Describe the outcome
The planned guided experience helps an IT professional describe a task, identify its data and tools, define success, and review the resulting workflow.
Atelier / AI agent workshop
In development · product design stageTurn a useful idea into a workflow your team can own.
Atelier is a planned workspace for training, building, governing, running, and improving AI workflows.
When complete, Atelier will not just provide the tools needed for production workflows, but do so with a user interface that is usable even without a degree in machine learning.
Objects and templates are prebuilt to get you started and an AI assistant is always ready to help or to build workflows from start to production.
And for those who know machine learning, everything is available to dig deep.
Inside the planned workspace
From development to daily operations.
Design mockups · sample data
Atelier is in development. These mockups illustrate the intended experience; features, metrics, and release numbers are illustrative.
Operations overview
View full size ↗ (opens in a new tab)A proposed overview of application activity, approvals, policy decisions, and operational signals.
Designed for two ways of working
Guidance when you need it. Configuration when you want it.
Inspect the details
Advanced controls are intended to expose model endpoints, tool permissions, retrieval settings, execution limits, versioning, and deployment configuration.
Planned capabilities
A lifecycle, not just a canvas.
Build
Compose a workflow using models, data sources, tools, and human approval steps. Start from a focused template or a plain-language brief.
In development · product design stageGovern
Define approved sources, allowed actions, and review requirements before execution. Keep policy enforcement visible during runtime.
In development · product design stageRun & route
Deploy an approved workflow and route model requests according to explicit endpoint and data policies.
In development · product design stageRetrieve knowledge
Use approved internal sources to supply relevant context. Preserve source references and access boundaries.
In development · product design stageEvaluate & adapt
Test proposed changes against known examples. Explore fine-tuning only when the use case and evidence justify it.
In development · product design stageObserve
Review execution traces, operational signals, and quality feedback. Use findings to prepare and approve the next version.
In development · product design stageHuman oversight
Make improvement accountable.
Evidence, not an assurance of correctness
Execution traces and evaluations can help identify problems. They do not guarantee factual accuracy or reveal a model’s internal reasoning. Observe is intended to surface useful evidence for an accountable workflow owner.
A conversation can be an interface
A private knowledge assistant is one possible Atelier workflow. Chat, scheduled execution, and application-triggered tasks are interaction patterns within the platform direction, rather than separate products today.
Build with us
Start with one useful workflow.
Bring your infrastructure, your operational constraints, and a problem you need solved. Together, we can define a focused pilot and what success should look like.