MCP Setup — Talk to Your Data via AI
Outcome, method, and proof
Three ways to evaluate whether this is the right service line for your team.
AI with structured, secure access
Your AI agents can read internal databases, query APIs, and act on business systems — scoped, audited, and revocable. No copy-pasting CSV exports, no exposing your stack to the open internet.
MCP servers as production infrastructure
We design the protocol, build the server, define permissions, set up observability, and document the runbook. Production-grade from day one, not a Saturday-afternoon demo that breaks under real load.
Anthropic ecosystem depth
MCP was authored by Anthropic. We work in the Anthropic ecosystem every day. When the protocol updates, we know about it before most teams do — because we’re shipping with it.
Three steps, weeks not months
Most MCP server engagements run 3–6 weeks from kickoff to production handoff.
Discovery
What data, what permissions, what use cases? We map the data your agents will reach, the actions they’ll take, and the security boundary that keeps everything else out of bounds.
Build
MCP server, scoped permissions, audit logging, observability. Built for production load, not demo traffic. Tested against your actual data, not synthetic fixtures.
Handoff
Documentation, runbooks, ownership transfer to your team. Optional ongoing support retainer if you want a Claude/MCP expert on call as the protocol and your usage evolve.
Where this fits — and where it doesn’t
Self-qualify before the call.
Best fits
High signal · book the call- Companies sitting on internal data they want AI agents to use
- Teams whose AI tools are blind to internal databases or APIs
- Security-conscious orgs where data exfiltration is a hard limit
- Engineering teams already running Claude (or planning to)
- Companies with structured data — Postgres, MySQL, Snowflake, internal REST APIs
Not a fit
Low signal · we'll redirect- Teams without a clear AI use case (build the use case first)
- Public-data-only scenarios (you might not need MCP at all)
- “Connect ChatGPT to our database” with no architecture (we don’t do shortcuts)
- Companies looking for a no-code data-to-AI plumbing tool
- Greenfield AI strategy work without a defined target system
Questions buyers actually ask
Q.01 What is MCP, exactly? +
Model Context Protocol is an open standard from Anthropic that lets AI models connect to tools and data sources through a defined interface. Think of it as the protocol layer that makes “AI agents that read your database” actually work in production — with permissions, audit logs, and revocable access — instead of being a security incident waiting to happen.
Q.02 Do MCP servers work with non-Anthropic models? +
Yes. MCP is an open protocol; any model client that speaks MCP can use the server. Claude has the deepest native support today, and increasingly other model providers and tools (OpenAI, Cursor, Cline, Continue) ship MCP client support too. Building on MCP doesn’t lock you into a single model.
Q.03 How do you handle authentication and permissions? +
Per-user OAuth or service-account auth, scoped to the minimum permissions the use case requires. Read-only by default; write access requires explicit scope and approval flow. Every action logged to an audit trail your security team can query.
Q.04 Can you integrate with our existing API gateway? +
Yes. MCP servers typically sit behind your existing gateway, inheriting its auth, rate limiting, and observability. We don’t replace your security stack; we slot into it. Discovery scopes the integration boundary in the first week.
Talk to a senior MCP engineer.
30 minutes. We’ll walk through your data, your use case, and whether MCP is the right tool for it — or recommend a simpler approach if it isn’t.