Automation and Integrations
Give AI safe access to your business tools with MCP
The Model Context Protocol lets AI assistants use your tools and data in a standard, permissioned way. We set up, build and secure those connections.
The problem
The problem this solves
AI assistants are only as useful as the information they can reach. On their own, they know nothing about your customers, orders, documents or projects. So people copy and paste context into chat windows, which is slow, error-prone and risky for sensitive data.
The Model Context Protocol, or MCP, is an open standard that solves this. An MCP server exposes a set of tools, such as "search customers" or "create a ticket", that an AI application like Claude or other MCP clients can call with tool calling. The AI decides when to use a tool; the server decides what the tool is allowed to do.
Many MCP servers already exist for common tools: CRMs, file storage, databases, project management, email and more. Our MCP server directory lists them. But connecting them safely to real business data needs care. Which tools should be read-only? Which actions need approval? Whose credentials does the server use? What gets logged?
We answer those questions for your setup. We configure existing servers where they are good enough, build custom MCP servers for your own systems, and add the safeguards that matter: least-privilege access, approval steps for writes, audit logs and clear separation between users.
We also set up MCP for development teams, connecting tools such as Claude Code to repositories, issue trackers and documentation so engineers work with better context. This is how we work ourselves.
A good first project is a read-only connection that answers real questions, such as "what is the status of this customer's orders?", before moving to actions. Our guide building an AI workflow with MCP explains the approach, and MCP security best practices covers the risks.
What you get
What you get
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Tool and data review
Which systems AI should reach, for what purpose, and with what limits.
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Existing server setup
Vetted MCP servers configured for your CRM, files, databases and tools.
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Custom MCP servers
Servers built for your own systems and APIs, exposing only the tools you approve.
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Least-privilege access
Read-only by default, scoped credentials and per-user permissions.
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Approval steps
Human confirmation before any tool that changes data or sends messages.
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Audit logging
Every tool call recorded with the user, inputs and results.
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Team rollout
Configuration for Claude Desktop, Claude Code or your own AI application.
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Usage guide
Example prompts and rules for your team.
How we build it
How we build it
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1
Pick use cases
We choose the questions and tasks where AI access to data helps most.
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2
Security design
Permissions, credentials, approval rules and logging agreed.
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3
Connect or build
Existing servers configured or custom servers built and tested.
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4
Pilot
A small group uses the setup on real work, with logs reviewed.
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5
Roll out
Wider rollout with a usage guide and ongoing monitoring.
AI and people
Where AI helps, where people decide
AI makes the repetitive parts faster. The decisions that shape your product stay with experienced people.
Where AI speeds things up
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Reading API docs and drafting MCP server tools.
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Generating tests for each tool, including misuse cases.
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Summarizing audit logs for review.
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Drafting example prompts and team guides.
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Comparing existing servers for your use case.
Where people decide
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Which systems AI may access at all.
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Which tools are read-only and which may write.
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Where a person must approve an action.
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Whether a community server is trustworthy enough to use.
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Who may use which connections.
Is this right for you?
When this is the right choice
A good fit when
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Your team uses AI assistants and pastes business data into them.
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You want AI to answer questions from live systems, not old copies.
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Your developers want AI coding tools connected to your repositories and tickets.
Consider something else when
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Nobody in the team uses AI assistants yet. Start with a pilot and training.
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The data is too sensitive for any AI access under your current policies.
Timeline and cost
What affects the timeline and cost
Configuring existing MCP servers for a pilot group can take days. Custom servers for your own systems, with approval steps and logging, usually take a few weeks.
We do not publish fixed prices because scope drives cost. How we estimate.
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Number of systems
Each connected system needs setup, testing and permissions.
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Custom servers
Systems without an existing server need one built.
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Permission model
Per-user access and approval flows add design work.
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Audit needs
Detailed logging and review dashboards add scope.
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Rollout size
More users means more configuration and training.
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Security review
Sensitive systems need deeper review and testing.
Keep exploring
Related services, solutions and reading
Related services
View all related services- Automation and Integrations CRM, payment and API integrations, MCP connections and workflows that stop people copying data by hand.
- AI agents AI that takes actions in your systems, such as qualifying leads or processing requests, with people checking the results.
- SaaS Product Development MVPs, subscription platforms, admin panels and multi-tenant products, planned to grow past the first launch.
- Multi-tenant architecture One product serving many customer accounts, with each account's data kept separate and secure.
- CRM integration Connect your CRM to forms, email, billing and support so customer records stay complete without typing.
- Database design Data models that stay fast and correct as your business grows, with backups and access rules in place.
Solutions
View all solutions- Knowledge assistant (RAG) Ask a question in plain words and get an answer from your own documents, with links to the sources.
- Automated reporting Reports that build themselves from your systems on schedule, with a plain-language summary of what changed.
- AI sales research agent Short, sourced briefings on each prospect before a call, drafted by an AI agent from public information and your CRM.
- Internal help desk assistant An assistant in Slack or Teams that answers policy and how-to questions from your handbooks and opens tickets when needed.
- AI lead qualification Score, research and route every new inquiry so your team talks to the best leads first.
- AI automation and integrations Connect the tools you already use so data moves once, correctly, and AI reads the parts that arrive as text or documents.
Industries
View all industries- SaaS and startups MVPs, subscription billing, AI features, multi-tenant platforms and scaling support for founders and product teams.
- Professional services Client portals, proposal drafting, knowledge assistants and internal tools for consultancies, agencies and firms.
- Finance and accounting Client portals, document collection, invoice and receipt processing, and reporting for accounting firms and finance teams.
Case studies
View all case studies- Payroll system for an IT services company Payroll is calculated from recorded inputs, reviewed once and sent as payslips in a single batch, with every change logged.
- Tax filing service platform Clients upload documents and see where their case stands, and staff work from one queue with deadlines and reminders.
Guides and articles
View all guides and articles- What are AI agents and how do businesses use them? What AI agents are, real business uses, the controls they need and how to start with one safely.
- What is the Model Context Protocol? A business guide to MCP: what it is, how MCP servers work, who supports it and how to start using it.
- How to connect AI to your CRM Connect AI assistants to HubSpot, Salesforce or other CRMs safely, with the tasks worth automating first.
- How to connect AI to your database safely Let staff ask questions of your data in plain English without risking production systems or sensitive records.
- How to build an AI workflow with MCP servers Plan, connect and test an AI workflow that uses MCP servers, from a single task to a reliable process.
- MCP security best practices How to choose, configure and monitor MCP servers so AI assistants get the access they need and nothing more.
AI models
View all ai models- Embeddings Models that turn text into vectors for search, retrieval, clustering and recommendations.
- Claude Opus 5.5 Anthropic's recommended starting point for most serious work: long-running agentic coding and knowledge work, with a 1M token context window.
- Gemini Embedding 2 Google's current embedding model, multimodal: it embeds text, images, video, audio and PDFs for search and retrieval.
- Llama 4 Maverick The larger Llama 4 open-weight model, with image input and a 1 million token context window, for self-hosted assistants and analysis.
- text-embedding-3-large OpenAI's most capable embedding model for search and retrieval, with 3,072-dimension vectors and support for English and other languages.
- text-embedding-3-small OpenAI's efficient, lowest-cost embedding model, with 1,536-dimension vectors for search, retrieval and similarity at scale.
MCP servers
View all mcp servers- Databases Servers that let AI query and, where allowed, change data in SQL, NoSQL, analytics and vector databases.
- CRM and sales Servers that connect AI to CRM and support platforms, to look up customers, update records and prepare follow-ups.
- Productivity Servers for notes, tasks, projects, calendars, spreadsheets and design tools used across the business.
- Browser automation Servers that let an AI agent open web pages, click, fill forms, take screenshots and extract data from sites.
- Developer tools Servers for code hosting, version control, files, containers, error tracking and documentation lookup.
- Search and web Servers that give AI live web search, page fetching and research tools, so answers can use current information.
Glossary terms
View all glossary terms- Model Context Protocol The Model Context Protocol, or MCP, is an open standard for connecting AI assistants to external tools and data in a consistent way.
- MCP server An MCP server is a program or hosted service that exposes a system's data and actions as tools that AI assistants can use through the Model Context Protocol.
- Tool calling Tool calling is when an AI model asks the application to run a tool, such as a search or database lookup, and then uses the result in its answer.
- Claude Code Claude Code is Anthropic's agentic coding tool, which works in a developer's terminal, IDE, desktop app or browser to read code, run commands and make changes.
- AI agent An AI agent is software that uses a language model to plan steps, call tools and act toward a goal, instead of only answering one question.
- Embeddings Embeddings are lists of numbers that represent the meaning of text or images, so software can find items that are similar in meaning.
FAQ
Questions about MCP integration
Have a question that is not here? Ask us directly.
MCP is a standard way for AI assistants to use tools and data, like a universal adapter between AI and your software. Read what MCP is for a plain-English explanation.
It can be, with the right limits: read-only access where possible, scoped credentials, approval for any changes and full logging. We design those controls before connecting anything.
Claude Desktop and Claude Code support MCP, and a growing number of other AI applications and frameworks do too. Support changes quickly, so we confirm current options for your setup.
Yes. If your system has an API, we can build an MCP server that exposes approved actions to AI. See API development if you need an API first.