Merge for AI

AI tool that allows anyone to contribute to Merge's Unified APIs and accelerate integration development

Merge for AI uses artificial intelligence to change how we build integrations, allowing anyone to contribute to Merge’s Unified APIs and accelerate integration development.

As a user, you have to input the documentation URL for an API and Merge for AI will automatically generate the API definition (“‘blueprint”) to Merge’s Common Models. You then have an opportunity to request Merge for AI’s output be added to Merge’s own Unified APIs.

Using AI, the system automatically shows how third-party endpoints and fields are standardized to Merge’s Common Models in just seconds — which is one of the first steps when they build a new integration. This speeds up the development of new integrations through Merge’s platform – on top of more than 170 that are already supported.

Finally, if you can’t make the integration work for yourself, there is an option to submit a request and Merge’s engineering team will evaluate and determine, based on a variety of factors, on how to prioritize building that integration. Pretty cool!

Homepage Screenshot 📸

Video Overview 🎬

What are the key features? ✨

Normalized Data: Delivers consistent data models from 220+ connectors to enable reliable embeddings and accurate retrieval in vector databases.
Access Control Lists (ACLs): Automatically syncs user permissions from source systems, particularly for file storage and ticketing, to enforce data boundaries.
Complete Data Flexibility: Allows retrieval in original formats, custom field mapping, reading/writing non-standard objects, and authenticated passthrough API requests.
Maximum Data Throughput: Optimizes syncing to handle rate limits and large volumes efficiently, achieving higher performance than typical in-house integrations.
Merge Agent Handler: Provides secure tool access, monitoring, and governance for AI agents interacting with third-party services.

Who is it for? 🤔

Merge for AI helps product engineering teams at B2B SaaS companies, AI startups, and enterprises that build AI-powered features such as search, agents, or knowledge assistants and need to incorporate customers' third-party data securely and at scale. It suits organizations that want to avoid the heavy lift of custom integrations across many tools while maintaining compliance, performance, and data accuracy.

Examples of what you can use it for 💡

AI Startup Founder: Builds a RAG-powered enterprise search product that pulls and indexes customer documents from file storage and knowledge bases with proper permissions.
Product Engineer at SaaS Company: Creates an AI agent that reads and acts on tickets from multiple helpdesk systems while respecting agent-specific access controls.
Developer at Compliance Tool Provider: Enables AI-driven insights by syncing normalized HRIS and ERP data for analysis without manual schema mapping.
Team Building Knowledge Assistant: Powers personalized recommendations by retrieving and embedding customer CRM and project management records consistently.
Enterprise AI Search Developer: Launches a secure, boundary-aware search experience across file storage and ticketing tools with automatic ACL enforcement.

Pros & Cons ⚖️

Normalized data improves embedding quality
Automatic ACL syncing ensures compliance
High throughput reduces latency
Single API covers 220+ connectors

Setup requires initial mapping effort
Churn handling retains credentials until revoked

FAQs 💬

What exactly does Merge for AI do?

It acts as a unified data integration layer that lets AI applications securely access and use normalized customer data from over 220 third-party tools through one API.

How many connectors does Merge support?

Over 220 connectors across categories like file storage, ticketing, HRIS, CRM, ERP, project management, and knowledge bases.

Does Merge handle user permissions automatically?

Yes, it syncs Access Control Lists (ACLs) from source systems, especially for file storage and ticketing, to enforce proper data access.

Is Merge suitable for building AI agents?

Yes, Merge Agent Handler lets AI agents securely call tools while Merge manages authentication, rate limits, monitoring, and errors.

How does data normalization help AI applications?

It creates consistent structures that produce better embeddings and more accurate context retrieval from vector databases.

What security standards does Merge meet?

It offers SOC 2 compliance, HIPAA eligibility, granular controls, audit trails, and role-based access management.

Can I access custom fields or non-standard objects?

Yes, Merge supports mapping custom fields, reading/writing outside standard models, and making passthrough requests to underlying APIs.

How does Merge compare to building integrations in-house?

It syncs data more efficiently (claimed 4.7x) and reduces maintenance, letting teams focus on AI logic instead of plumbing.

Who typically uses Merge for AI?

AI startups, B2B SaaS teams, and enterprises building search, agents, or knowledge assistants that rely on customer third-party data.

Is there a way to test Merge before committing?

Yes, you can request a demo or use their documentation and sandbox to link test accounts and evaluate data flow.

Ready to try Merge for AI?