. 

The approach makes less sense for teams that already have mature data engineering organizations with established ETL processes optimized for their specific domains, or for organizations building standalone AI models rather than integrated applications.

The decision point is whether data preparation is blocking AI velocity in the organization. If data scientists are preparing datasets for experimentation that engineering teams then rebuild from scratch for production, integrated data prep addresses that gap. 

If the  bottleneck is elsewhere in the AI development lifecycle, it won't. The trade-off is platform integration vs tool flexibility. Teams using golden pipelines commit to an integrated approach where data preparation, AI application development and governance happen in a single platform. Organizations that prefer assembling best-of-breed tools for each function will find that approach limiting. The benefit is eliminating handoffs between data prep and application development. The cost is reduced optionality in how those functions are implemented.