The customer-data layer for AI agents

Customer data for AI agents — one MCP for your whole stack.

Your customer lives in 6–10 tools. Pathbound connects them once and serves your AI agents one real-time, identity-resolved profile per customer — over the Model Context Protocol and REST, with governed write-back. One MCP for the whole customer stack, not one per tool.

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The problem

An agent is only as good as the context it gets.

The model isn’t the hard part — the data is. It lives in your CRM, your help desk, your billing tool, your inbox, your product, and your warehouse. Each models the same person differently. Stitching it into one real-time, LLM-ready profile takes weeks to build and breaks the moment a vendor changes their schema. So most agents act on a fraction of the picture: one tool, one tab, one stale export.

Why Pathbound

Not a connector. The layer underneath your agents.

A single-tool MCP shows your agent one app. A warehouse gives you yesterday’s data, read-only. Pathbound is the operational customer-data layer — three things neither of those can do:

Real-time

Reads reflect the live state; webhook sources stream in near real-time. An operational agent needs the customer as they are now — not as they were at last night’s batch load.

Governed write-back

Agents don’t just read — they update contacts and companies and log notes, fanned out across your sources. Versioning, rollback, and a full write-audit trail are rolling out — the governance layer compliance reviewers expect.

Identity-resolved

[email protected] in your CRM, [email protected] in Gmail, “Jane R.” in support — resolved to one entity, across every source. A direct connector only knows its own IDs.

Switch agents tomorrow. Add a new tool next quarter. The customer profile stays the same.

How it works

Connect once. Ask anything.

  1. 01

    Connect your sources

    OAuth your CRM, help desk, inbox, billing, enrichment, and the built-in tracker. Reads are breadth-driven — every source you add makes the profile richer.

  2. 02

    Pathbound resolves identity

    Every record is merged into one profile per customer — email, phone, and external IDs reconciled across sources, anonymous web activity stitched on at sign-in.

  3. 03

    Point your agent at one MCP

    Add https://mcp.pathbound.ai/mcp to Claude, ChatGPT, Cursor, n8n, or any MCP client — or call the REST API. One endpoint, your whole customer stack.

Integrations

Plug in your stack. We resolve the rest.

Every record from every tool is identity-resolved and merged into the right contact — automatically. The dedup and stitching you’d otherwise hand-build, done for you.

HubSpot CRM

Sync contacts, companies, and deals between HubSpot and Pathbound.

read write
Pipedrive CRM

Bring Pipedrive deals and contacts into Pathbound profiles.

read write
Salesforce CRM

Sync Salesforce Contacts and Accounts onto one identity-resolved profile per customer.

read
Zoho CRM CRM

Pull Zoho contacts and accounts into Pathbound profiles, identity-resolved with the rest of your stack.

read write
Creatio CRM

Sync Creatio contacts and accounts into one identity-resolved profile.

read
Apollo Enrichment

Enrich contacts and companies with firmographic data.

read write
PostHog Analytics

Sync PostHog persons and product events onto the unified contact timeline — identity-resolved with the rest of your sources.

read
Gmail Email

Pull threaded email history into the contact-level timeline.

read write
Intercom Support

Surface Intercom conversations on the unified contact timeline.

read
Zendesk Support

Stitch Zendesk users, organizations, and tickets onto the unified customer profile.

read
Calendly Scheduling

Pipe scheduled meetings and cancellations into every customer profile in real time.

read
Resend Email

Send transactional email enriched with Pathbound context.

read write
All integrations →
Safety primitives

Compliance, privacy, safe writes — built in.

Versioned writes

Shipping soon

Every change snapshots the resolved entity and the raw source state before fan-out. Roll back by ID, time range, or actor.

Full audit trail

reads · writes shipping

Every read and write logged with actor, timestamp, payload, propagation, and result. The audit trail SOC 2 and GDPR auditors look for — without you stitching it together.

Safe updates

Shipping soon

Schema validation, dry-run mode, idempotency keys, per-source rate limits. Agents can't double-send or write malformed records.

Approval gates

Roadmap

Human-in-the-loop on flagged fields, segments, or action types. Useful for high-stakes ops: deletions, mass updates, large-scale outbound.

GDPR / CCPA fan-out

Shipping soon

One call propagates a deletion or redaction across every source where the customer's data lives. Right to be forgotten, made real.

Conflict resolution

Shipping soon

When sources disagree, Pathbound is the source of truth. You set the merge rules; we enforce them on every read and every write.

Every customer in Pathbound carries their full history. Versioning, audit, and rollback come built in.

FAQ

Customer data, MCP, and AI agents.

What is a customer data MCP?

A Model Context Protocol (MCP) server that gives AI agents live access to your customer data. A single-tool MCP exposes one app (just your CRM, say). Pathbound is a customer-data MCP: it unifies CRM, support, billing, email, and product behaviour into one identity-resolved profile per customer, then serves that to Claude, ChatGPT, Cursor, or any MCP client through one endpoint.

What does "customer data for AI" actually mean?

Agents are only as good as the context they get. "Customer data for AI" means taking the fragmented record of a customer — scattered across 6–10 tools, modelled differently in each — and turning it into one real-time, token-efficient, LLM-ready profile an agent can read (and write back to) in a single call.

How is this different from a data warehouse or reverse-ETL?

A warehouse is great for analytics — batch-loaded, queried with SQL, read-only, hours stale. That is the wrong shape for an operational agent acting on a customer right now. Pathbound is real-time, identity-resolved, and supports governed write-back to the source tools. It is the operational layer, not the analytics layer.

Which AI agents and clients can connect?

Any MCP-compatible client: Claude.ai, Claude Desktop, Claude Code, ChatGPT (custom connectors), Cursor, Cline, Continue, plus agent frameworks like n8n, LangChain, and the Vercel AI SDK. There is also a plain REST API for services that do not speak MCP.

Is the data real-time?

Reads reflect the latest synced state, and sources that support webhooks stream in near real-time. This is the key difference from the warehouse pattern, where data is hours behind — an operational agent needs the customer as they are now, not as they were at last night’s batch load.

Can agents write back, or just read?

Both. Beyond reading the unified profile, agents can update contacts and companies and manage notes, and changes fan out to the connected sources. Schema validation, versioning, rollback, and a full write-audit trail are rolling out as the governance layer — the controls SOC 2 and GDPR reviewers expect.

What is a "customer context layer"?

The same idea by another name. A customer context layer sits between your AI agents and every tool that holds customer data, so each agent reads one coherent profile instead of stitching six APIs together mid-run. Pathbound is that layer, delivered over MCP and REST.

Try it on your own data

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

Sign up. Drop in the tracker snippet. Connect one CRM. Point your MCP client at mcp.pathbound.ai/mcp.

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