Our whole operation, readable and writable by any AI model
An MCP server with 87 business tools over our projects, clients, pipeline, finance and team, so any LLM can work inside the business.
- AI agent
- In-house product
- In daily use since February 2026
At a glance
Client OutGrow
Client
OutGrow
Industry Business operations
Industry
Business operations
Year 2026
Year
2026
Status In daily use since February 2026
Status
In daily use since February 2026
Every case on this site answers the same four questions, so you can compare them.
Before
Projects, clients, invoices and tasks lived in separate tools. Every status update meant opening five tabs and asking three people.
What it does now
Projects, tasks, clients, the sales pipeline, invoicing and the team sit in one system.
An MCP server exposes 87 tools, so any LLM can read the data and make changes.
The day's agenda goes to an LLM, which turns it into tasks and suggests who should take each.
Imported files are parsed and routed to clients, projects, invoices or tasks.
Every morning an LLM writes a founder digest of what moved, what's late and what's owed.
Where a person decides
AI agents are registered like team members, and everything they do lands in the same audit log as a person's work.
Result
Business tools on the MCP server
Daily
Founder digest, written and sent automatically
This is how we run our own company. It's also the pattern we build for clients who want their agents to work inside their own systems.
How we built it
Built the operations app: projects, tasks, clients, pipeline, finance, attendance and a client portal.
Wrote the MCP server and its 87 tools, with a batch tool so an agent can make several changes in one call.
Added the LLM features: briefings, agenda-to-tasks, import parsing and the daily digest.
Set up model failover so a provider outage doesn't stop the work.
Stack
- React
- Vite
- Supabase
- MCP
- Vercel
- Resend
- LLMs with failover
More work like this
Every inquiry read, scored and routed, at any hour
BigDeal AI: a real estate CRM with an agent that asks before it acts
TeamBoard Studio: a whiteboard where the AI is a teammate
Your case study starts with an audit.
One to two weeks inside your operations. You get a map of where time and money leak, the return on each fix, and a fixed scope before we build anything.
Most businesses we meet have paid for an AI tool nobody uses. We start from your numbers, so the first build is chosen by payback.
Zain Khan and Anwar Sheikh
Founders, OutGrow
Outgrow
Intelligence
Studios.