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The agent-ready foundation under every capability: context, business rules and trusted data in one governed model.
Power BI, conversational analysis and an open API, all reading the one model.
See AnalyticsA digital workforce you can audit, working on your own governed model. Included, not an add-on.
See AI StudioWhy it matters
Most enterprises are now planning agents of their own, and most are finding that the hard part isn’t the agent. Models are capable and getting cheaper. What decides whether an agent’s answer is right is the context it reasons over: what each record means, how the business allocates its costs, and whether the data underneath can be trusted.
For technology spend, that context comes from hundreds of systems that rarely agree. The general ledger knows a vendor and an account. It doesn’t know which application the money powers or which business unit uses it. That detail sits in the CMDB, the HR system, the project tools and the cloud and AI bills, each with its own names and its own gaps.
Building that context has always needed a scarce, expensive specialist team, which is why so many organizations get as far as the cost pools and stop. Data Studio is built to take that work on. A guided setup replaces the hand-built pipeline, enrichment rules are written in plain English and the model arrives already loaded with the standards that give an agent its vocabulary, the TBM taxonomy, FOCUS and the standard objects, rather than empty and waiting for you to fill it.
An agent is quick to build. The context it stands on is not.
What makes data agent-ready
What things are
Standard objects aligned to the TBM taxonomy, cloud billing in FOCUS and seven categories of spend give every record a meaning an agent can reason over, from applications and servers to tokens, LLM APIs, agent workloads and GPUs. It arrives already built, not empty.
How your business works
The allocation model states which consumer carries each cost, by which driver, traced back to the ledger line it came from. These are rules a machine can follow, so an agent applies the same logic your finance team does.
Whether to believe it
Quality checks on every load, AI enrichment that shows its reason and confidence, every operation on the record and a named sign-off before anything reaches the model.
It doesn’t matter whose agent asks. Yours, ours or a person with a dashboard, every one draws on the same governed model.
No agent produces a number. Agents read the model. Every figure they return comes from the model’s own calculation.
What Data Studio does
Applications, servers, cost centers, employees, projects and licenses arrive as standard objects aligned to the TBM taxonomy, along with Token Usage, LLM API, Agent Workload and GPU for AI spend. You map your data to a structure that already exists, and every agent reasons in the same vocabulary.
Allocation rules and drivers live in the model, not in a spreadsheet or someone’s head. They state which consumer carries each cost and why, so a person and an agent reach the same answer.
Data that passes validation is signed off by a named person, with a timestamp, before it enters the model. When someone asks where a number came from, the answer includes who approved the data behind it.
Errors stop a load, warnings advise and information flags note what’s worth a look. Referential integrity, data types and duplicate keys are all checked first.
AI classifies and reconciles records against a standard already in the system, or adds a fact no internal system holds, such as an end-of-life date or a vendor owner. Each enrichment can carry a reason and a confidence score.
An open connector library reaches the ledger, the CMDB, the HR system, service management, project tools and the cloud accounts. ServiceNow, Workday, Jira, Databricks, Snowflake and SFTP are among those switched on from the start.
Billing data from every cloud provider is normalized to FOCUS, the open specification for cloud cost and usage data, so a line from one provider means the same thing as a line from another.
When a source system adds or renames a column, the pipeline adapts on the next run instead of breaking.
A four-step wizard takes each source from connection to schedule: source, target, transform and schedule. Data preparation happens inside the platform, visible and versioned, so there’s no separate extract and load tool to license or staff.
Data moves from raw, to cleansed, to business-ready on a Databricks lakehouse, built to carry the volume of usage data that cloud and AI now generate.
Every data operation is logged: who ran it, when, how many rows and from which file. Reconciliation and audit start from the record.
Findings write back to Jira and ServiceNow, and outbound APIs let other systems draw on the model.
Mapping your source fields to the standard objects is done by hand today, and there’s no visual lineage graph yet. The audit log gives an auditor the trace they ask for, but not yet the picture a governance team would like to look at. Agents query the model; they don’t write to it.
Questions Data Studio answers
What it covers
Standards
The standard objects follow the TBM taxonomy. Cloud billing is normalized to FOCUS. MOTEF, MagicOrange’s own framework, holds all seven categories of spend on one model.
Published results
Each of these came from moving onto one governed model, and that starts with getting the data in.
Costed with an automated Bill of ICT, TBM-aligned, with cross-agency transparency.
eHealth Queensland
Fragmented Excel models consolidated into one automated system, run times cut to minutes, defensible chargebacks posted back into the general ledger.
Major international bank
