AI & Machine Learning · Openlayer + Claude
Openlayer MCP with Claude: Catch AI Regressions Before They Reach Customers
The short answer
Claude connects to Openlayer through the Model Context Protocol (MCP), a secure standard that lets Claude read and act within the platform. Once connected, teams can manage projects, review production monitoring, evaluate tests and inspect model performance by asking questions in natural language, with access limited to what the organisation permits.
What is Openlayer?
Openlayer is an AI quality platform. It helps teams manage AI projects, monitor the data their models see in production, run evaluation tests and inspect how models perform. Product teams, machine learning engineers and quality leads typically use it to check that AI applications such as chatbots and agents stay reliable before and after each release.
How Claude connects to Openlayer through MCP
The Model Context Protocol, or MCP, is an open standard that acts like a universal adapter between Claude and other software. It lets Claude use a tool securely, reading information and taking actions, with permissions the organisation controls. For Openlayer, the connection is made using an API key, so access is tied to a credential that the organisation can issue, limit and revoke.
Once connected, Claude can typically look through projects, check production monitoring data, review the results of evaluation tests and inspect model performance. A quality lead might ask, in plain English, which recent conversations scored poorly, and Claude can gather that picture from Openlayer without anyone writing a query by hand.
Permissions and approvals keep people in charge. We set access to the least that the job needs, so Claude sees only the projects it should. Consequential steps, such as changing a test configuration or acting on an alert, can be routed to a named person for approval first. Every action is logged, so the team can see what was asked and what was done.
What you can automate
Spotting regressions after a model update
When a team changes a prompt or swaps a model, a product manager can ask Claude to compare evaluation results before and after the change. Claude pulls the relevant test runs from Openlayer and highlights where quality has dropped, such as lower accuracy on a particular type of question, so the team can decide whether to roll back or fix forward.
Weekly production quality reviews
A customer experience lead can ask for a summary of how a support chatbot performed over the past week. Claude reviews production monitoring data and reports on trends, unusual spikes in failures and the topics that caused the most trouble, saving the team from building the report manually each Monday.
Investigating a customer complaint
When a customer reports a wrong answer from an AI assistant, a support manager can ask Claude to locate similar interactions and check how the model performed on them. This speeds up root-cause work and helps the engineering team decide which test cases to add.
Preparing for a release
Before launch, a QA lead can ask Claude to confirm which evaluation tests have been run on the latest build and which have not. Claude reports gaps in test coverage in plain language, so release decisions are based on a complete picture rather than memory.
Keeping stakeholders informed
Executives often want a short view of AI reliability without technical detail. Claude can translate the latest Openlayer results into a brief, non-technical update covering status, open concerns and next steps, which the project owner reviews before sharing.
An end-to-end workflow: Release quality gate for a customer support agent
TriggerA developer submits a new version of the support agent for release.
Claude runs a check in Openlayer to confirm that the standard evaluation tests have been executed on the new version.
Claude compares the new test results with the last approved release and flags any drop in accuracy or safety scores.
If results fall below the agreed threshold, Claude prepares a summary and routes it to the QA lead for approval in Microsoft 365 Teams.
Claude records the decision and creates a ticket in the team's ticketing system for any failing test that needs engineering work.
After release, Claude reviews the first day of production monitoring data and reports any unusual patterns to the project owner.
OutcomeReleases go out with a documented quality check, regressions are caught before customers see them, and the team spends less time collating results by hand. Typical outcomes vary by team and by how well the evaluation tests reflect real use.
How we set it up
Free automation audit
We review how your team tests and monitors AI today and identify the workflows, such as release checks or weekly quality reports, that are worth automating with Openlayer and Claude.
Secure API key connection
We set up the connection using an Openlayer API key, issued and held by your organisation. We limit its scope to the projects Claude needs and set up a process for rotating or revoking the key.
Workflow design
We design the end-to-end flow across Openlayer and your other systems, such as Microsoft 365, your CRM or your ticketing tool, and agree which actions need human approval.
Testing and launch
We test the workflow against real past releases and incidents, then launch with monitoring and audit logs turned on so you can see every action Claude takes.
Team training
We train your product, QA and operations staff to ask Claude questions, review its summaries and manage approvals, and we provide documentation for ongoing use.
Who benefits
- Machine learning engineers
- Quicker access to evaluation results and production data, without writing a query for every question.
- QA and release managers
- Consistent, documented quality checks before each release, with gaps in test coverage flagged early.
- Product and customer experience leads
- Clear, regular summaries of how AI features are performing in real use.
- Executives and risk owners
- A plain-language view of AI reliability, supported by the underlying evidence.
Security and control
- Access is limited to the projects and data each person or workflow needs, using a scoped API key.
- Consequential actions, such as changing tests or acting on alerts, require approval from a named person.
- Every request and action is logged, giving the team a full audit trail.
- API keys are stored securely, rotated regularly and can be revoked immediately if needed.
- Claude's summaries are reviewed by people before they inform release or customer decisions.
For teams in the US, UK, India and the Middle East
Organisations in the United States and the United Kingdom can host their Claude workflows in the US, the UK or the European Union, depending on their data requirements. In India, we align the setup with the Digital Personal Data Protection Act, 2023, and can host in India. Organisations in the UAE and Saudi Arabia are supported under their local data protection laws, with hosting region chosen to meet those requirements.
Learn more about Claude and MCP
Anthropic's AI model and assistant.
Secure links between Claude and business tools.
How engineers build and change software with Claude.
Hosted agents that run multi-step work.
The open standard Claude uses to connect to tools.
Frequently asked questions
What does the Openlayer MCP server do?
The Openlayer MCP server acts as a secure bridge between Claude and the Openlayer platform. It lets Claude manage projects, check production monitoring data, review evaluation test results and inspect model performance when asked in plain language, within the access the organisation has set.
Is connecting Openlayer to Claude secure?
The connection uses an Openlayer API key, which the organisation issues and controls. Access can be limited to specific projects, rotated on a schedule and revoked at any time. Every action is logged, and sensitive steps can require human approval before they run.
What can Claude actually do with Openlayer?
Typically, Claude can look up projects, review production data, check evaluation test runs and explain model performance trends. It can also help prepare summaries and route findings to the right people. The exact actions depend on the permissions your organisation grants.
How long does setup take?
Timing depends on how many projects, tests and other systems are involved. A focused connection with one workflow is often ready within a few weeks, including the audit, testing and team training. We confirm a timeline after the free automation audit.
Which plan or permissions do I need?
You need an Openlayer account with an API key that has access to the projects you want Claude to use. The exact account level depends on your Openlayer agreement, so we check your current setup during the audit and confirm what is required before any connection is made.