Model and MCP integration: Claude, GPT, Gemini and open-weight models without lock-in
Connecting leading models, including Claude, GPT, Gemini and open-weight options, to your software through standard connectors, without tying you to one vendor.
Typical balance
35 human : 65 AI
AI-run, reviewed. Varies by engagement.
Your people keep choosing models on cost, privacy and quality, deciding what data each system may see and security review.
AI takes on routing each task to a suitable model, reading and writing through Model Context Protocol connectors and evaluating outputs automatically.
What is model and MCP integration?
Model and MCP integration connects large language models such as Claude, GPT and Gemini to your own software and data, using the Model Context Protocol and similar standards so the models can read information and take actions securely. It is built to be model-agnostic, so you can switch providers as the market changes.
Also known as LLM integration, GPT integration, Claude integration, Model Context Protocol and MCP server development
Who needs model and MCP integration?
Companies that want AI inside the tools they already use, and those who built on a single model provider and now want flexibility.
A good fit when: you built on one model last year and the ground has shifted.
How model and MCP integration works
Choose models
We compare models for each task on quality, cost, speed and privacy terms.
Connect
MCP connectors give models scoped access to your core systems.
Route
Each task is sent to a suitable model, so you are not paying top prices for simple work.
Evaluate
Automated tests check output quality, so a model change never goes live unnoticed.
What you get
- Model-agnostic architecture
- MCP connectors for your core systems
- Evaluation suite
- Cost and usage controls
How long does model and MCP integration take?
A first integration in three to six weeks.
Common questions about model and MCP integration
What is the Model Context Protocol (MCP)?
MCP is an open standard, introduced by Anthropic, for connecting AI models to external tools and data sources. It lets one connector work across many AI applications instead of building a separate integration for each.
Should we use one AI model or several?
Usually several. Different models are better and cheaper at different tasks, and a model-agnostic setup protects you when a provider changes prices or terms.
Is our data used to train the AI model?
That depends on the provider and plan. Business and API plans from major providers generally exclude your data from training, and we check the terms as part of choosing models.
