Platform · AI Labs

Secure sandboxes for your teams to practise with AI tools

Give your engineers and AI teams a secure place to experiment, test, and build with Claude Code, GitHub Copilot, and Codex. Fully managed and isolated by Anthropos, with cost capped and every session scored into your AI Readiness Score.

4
Lab types — prompt to code
15–30
Minutes per challenge
30+
AI Skill Paths included
0–5
Evidence-backed scoring scale
Anthropos AI Lab — in-browser sandbox with coding agent
What an AI Lab is

A sandbox you launch, hand to your people, and throw away

A secure sandbox you spin up for your team. Nothing runs on your own machines: it is a real server people build and test on live, download what they made, then delete and start fresh. Labs are for hands-on technical work, run in two modes, and you set how powerful each one is.

Playground

An open sandbox. Your people jump in and use Claude Code, Copilot, or Codex however they want: build something, try an idea, push the tools and see what they can really do.

Template

A sandbox pre-loaded for a task, like building an application. Your people build it, run it live, test it, and download what they shipped. Then the sandbox is wiped.

Practice or assessment, same sandbox

Use a Lab for open practice and training, or run it as a graded assessment. Anthropos sees the full session (every command, how each model and tool is used, and what gets built) and scores it with the same evaluation engine behind our AI Simulations.

As powerful as you need

Size each Lab to the work. Allocate more compute and memory when people are running heavier builds or bigger workloads.

The models you choose

Pick which models and LLMs your people can use inside each Lab, so they work only with the tools you have approved.

A ceiling you set

Cap every Lab by credits or by minutes. Decide your team can spend, say, $1,000 on Labs this quarter, and that is the limit.

Pick the models your teams work with

Anthropic Claude
OpenAI
Google Gemini
Mistral
Meta Llama
Platform integration

AI Labs is a module inside Anthropos Workforce — not a standalone tool

Every Lab session is connected to the same skills layer as your Workforce Intelligence, AI Readiness Score, and development plans. There is no data silo — results are immediately visible across the full platform.

Feeds the AI Readiness Score
Labs completion is one of three signals driving the AI Readiness Score — alongside AI Simulation results and AI Interview data. Each session updates the participant’s verified AI proficiency level in real time.

Visible in Workforce Intelligence
Labs results roll up into heat maps and skill gap views by role, department, and organisation — so L&D can see where AI proficiency is growing and where it is not, at any level of aggregation.

Closes the loop with AI Academy
Each Lab points people to the AI Academy paths that close their specific gaps, then lets them prove the progress with a new challenge in the same area. Learn in AI Academy, practise in AI Labs, verify in your AI Readiness Score — that is how AI transformation actually moves inside a company.
Who it’s for

Bought by IT and engineering leaders, rolled out with L&D

AI Labs lands with the people responsible for AI inside the company. CTOs, CIOs, and IT leaders own the outcome. L&D and Learning Technology teams run the programme alongside them.

Primary buyer

CTOs, CIOs and IT leaders

They need a safe place for their engineers and most advanced AI people to experiment, test, and build with real AI tools. No risk to production, internal systems, or budget. Anthropos owns and runs the environment.

Co-owner and rollout

L&D and Learning Technology

The Learning Technology lead who has to give technical teams something real, not another course. They run AI Labs alongside IT: assigning challenges, tracking progress, and connecting it to development plans.

The users

Engineers, data and advanced AI practitioners

The people inside the sandbox doing the work: developers, DevOps, and data engineers, plus the advanced AI users pushing what is possible. Business roles use the prompt engineering and GenAI workflow Labs too.

Trusted by leading enterprises

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Common questions

What L&D and Engineering leaders ask before rolling out AI Labs

Practical answers for teams evaluating AI Labs for their organisation.

What is the difference between AI Labs and an AI Simulation?
AI Simulations assess broad workplace skills through a 30–45 minute scenario with AI actors. AI Labs are the technical, hands-on layer: a live sandbox with real AI tools, scored on what gets built. They complement simulations, not replace them.
Which AI tools are available in the coding Labs?
AI Labs currently support Claude Code, GitHub Copilot, and Codex in sandboxed coding environments. The environment can be configured for specific languages and frameworks — Python, JavaScript, Go, and more. You also choose which models and LLMs are available inside each Lab, and you can allocate more compute and memory for heavier workloads. Custom AI tool configurations for enterprise clients are available on request through the Studio custom build process.
Where do the sandboxes run, and is our code and data safe?
Every Lab runs in an isolated sandbox that Anthropos hosts and manages. Nothing installs on your machines, and it never touches your internal systems unless you wire it in. It is metered and torn down after the session, so cost stays capped and no data lingers. For enterprise clients we configure region, retention, and tool access to your requirements.
Can we build custom AI Labs challenges for our company’s specific tools and workflows?
Yes. Anthropos Studio builds custom Labs around your own tools, documents, and workflows, with no technical background required. Most are ready to deploy within a day or two.
AI Labs

See what a Lab challenge looks like — and how it gets scored

Book a 30-minute demo to see a live Lab session, understand how results connect to your workforce data, and explore how custom challenges are built for your specific AI tools and workflows.