Implement an MCP architecture across your company — in one month
In 4 weeks I roll out a swarm of in-house MCPs, plugins and skills that plug AI into your internal tools — so every team, technical and non-technical, can use AI to the fullest.
What I actually build
Not one MCP server, and not your product's backend. I implement an MCP (Model Context Protocol) architecture inside your company — a swarm of in-house MCPs, plugins and skills, each one plugging your AI (Claude, agents, copilots, IDEs) into a specific internal tool. Together they become the governed layer your whole company uses to make AI actually do things in your systems.
A swarm of in-house MCPs
One MCP per internal tool — logging, databases, APIs, CRMs, dashboards — so AI can read and act on live systems.
Plugins & skills
Reusable plugins and skills that package your workflows, so people trigger complex actions in plain language.
Governance & access
Authentication, role-based access and audit logging, so each team only sees and does what it's allowed to.
Handover & docs
A clean, documented pattern so your team keeps extending the swarm with new MCPs and skills without me.
A concrete example
Take customer service and your internal logging tools. Today, when a booking breaks, a support agent raises a ticket and waits for the tech team to dig through logs. With an MCP wired to your logging and observability stack, the support agent just asks the AI — and gets the exact error, the step that failed and the likely root cause in seconds. No ticket, no waiting on engineering. Now imagine the same for ops checking a payment, product pulling live usage, or sales checking an account — every team, self-serving through AI.
The one-month plan
- Week 1 — Discovery & architectureMap your internal tools, the highest-value use cases per team, and the permission model. Design the MCP swarm.
- Week 2 — First MCPs, plugins & skillsBuild the first in-house MCPs for your priority tools (e.g. logging for support), with auth wired in from day one.
- Week 3 — Expand the swarm & governAdd more MCPs, plugins and skills across teams, layer in role-based access and audit logging, and prove it with evals.
- Week 4 — Harden, roll out & hand overDeploy to your environment, add monitoring, onboard the teams, and hand over docs so they extend the swarm themselves.
Why this lifts the whole company, not just the tech team
The moment the swarm exists, the value compounds. Engineers get AI that reads and acts on real systems — but so do support, operations, product, sales and data teams. Instead of queuing behind the tech team with tickets and manual lookups, they self-serve through governed AI that can actually do things on live company data. That is where organisation-wide efficiency really comes from — democratizing AI access and removing the cross-team bottleneck.
Why me
I'm Vilva Athiban P B, a Lead AI Engineer at Omio, where I built the shared MCP services the whole company uses and drove AI adoption across the organisation — and single-handedly built the MCP-first backend of Omio.ai. So you get someone who has already stood up an MCP swarm in production and got real teams to adopt it — and who leaves your team able to run with it.
Frequently asked questions
What do you mean by 'implementing an MCP architecture'?
I don't hand you one server and leave. I roll out a swarm of in-house MCPs, plugins and skills across your company — each one plugging your AI (Claude, agents, copilots, IDEs) into a specific internal tool. Together they become the layer every team uses to make AI actually do things in your systems, not just chat.
Can you give a concrete example?
Customer service plus your internal logging and observability tools. Instead of raising a ticket and waiting for the tech team to investigate a broken booking, a support agent asks the AI — which uses an MCP wired to your logs to pull the exact error, the failed step and the likely root cause in seconds. Now multiply that across ops, product, sales and data teams.
Which teams benefit — only engineering, or non-technical teams too?
Both, and the non-technical teams are usually where the biggest wins are. Support, operations, product, sales and data teams get AI that safely reads and acts on the same live systems engineers use — so they self-serve instead of queuing behind the tech team. Efficiency lifts across the whole organisation, not just engineering.
What internal tools and systems can the MCPs connect to?
Logging and observability, internal APIs and microservices, SQL and vector databases, data warehouses, CRMs, ticketing, docs and knowledge bases, dashboards, CI/CD and SaaS tools — anything with an API. Each MCP, plugin or skill ships with authentication, role-based access and audit logging so people only see and do what they're allowed to.
How long does the rollout take, and what does it cost?
One month. Week 1 is discovery and design, weeks 2–3 build the first swarm of MCPs, plugins and skills for your priority use cases, and week 4 hardens, deploys and hands over. It's fixed-scope and fixed-price after a short discovery call, so you know the number and the deliverables up front.
Is it secure and production-ready?
Yes — that's the point. Everything is built for authentication and authorization, role-based access, audit logging, data isolation, schema validation and failure recovery, plus evals and monitoring so you can trust what agents do on live company data.
Should we build this in-house or bring you in?
Prototyping one MCP is easy; standing up a governed, multi-team swarm that non-technical people can safely use is a different project. I compress months of learning into weeks, ship the first high-value MCPs, and leave your team a clean, documented pattern to keep extending the swarm themselves.
Who are you and why trust you with this?
I'm Vilva Athiban P B, a Lead AI Engineer at Omio. I single-handedly built the MCP-first backend of Omio.ai, ship Agentic AI in production, and have taught 20,000+ developers with 50+ talks across 7 countries.