Today we are launching ChatStudio, the place teams use AI to do real work inside the CodeBots platform.

AI can get you to a first answer quickly. But once work moves beyond a single person and a single prompt, things start to drift. Decisions get buried in chat history, model choices vary from task to task, and it becomes harder to see what actually happened between the request and the result.

That is the problem ChatStudio is built to solve. It gives teams a place to work with AI while keeping the plan, the activity, and the resulting changes visible as work happens.

In other words, ChatStudio is where structured intent turns into governed execution. It helps teams move quickly without losing control, and it creates a place where meaningful work can become reusable over time instead of disappearing into chat history.

Why we built it

There is a big difference between using AI to get an answer and using AI to deliver work you can trust.

We built ChatStudio because the real challenge is not generating output. The challenge is keeping execution aligned with intent while work moves across prompts, models, bots, and platform services.

Without that structure, teams quickly run into familiar problems:

• useful experiments that cannot be repeated
• chat history that explains too little when you need to review a decision
• model choices that are hard to standardise across teams
• changes happening faster than people can confidently inspect them

ChatStudio is our answer to that. It brings the conversational experience into a governed operating model, where the work stays visible, reviewable, and tied back to the platform around it.

What teams can do today

ChatStudio already gives teams a practical set of capabilities for structured AI execution.

1. Execute workflows through pipelines. ChatStudio is connected to the wider CodeBots environment, including pipeline resources, so teams can move from conversation into executable workflows instead of stopping at suggestions.
2. Review changes before they become drift. When work produces model changes, ChatStudio can surface a review panel that shows what changed and lets teams keep or undo those changes deliberately.
3. Bring bots, resources, and activity into the same place. Installed bots, resource-aware execution, todo lists, and in-progress task states keep the conversation tied to the actual work system and make it easier to see what the assistant is doing.
4. Choose the right model for the job. The platform already supports multiple providers, including OpenAI, Anthropic, Mistral, and Ollama, with per-user credentials and provider-aware model selection.
5. Keep long-running work usable. Search, session history, paginated message loading, and streaming responses make it easier to follow execution as it happens and return to important conversations later.

The result is a better balance of speed and control. Teams can move quickly, but they are not left guessing what happened between the question and the outcome.

How ChatStudio fits the platform

ChatStudio matters most when you see it as part of the broader CodeBots operating model.

• AILab turns intent into structured models, pipelines, and workflows, so teams start from something more durable than a loose prompt.
• ChatStudio turns that structure into governed execution, with visible activity, model selection, and reviewable outcomes.
• Paths and Docs carry meaningful outcomes forward as durable, searchable bot knowledge that future agents can reuse.
• Marketplace makes reusable bots, workflows, standards, and knowledge easier to share across the organisation.
• DataIQ is the next step on the roadmap, helping teams measure outcomes, expose drift, and eventually give agents clearer optimisation signals tied to real KPIs.

That is the pattern we are building toward: intent, execution, reuse, and measurement working together instead of living in separate tools.

What comes next

This release is the start of the ChatStudio layer, not the end state.

Near term, we are focused on making the execution surface even stronger:

• turning significant ChatStudio conversations into reusable knowledge, so the useful outcome of a session does not disappear when the chat ends
• carrying that knowledge forward with the bot, so it becomes durable documentation through the Docs service
• feeding that documentation back into future agent work through RAG, so teams can reuse what worked instead of starting from scratch each time
• bringing DataIQ into the picture so teams can measure what is working, spot drift earlier, and improve against real KPIs

That roadmap matters because governed delivery needs more than interaction design. It needs a durable feedback loop. ChatStudio is where the work happens, Paths and Docs make that work reusable, and DataIQ is the next step for measuring what is actually improving.

Try it

If you want to see where ChatStudio fits in the platform today:

• Sign up and Start Here
• Read the broader release notes
• Join the discussion on MatterMost

If you are already thinking beyond one-off AI experiments and toward a more governed delivery model, ChatStudio is where that transition starts.