OpenAI & Anthropic Integrations
We build OpenAI and Anthropic integrations that make your data available through AI marketplaces and create new revenue channels. From legacy APIs through security review to marketplace approval, end to end.
The Shift
Users increasingly ask an AI assistant instead of navigating portals, dashboards, and search interfaces. An MCP connector puts your data directly inside that workflow — so instead of digging through your platform, they ask:
The assistant retrieves the right data, applies your business rules, and delivers a useful answer.
Two Ways to Use MCP
Make your proprietary data, catalog, research, or analytics available to customers through the leading AI platforms:
Give executives and employees a secure way to ask business questions across CRM, ERP, finance, and internal systems:
More Than an MCP Server
We connect your existing APIs, databases, and legacy systems, and translate your domain expertise into tools and rules the AI assistant can use reliably.
Secure authentication, subscription and role-based permissions, and controls over what data can be retrieved, by whom, and under what conditions.
We prepare and submit your connector to ChatGPT, Claude, and Microsoft Copilot, and guide it through their technical and marketplace reviews.
Onboarding flows, example prompts, and documentation for your users — plus usage analytics that show which questions people ask and what to build next.
Most first implementations take 8–12 weeks, depending on the condition of your existing APIs, the number of data sources, and the security requirements.
Why Node8
You end up with a working AI interface for your company’s data, running in the platforms where your customers and employees already spend their day.
By industry
We catalogued all 2,289 apps in the public ChatGPT directory. Each category page lists every one of them, plus what companies in that vertical typically expose to an assistant.
Related reading: ChatGPT app development for the OpenAI-specific path, where an MCP server can be distributed for how ChatGPT, Claude, Copilot, and Gemini differ, and The State of ChatGPT Apps for what the full directory shows about who is building and where the gaps are.
FAQ
An MCP (Model Context Protocol) connector makes your data available inside AI assistants like ChatGPT, Claude, and Microsoft Copilot. Instead of logging into your platform, learning its interface, and exporting data, users ask questions in the assistant they already use — and the connector retrieves the right data, applies your business rules, and returns a useful answer.
Most first implementations take 8–12 weeks, depending on the condition of your existing APIs, the number of data sources, and the security requirements. Marketplace review at Anthropic, OpenAI, and Microsoft adds time on top, so we start submissions early.
Cost tracks scope rather than a fixed price list. The variables that move it are how many backend systems the connector reaches, how much work the underlying APIs need before they can support it, whether it only reads or also writes to production systems, and how much security and compliance review your category attracts. A single-source read-only connector is a materially smaller project than a multi-system one with write actions. We scope it concretely in a discovery session before any build commitment.
Data and API architecture, business logic and tool design, authentication and role-based access control, security and privacy architecture, marketplace submission to ChatGPT, Claude, and Copilot, user onboarding materials, and usage monitoring. The protocol layer is a small fraction of what makes a connector succeed.
Yes. MCP is an open standard: one server serves Claude, ChatGPT, Microsoft Copilot, Perplexity, and any other MCP-compatible client. You build the integration once and distribute it everywhere.
Every connector ships with secure authentication, subscription or role-based permissions, and explicit controls over what data can be retrieved, by whom, and under what conditions — designed around your governance and compliance requirements before launch, not bolted on after.
Financial data providers, research platforms, healthcare technology companies, marketplaces, automotive platforms, real estate databases, travel platforms — any company that owns valuable structured data its customers would want to query from an AI assistant, or that wants employees to query internal systems conversationally.
In a strategy session we will work out the best first use case, which data to expose, whether your current APIs are sufficient, and which AI platforms to prioritize.