Give your AI agents evidence they can act on.
Connect ChatGPT, Claude or any other AI agent or assistant to Laup through the Laup MCP server. Let them analyze visibility data, surface likely root causes, perform work across the tools you give them and verify the result against the underlying evidence.
14 days. No credit card.
Agent run
Improve visibility in one category
- 1
Inspect
Category visibility is below the leader
Robot vacuums · ChatGPT and Claude
- 2
Diagnose
Comparison sources name three alternatives more often
Evidence linked to 18 captured answers
- 3
Act
Add the category comparison action
Action plan · Added by your agent
- 4
Verify
Measure the same questions after publication
Category, engine and source trend
Connect from
- ChatGPT
- Install the Laup plugin from ChatGPT's directory and sign in. Nothing to copy. Open Laup in ChatGPT
- Claude
- Add Laup as a custom connector in Claude, then sign in and approve access.
- Any other agent or AI assistant
- Claude Code, Codex, Cursor or anything else that speaks MCP: create a token in Laup and add the server to its config.
Each one needs an active Laup trial or plan with at least one tracked domain.
What your agents get from Laup.
The connection carries the evidence, actions and measurement loop already inside your Laup workspace.
- Your visibility data
- Read category performance, engine differences, tracked prompts and the answers behind every result.
- Sources and context
- Inspect citations, grounding sources and the context an engine exposed while answering.
- Likely root causes
- Compare visibility, competitor gaps, source patterns and readiness findings before deciding what to change.
- Action plan control
- Add evidence-backed action cards, move them through the plan and join a card's discussion. Every change is signed with the agent's name.
- Work across your tools
- Let an agent use Laup evidence while it performs the underlying work through the other systems you connect.
- Verification
- Return to the affected categories, answers and sources to test whether the result changed after the work shipped.
Analyze. Act. Verify.
Agents can move faster without hiding the boundary between a measured fact, a likely explanation and a completed action.
- 01
Analyze
Give an agent the measured answers, visibility, source context and readiness evidence behind the question.
- 02
Act
Add new work or change a card's status in Laup, then perform the underlying change through the tools and permissions you provide.
- 03
Verify
Check the claim against the recorded evidence and watch the affected category and engine after the change.
Need someone to build the agent first? We work with Jenz, who build AI teammates for commerce teams in Norway. They take it from there.
The boundary stays visible.
- Every read and write stays scoped to the company and domain the agent is authorized to use.
- Every change an agent makes carries its name on the card, and your team can move a card back or dismiss one it added.
- Claims can be checked against captured answers, citations, grounding sources and dated measurements.
Put your visibility data to work.
Give your agents the context to diagnose, the tools to act and the evidence to verify.
14 days. No credit card.