Ready your BusinessAI-Ready Data Foundation
Make your data fit for the AI you're about to build.
Start with the use case you want to run, not an inventory of every source. Roboyo works with your teams to find the data that use case needs, bring it together from the systems where it sits, and put governed pipelines into production for it. You also get a recommended architecture and a roadmap for the data work that should follow.
Where AI projects stall
AI use cases stall on data nobody set up for them.
An AI use case needs records it can act on, drawn from several systems and consistent enough to trust. Where those systems were built at different times, have no clear owner and are kept in line by hand, that data is not ready. When a pilot stalls there, the model takes the blame.
AI-Ready Data Foundation is designed for teams facing one or more of these situations:
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A pilot stalled, and the data is the suspect.
An earlier attempt did not reach production, and the team believes the data is why.
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The use case is clear; the data behind it is not.
You know the agent or workflow you want to build, but not which sources it needs, who owns them or whether they can be trusted.
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The same data lives in systems that disagree.
Records are copied and corrected by hand between systems, and no single version is trusted.
Use what is already there
How not to rebuild your data estate.
We start from what one use case needs and work with the systems and platform you already have. There is no platform trial to run first and no multi-year modernization to wait for: the work covers the data that use case depends on, built to run in production.
- 01Source systems that disagree
- 02Sources mapped and assessed
- 03One governed view for the use case
- 04Pipelines with quality checks
- 05Ready for the agent or workflow
The records the use case needs sit in several systems that disagree.
Brings them together into one governed view the use case can run on.
Nobody owns the data, or nobody knows whether it can be trusted.
Assesses its quality and completeness, and sets owners and access rules.
People move and correct the data by hand.
Replaces the manual steps with automated pipelines and quality checks.
What you get
Data one use case can run on, and a plan for what follows.
A readiness view of the data the use case needs
We identify the sources the use case depends on, where they live and who owns them. Each is assessed for quality, completeness and fitness for AI, and the gaps are named, so what gets built rests on evidence rather than assumption.
Governed pipelines in production
The data is brought together from its source systems and delivered through pipelines built for the use case. Ownership, access controls, quality checks and lineage are built into how the data flows, not added afterward.
An architecture and a roadmap
You get a recommended target architecture and a roadmap for the data work that should follow, ranked by business value and delivery effort. Documentation and runbooks go to the team that will operate the pipelines.
How it works
From one use case's data to pipelines in production.
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01
Discover
We trace the data the use case needs across your systems. We inventory and map those sources, interview the people who own and use them, review the current architecture, assess each source for readiness and identify quick wins.
OutputA readiness view of the use case's data, with the gaps named.
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02
Prioritize
We rank what to fix first by business value, effort and readiness, check the fit of the technology you already run, find the gaps in governance and recommend a target architecture.
OutputAgreed priorities and a recommended target architecture.
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03
Deliver
We set up the data platform on your cloud or extend the one you run, build the priority pipelines, switch on quality checks, and hand over runbooks and documentation to the team that will operate them.
OutputGoverned pipelines in production for the use case, with the architecture and documentation behind them.
Find the right starting point
Data problem, knowledge problem or delivery problem?
AI-Ready Data Foundation fits when you have a use case in mind and the data it needs sits in systems that were never set up for it. Once the first pipelines are live, the Value Delivery Data Engine keeps building, running and improving them as demand grows. If your agent needs to answer from documents, email and files rather than from operational systems, explore Knowledge Fabric for Agents.
Practical questions
Scope, platforms, effort, timing and cost.
Does this cover our whole data estate?
The pipelines are built for the use case you choose first. The assessment also recommends a target architecture and scope, so you can see what the wider estate would need. Wider changes appear on the roadmap, not as a precondition.
Which platforms do you work with?
We build on the platform you run, or help you choose one, including Microsoft Fabric, Azure, Snowflake, Databricks and AWS.
What will our team need to contribute?
We need the people who own and use the source systems, access to those systems, and input from security and compliance on who may see which data. The specific access and time are agreed when the scope is set.
How long will it take, and what will it cost?
That depends on how many systems are involved, the state of the data and your approval requirements. We agree the scope, milestones, responsibilities and commercial terms with you before work starts.
Bring us the AI use case that is waiting on its data.
Tell us what you want to build, which systems hold the data it needs and what has held it back so far. We will help you work out what the data needs before anything is built.