A model demo is easy. A production AI feature needs permissions, data boundaries, tools, structured outputs, evaluations, cost controls, failure handling, and a usable interface.
07 / MCP Integrations
MCP Integrations
Add dependable AI, agent tools, and MCP integrations to a product people already use.
Connect AI systems to CRMs, databases, business tools, and internal platforms.
We build the complete product path around the model. MCP is used where a standard tool and context layer improves interoperability; direct APIs remain appropriate when they are simpler and safer.
What the build includes
A complete production workflow.
AI feature architecture
Provider abstraction, model routing, context, tools, structured outputs, memory, permissions, and failure states.
MCP servers and clients
Secure MCP tools, resources, authentication, tenant boundaries, capability discovery, and audit logging.
Product interface
Streaming UX, history, approvals, citations, review, usage states, and clear error handling.
Production controls
Evals, observability, token and latency tracking, caching, rate limits, deployment, and documentation.
Technology and fit
Built with the right tools, not every tool.
The exact stack follows the workflow, security requirements, existing systems, and deployment environment.
Technologies
Model Context ProtocolOpenAIClaudeGeminiLangGraphPythonFastAPITypeScriptNext.jsDocker
Industries
Software companiesStartupsInternal product teamsMarketplacesDeveloper toolsEnterprise products
Expected outcomes
- →AI inside the real product
- →Reusable MCP tools
- →Provider flexibility
- →Measured model quality
- →Maintainable production code
Related work
What we have already shipped.
These examples show the type of workflow, product, or operating system this offer is built to solve.
Upwork MCP
Job context, relevant proof, proposal drafting, and follow-up in one review
An AIOVIX MCP workflow that connects a selected opportunity to the right service positioning, proof, draft, and follow-up.
Google Workspace MCP
Client brief assembled from Gmail, Calendar, and Drive
An internal client-operations MCP workflow that brings email, meetings, and documents together before the next conversation.
HubSpot MCP
Account history turned into a reviewable next action
An internal HubSpot MCP workflow that turns related account records into a brief and a proposed next action.
QuickBooks MCP
Invoice and payment context ready before the follow-up
An internal finance MCP workflow that gives payment follow-up the invoice and customer context it needs.
Common questions
Before we scope it.
What is MCP?
Model Context Protocol is an open standard for exposing tools, resources, and prompts to AI applications through a consistent interface.
Does every AI product need MCP?
No. MCP is valuable when tools or context should be reusable across clients and models. A direct internal API is often better for one narrow product integration.
Can you improve a product created with an AI coding tool?
Yes. We audit the code, preserve working product behavior, repair architecture and security issues, and introduce tests and release controls before expanding features.
Free Audit
Get Free Audit ↗Know what to build first.
We review the workflow, recommend the first build, and give you a rough price range and timeline before you commit.