OneSchema Studio
The AI workspace for all your data.
Connect any data source, clean and reshape files by talking to an AI agent instead of writing a script, and turn what works into something your whole team can reuse — no engineering required.
The ONeSchema Difference
OneSchema Studio vs. Claude Cowork
Both put an AI agent in front of your data, and both are good at what they were built for. The difference is what surrounds the agent: Claude Cowork is a coding agent that lives in a repository, while Studio is a data-operations workspace wired into your pipeline from day one
Where it runs, and what persists
A collaborative cloud workspace your team opens in a browser — nothing to install. Sessions are saved and carry memory of the work, so a teammate can reopen a project and pick up in full context instead of starting over. Nothing is trapped on one laptop.
A local CLI in your terminal or IDE. Source files stay on the machine and only relevant snippets go to the cloud for inference — so neither fully cloud nor fully offline. Sessions and memory files do persist, but on the individual machine that ran them, and every user installs and maintains their own setup
Getting data in and out
Managed connectors for S3, GCS, Azure Blob, SFTP, and enterprise systems like Salesforce and Workday — pull source files in and deliver results back out.
Can reach external systems through MCP servers or scripts a user writes, but there are no managed, built-in data connectors to configure
Working with large, tabular files
An editable data grid built for large tabular files — see the data, correct it in place, with autosave and real-time feedback
Terminal- and IDE-based. No visual spreadsheet or grid, so inspecting a wide, messy file means reading it through text output
Schema and validation governance
OneSchema Templates enforce your organization's schema rules and validations on every file, consistently across the team
No built-in schema or template governance. Validation rules are whatever each user writes into their own code
Shared, reusable team knowledge
A centrally governed library of Templates and Skills, curated for the organization and available in every session by default.
Skills and plugins do exist and can be shared through version control or installed as plugins — but they are ad hoc markdown and config files, not a governed library.
Promote a proven session into a Workflow — a version-controlled pipeline that runs on a schedule or via API.
What is Studio
One workspace: your files, an AI Agent, and a live grid
Studio brings together everything a messy file needs before it's usable — a place to keep your files organized, an AI agent that does the cleanup work, and a grid where you can see and correct the result — instead of spreading that work across spreadsheets, scripts, and tickets.
Skills your whole team can reuse
Generate, update, and share reusable Skills across the team with governance built in, so one person's work becomes something the organization can run.
Your team validates the AI
The agent proposes, a person validates, then the agent executes. Your team reviews that execution before approving it for production — which matters most when the data is sensitive.
A live grid built for large data sets
Visualize and operate on data at real scale. See exactly what the agent changed and correct it in place, instead of reading a big file through text output.
Bring your own key
Plug OneSchema into your enterprise LLM stack so AI-powered transformations run inside your environment, not ours.
Pull from any source, push to any destination
Object storage (S3, GCS, Azure BLob), lakehouses, databases, enterprise systems like Workday and Salesforce, SFTP, or upload — your files or your customers', pulled in and pushed back out.
Why We built studio
Messy data shouldn't require a script or a ticket
Today, every messy file is either a manual afternoon in a spreadsheet or an engineering ticket. Neither scales: manual work doesn't get faster as your team grows, and an engineering queue was never built to prioritize one team's data cleanup. We built Studio so the people closest to the data — not just the people who can code — can do this work themselves, without losing visibility or governance along the way.
No engineering bottleneck
- Business users explore, clean, join, and reshape files without waiting on a script.
Ad hoc work becomes reusable
- A one-off cleaning session becomes a saved Skill that the next person can start from.
Visible and auditable
- Files, edits, prompts, and outputs are scoped to a project — not a local spreadsheet.
Inside your governance
- The agent works against your templates and validation rules. Not shadow IT.
Who studio is for
Built for the people closest to the data
Not engineers writing one-off scripts — the non-technical teams who are accountable for turning a messy file into something clean, trustworthy, and ready to use.
System Integrators
- Cleaning customer files before a repeatable process exists for them.
Data / Operations analysts
- Handling recurring, not-yet-automated data prep across the business.
AI Enablement / Operations Leaders
- Handing routine file wrangling back to the business — with governance intact.
Use cases
What a session in Studio actually looks like
A few real shapes of work teams bring to Studio — each one a file (or a few) that needed to go from messy to usable.
Merge two CRMs into one clean schema
- Combine Salesforce account exports with a HubSpot company file, match by domain, flag duplicates, and load one standardized account list — reviewed in the grid before it ships.
Load a new enterprise customer's data
- Take a customer's roster or catalog, map it to your schema, validate required fields, and get them live — without opening an engineering ticket.
Turn a PDF or multi-tab Excel into a usable file
- Pull tables out of scanned statements, invoices, or multi-worksheet exports into a clean CSV, ready for the next step in your process.
Save what worked as a Skill
- Turn a proven cleanup or mapping pattern into a reusable Skill your teammates can pull from — so it isn't rebuilt from scratch next time.