AI AGENTS
Agent22
Agent22 lets people train a personal AI agent on their own work. You connect your Google Workspace and Slack, point it at the files and notes you already have, and it keeps learning as that data changes. The result is an agent that knows your business the way you do, not one that answers from the public web.
Industry
AI / Personal Agents
Status
Early access
The challenge.
A general chatbot does not know your property listings, your case files, or last week's pricing sheet. To get useful answers, people end up copying and pasting context into a prompt over and over, and the moment a document changes the agent is out of date again. Agent22 needed to flip that around. The agent had to connect directly to where the work already lives, read it, and stay current on its own, so a real estate broker or a law firm could train an expert on their own material without managing any of the plumbing.
What we built.
We built Agent22 as a platform that ingests a user's own data and trains a specialized agent on top of it. Users connect Google Workspace and Slack, upload files in bulk, and the system syncs in real time so the agent always reflects the latest version of every source. Under the hood it embeds and indexes that material into a vector store, so answers are grounded in the user's documents, emails, and messages rather than guessed.
Connect, train, stay current
An agent trained on your own data, kept live
The product centers on one idea: train your own agent on anything. Users connect Gmail, Drive, Docs, Sheets, Slides, Calendar, and Contacts through Google Workspace, link Slack, and upload files directly. From there the agent answers from that material, and real-time sync across every connected source means it updates the moment a file or message changes, so it never falls out of date. The marketing site walks through concrete examples, from a broker training on property listings and sales scripts to a law firm training on case briefs and filing deadlines, and a tiered plan covers how much training data and sync each user needs.
- Google Workspace sync across Gmail, Drive, Docs, Sheets, Calendar, and Contacts
- Slack sync and bulk file upload for everything else
- Real-time context syncing so the agent reflects the latest version of every source
How we built it.
The path from first conversation to a production system.
Define the data connectors
We mapped the sources people actually keep their work in, Google Workspace and Slack first, and designed the auth and ingestion around them.
Build ingestion and sync
We built the pipeline that pulls in files, emails, and messages, then keeps them current with real-time syncing instead of one-off imports.
Ground the agent
We embedded and indexed each user's material into a vector store so the agent answers from their own documents, not the open web.
Package and launch
We shipped the waitlist and early-access experience with tiered plans for training-data limits and sync depth.
The results.
What Agent22 delivers in production, and the core stack it runs on.
Specialized agents trained across industries on users' own data.
Connected sources stay current so the agent never answers from stale files.
Google Workspace, Slack, and direct file upload feed a single agent.
Responses come from the user's own documents, emails, and messages.
Next.js
React
TypeScript
Tailwind
Node.js
PostgreSQL
Redis
OpenAIHave a project like this?
Tell us what you're building. We'll come back with a clear plan, a timeline, and the team to ship it.
