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APPLIED AI / CLAUDE

Claude, put to work safely

We help enterprises adopt Claude, build agents with it and connect it to their systems — inside the cloud boundary you choose.

Where we help

  • Claude enterprise adoption

    Rollout, governance policies, access, data controls and enablement for teams using Claude.

  • Claude Code for engineering teams

    Rollout, guardrails, policies and productivity measurement for engineering teams using Claude Code.

  • Agents built with Claude

    Workflow agents using the Claude API or the Claude Agent SDK, with human approval gates and audit logs.

  • Connecting Claude to your systems with MCP

    Secure Model Context Protocol integrations to internal data and tools, with scoped access and logging.

  • Evaluation and safety

    Test sets, red-teaming, monitoring and cost control for Claude-based systems.

Run Claude where your data lives

Claude is available through Anthropic's API and through Amazon Bedrock, Google Cloud Vertex AI and Microsoft Foundry, so we can run it inside the cloud boundary you choose.

  • Anthropic API
  • Amazon Bedrock
  • Google Cloud Vertex AI
  • Microsoft Foundry

Governed from day one

  • Data boundaries

    We agree what data Claude may see and where it runs before anything is built. Your data stays inside the boundary you define.

  • No training on client data

    We do not use client data to train or fine-tune models, unless a client asks us to in writing for their own dedicated model.

  • Evaluation

    Accuracy and behavior are tested on your data against a target you set, before go-live and after every change.

  • Audit logging

    Prompts, tool calls, outputs and approvals are logged so you can answer who did what, and when.

  • Human approval

    Where liability sits with a person, a person approves. Agents propose; people decide.

How an engagement runs

  1. Readiness

    We agree the use cases, check the data and risks, choose where Claude runs and set the policies that apply.

  2. Pilot on your data

    A working version on your own data, with accuracy measured against a target you set.

  3. Production

    Integration with your systems, security hardening, approvals, logging and monitoring.

  4. Run

    We operate and improve it, or hand over to your team with a run guide.

Questions

Which cloud should we run Claude on?

Usually the one where your data and security controls already live. Claude is available through the Anthropic API, Amazon Bedrock, Google Cloud Vertex AI and Microsoft Foundry. Feature availability and regions can differ by platform, so we check them against your requirements during readiness.

Does Anthropic train on our data?

Data use is governed by the commercial terms of the provider you run Claude through, whether that is Anthropic directly or a cloud platform, so check that provider's current terms. We review those terms with you, configure the relevant settings and confirm them in writing. We do not use client data to train or fine-tune models, unless a client asks us to in writing for their own dedicated model.

How do you measure Claude Code impact?

We agree the measures with your team before rollout, such as cycle time, review load, defect trends and developer feedback, and take a baseline first. We report what changes, including where it does not help.

Can Claude connect to our internal systems?

Yes. We use the Model Context Protocol (MCP) to connect Claude to internal data and tools, with access scoped to what each use case needs and every call logged.

How long does a pilot take?

It depends on the use case, data and integrations involved, so we do not promise a fixed duration. Each pilot is scoped in the Blueprint before work starts.

Claude and Anthropic are trademarks of Anthropic, PBC. AWS, Microsoft Azure and Google Cloud are trademarks of their respective owners. YatiSphere is an independent company and is not affiliated with or endorsed by Anthropic.

Enterprise AI, engineered to be trusted

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