You can't simply point AI at your business and expect it to find a magic bullet.
Without a defined objective and proper AI data preparation, you risk wasting internal resources, spending thousands on AI tokens and building a solution that fails to deliver business value.
VLDB's AI data preparation and data readiness services help you establish whether your enterprise data can support your AI ambitions - and address the gaps before they undermine your project.
Whether you're developing a private AI agent, deploying generative AI or launching an AI proof of concept, the quality and relevance of your data directly affect the usefulness of the results.
Incomplete records, inconsistent definitions, outdated documents and disconnected systems can lead to inaccurate or misleading answers. Even accurate data can be misinterpreted when the AI lacks business context.
Before you invest in AI, you need to establish:
Is the information accurate, complete, consistent and current?
Does it support the specific business problem you want to solve?
Are definitions, relationships, terminology and business rules understood?
Is ownership clear, with appropriate access permissions and controls?
Can you test whether the solution delivers the intended outcome?
This groundwork may be the least exciting part of an AI project. It is also essential to making the results useful.
An unclear use case can turn an AI proof of concept into an open-ended experiment. Your team spends time fixing inputs, adjusting prompts and repeating tests, while AI token costs and development effort accumulate. Preparation helps you focus investment on a defined problem with measurable business value.
Poor data quality and missing context can cause AI systems to produce answers that sound credible but are wrong. If those outputs inform customer service, reporting or operational decisions without suitable validation, errors can spread into business processes. Data validation and business context reduce avoidable errors and support meaningful testing.
Connecting an AI solution to enterprise data without reviewing permissions can make sensitive information available to users who should not see it. Private AI deployment still requires clear access controls and appropriate data handling. AI data governance helps establish which information the solution can access, who can use it and how it should be protected.
A demonstration built on manually cleaned files may work in a controlled setting but struggle with live enterprise data. Missing integration, refresh processes and ownership can prevent a promising AI POC from becoming a reliable business service. Preparing data pipelines and maintenance processes helps bridge the gap between demonstration and operational use.
When an AI solution gives inconsistent answers or fails to meet expectations, employees and business sponsors can lose confidence in the project. Rebuilding that trust can be harder than correcting the underlying data. A clear scope, agreed limitations and evidence-based validation help set realistic expectations from the start.
VLDB specialises in enterprise databases, data engineering and complex data environments. We help organisations prepare the data and context their AI projects need.
We work with business stakeholders to identify the pain point, agree the scope and establish how success will be measured.
We identify relevant databases, documents and business systems, then assess data quality, availability, integration requirements and access constraints.
We address issues such as missing values, duplicate records, inconsistent formats and conflicting definitions, prioritising the data your use case depends on.
We capture the meaning behind your data: business terminology, relationships, rules and source information - giving your AI solution a stronger foundation for interpreting information correctly.
We help define data ownership, appropriate permissions and handling requirements, including how sensitive information should be managed.
We help establish how results will be checked against the business objective and how the underlying data will remain current as the solution develops.
VLDB can take your project from AI data readiness assessment through to AI implementation, including setting up a private AI agent and working with stakeholders to validate the outcome.
We can also prepare your data for an internal development team or another AI supplier - you don't have to use VLDB for the entire project.
You choose the delivery model. Our role is to provide a reliable, well-understood data foundation that supports your business objective.
Need broader data engineering, not just AI readiness? Data Services covers ETL/ELT, modelling and migration. Already have clean metadata and just need people to query it in plain English? See DBAsk. Need the data to stay on European soil throughout? See SovereignDB.
Before you commit more budget to AI development, establish whether your data can support the result you need. Talk to VLDB about your AI use case, enterprise data readiness and the preparation required to move forward with confidence.