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An AI assistant connected to the Leadspace MCP server enriches a list of company domains with industry, employee count and revenue

Article

MCP for RevOps: 28 Use Cases, Examples and MCP vs API

MCP for RevOps means running data, list, export and admin work from an AI assistant, using natural-language requests instead of clicks and scripts. With the Leadspace Sidekick MCP server connected, Claude, ChatGPT, Gemini, Cursor or any MCP-compatible assistant can enrich accounts, check records against your ICP, move lists to CSV or your CRM, track credit usage and manage users, all inside your existing Leadspace permissions.

An AI assistant connected to the Leadspace MCP server builds and sizes a B2B segment from a plain-language prompt, returning 382 matching companies

Article

MCP for Marketing: 27 Examples for B2B Marketers

MCP for marketing means connecting your AI assistant to live B2B data, so you can build, size and enrich segments by asking in natural language. With the Leadspace Sidekick MCP server connected, Claude, ChatGPT, Gemini, Cursor or any MCP-compatible assistant can find accounts that match your ICP, count how many fit, map the buying group at each one and enrich your inbound leads, all in one conversation.

An AI assistant connected to the Leadspace MCP server searches for VPs of Sales at US fintechs and lists matching buyers with email and phone available

Article

MCP for Sales: 28 Use Cases and Examples for Sales Teams

MCP for sales means connecting your AI assistant to live buyer data, so you can prospect, prepare for calls and find contact details by asking in natural language. With the Leadspace Sidekick MCP server connected, Claude, ChatGPT, Gemini, Cursor or any MCP-compatible assistant can search Leadspace's B2B data, map the buying group at an account, pull a person's recent LinkedIn posts and reveal a work email or phone number, all in one conversation.

Learn how to audit duplicate CRM records, apply data governance rules, and maintain CRM data quality with enrichment tools.

Article

How to audit and fix duplicate CRM records in 2026

Your CRM is supposed to be the system of record for your entire go-to-market operation. In practice, it often becomes a graveyard of duplicate contacts, mismatched accounts, and stale fields that no one trusts. When that happens, every downstream system that depends on CRM data starts making bad decisions.

Scoring models weight the wrong signals. Routing sends leads to the wrong reps. Segmentation breaks. Campaigns reach the same buyer five times across three different records. The problem is not that your team is careless. The problem is that CRM data quality issues compound fast, especially when you are pulling data from multiple sources and running enrichment at scale.

This guide walks through how to find the root causes of duplicate records, build governance rules that hold, and maintain data validation and cleansing as an ongoing operation rather than a quarterly fire drill.