About
AI agent workers that operate the systems a business already runs.
AptiveAI gives a company agents that do the work inside its own software — on isolated computers the company can watch.
How we got here
AptiveAI started in 2024 as a connector layer: MCP servers and Azure Functions that let ChatGPT and Claude reach a customer’s internal systems. Getting into fleet software, yard systems, and business mailboxes turned out to be the whole job, and that part of the thesis held — the connectors are the hard part.
The surface was wrong. A chat window with extra context is still a chat window: it answers questions, and somebody else still does the work. What changed underneath us is that an agent harness can now drive a computer rather than only call an API. So the product became the harness — agents that own a computer, hold credentials, run on a schedule, and report what they did.
The connector layer did not go away. It became the tier the product consumes internally. Our reference customer, MS Cargo Express, runs trucking and logistics on three connected systems — fleet management, yard operations, and business email — with 32 tools live across them.
The problem we are solving
Small and mid-size businesses run on software with years of data behind it, and no path to using AI against it. The tools on offer either answer questions about data you paste in, or replace a system the company has no intention of replacing. Neither one checks trailer status in the yard system every morning.
AptiveAI closes that gap. An agent is a configuration — instructions, connectors, tool grants, guardrails, and a schedule — working on its own computer with a browser and a desktop. It does the work in the software the company already uses, and the customer watches it happen.
What we believe
Their systems, unchanged
No rip-and-replace. We connect to the fleet software, the yard system, the mailbox, the EHR, and the ERP a company already runs.
Isolation by default
Every agent works on its own computer, holding credentials only for the connectors it was granted. A mistake stops at that computer.
Show the work
The customer watches the agent’s screen while it runs, steers it mid-task, and reads a full transcript of every action afterwards.
Depth before breadth
We go deep in one industry before adding the next. The hard part is reaching software with no public API, and that work pays off once per industry.
Want to see an agent working in your systems?