One layer between any AI agent and every customer tool.

Pathbound collects customer data from the tools you already run, resolves it into one real-time profile per person and company, and serves it to Claude, ChatGPT, or any AI agent over MCP and REST. Here’s how each stage works.

Collect

Every signal your customers give you lands in a different tool.

Pathbound pulls them into one place as they happen. Connect your CRM, help desk, email, and billing once, and each source syncs continuously over OAuth and webhooks instead of on a nightly batch. Events covers the half no integration sees: what people do on your website, including visits from before they signed up.

Resolve

Every tool keeps its own copy of the same customer.

Pathbound resolves them into a single contact, rolled up to a single company. Records match by email, external IDs, and the site’s visitor trail, then merge with per-field provenance, so you can see exactly which source set a value. The pages someone read before signing up attach to their contact the moment they identify.

Act

Letting an agent loose on your customer stack is the part that feels risky.

Your agent connects over MCP (the open protocol agents use to call tools); your backend can read the same data over REST. Reads cover profiles, timelines, and conversation history. Writes go through governed tools such as updating a contact, logging a note, or managing segments, and each tool can be allowed or blocked for your workspace. Agent responses come with PII redaction, and a usage log records what each agent touched. Cross-source write-back is rolling out.

See what agents run on this: recipes with one real job, the exact tool calls, and a tested prompt
Try it on your own data

Within five minutes, your agent is querying real customer context.

Sign up, connect one CRM or drop the events snippet, and point your MCP client at mcp.pathbound.ai/mcp.