Rohit delivered our MVP in 5 weeks — on budget and ahead of schedule. His architecture decisions saved us from rewriting everything when we scaled.
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Connect Your AI Agents to the Systems That Run Your Business
Model Context Protocol (MCP) is how AI agents reach your APIs, databases, and internal tools. I design, build, and deploy production MCP servers — securely, with evals, inside your stack.
The Problem
Your team wants Claude, ChatGPT, or internal agents to actually use your systems — read the CRM, file the ticket, query the warehouse. MCP is the open standard that makes this possible, but most MCP servers stop at 'works on my machine': no auth model, no permission boundaries, no tests, no observability. Wiring an LLM to production systems without those is how companies end up in incident reviews. You need someone who has shipped MCP servers with real guardrails, not a first-timer learning on your infrastructure.
What You Get
- ✓Custom MCP servers connecting your agents to internal APIs, databases, and third-party tools
- ✓Security design first: authentication, scoped permissions, input validation, audit logging
- ✓Tool definitions engineered for reliable agent use — schemas an LLM cannot misread
- ✓Evaluation suite proving the agent uses each tool correctly before production
- ✓Deployment into your infrastructure (AWS, GCP, on-prem) with monitoring
- ✓Team handoff: documentation and a working session so your engineers own it
- ✓Advisory option: architecture review of MCP servers your team is already building
Tech Stack
MCPClaude APITypeScriptPythonOAuthDockerAWS
Timeline
First production MCP server: 1-4 weeks
Frequently Asked Questions
What is MCP and why does my company need it?
Model Context Protocol is an open standard (introduced by Anthropic, now adopted across the industry) that lets AI models securely call tools — your APIs, databases, file systems, SaaS products. Without it, every AI-to-system connection is a custom one-off integration. With it, you build one MCP server per system and every MCP-capable agent can use it. If you want AI that does things rather than just answers questions, MCP is the plumbing.
Why hire an MCP consultant instead of having my team build it?
Your team absolutely can build MCP servers — the protocol is open. What an experienced consultant adds is everything around the happy path: permission scoping so an agent can read but never delete, injection-resistant tool descriptions, rate limiting, audit trails, and eval suites that catch when a model misuses a tool. I've built these guardrails before, so you skip the expensive first-timer mistakes. I also offer a review-only engagement if your team builds and I audit.
Is it safe to give an AI agent access to production systems?
Only with boundaries — and that is most of the real work. Safe MCP deployments use scoped credentials (least privilege per tool), read-only defaults, human confirmation gates on destructive actions, input validation on every tool call, and logging of everything the agent touches. I design those boundaries first and demo the failure cases before anything reaches production.
Can you also build the agent, not just the MCP server?
Yes. MCP work is usually one half of a larger engagement: the server connects your systems, the agent uses them to do the job — triage tickets, reconcile invoices, draft responses. As a forward deployed engineer I build both sides and the evals that prove the loop works end to end.
Testimonials
What Clients Say
We needed a WhatsApp bot for our clinic chain. Rohit understood the problem immediately and shipped a working solution that our staff could use without training.