Computer use you can run every week. At a fraction of the cost, the same way, on the record.
What this means for your back office: the work that takes your team hours every week runs on its own, at a cost you can see on every run. The month-end reconciliation comes out the same way every month, because code does the matching and checks every change. And every run leaves a record you can check step by step, the way you’d review an employee’s work.
General-purpose AI agents use a computer by taking a screenshot, deciding where to click, and repeating. That works for a one-off task with someone watching. For work that runs every week in your accounting software, it is too slow, too costly and too unpredictable. Here is what Fleet AI does differently.
The cheapest reliable path first.
Every screenshot a general agent reads costs time and money, and it reads one for every click. Fleet AI works down this list and only reaches the screenshot when nothing above it will do.
- 1
The app's own tools
Built-in actions for Microsoft Access, Sage 50 and Excel. Fleet reads an Access form's rows and totals directly, in about half a second, and addresses each row by number instead of hunting for it on screen.
- 2
The names Windows gives every control
Windows publishes the name of every button, field and menu. Fleet clicks "Export" by name, not by guessing which pixels it occupies.
- 3
The text of pages and files
PDFs are read from their text layer, so amounts come out exactly as printed. Web pages are read as text too.
- 4
The screenshot, last
When nothing else will do, the AI looks at the screen. On a general agent, this is the only way it works.
Fewer, cheaper AI steps.
- Many actions per AI step: up to eight fields filled in one go, rows ticked in batches.
- Only the current screen goes to the AI, not every screenshot taken so far.
- The parts of the request that repeat every step are cached, so most of it is billed at a fraction of the price.
- A strong model writes the plan, and a faster, cheaper one does the hands-on work.
- A run that can't continue, for example because the computer's session dropped, stops at once instead of retrying for forty steps.
Sage 50, Microsoft Access and Excel have built-in Fleet actions, the fastest and cheapest way to work. Every other Windows or web app is worked through control names and the screen, which takes more steps. For an app you run every day, ask us about built-in actions.
The AI decides. Code does the work.
A general agent works out every click from scratch on every run, so two runs of the same task can go two different ways. In Fleet AI the AI reads the task and writes the plan, and tested code does the parts that have to come out exactly right.
Known tasks run as tested steps
For tasks with a built-in routine (a Sage 50 payroll report, an invoice draft), one AI call reads the details from your request, then a fixed list of steps runs with no AI in the loop. If a step doesn't check out, the routine stops and hands the task back.
Matching is done by code
Pairing bank lines with ledger entries is done by code, so the same statement and ledger always give the same matches. Lines it can't pair for certain are flagged for you, never guessed.
Statements must balance
For a long bank statement, the AI writes the rules for reading that layout and code applies them line by line. The result is only used if opening balance + credits − debits = closing balance.
Every change is checked
Each ticked Access row is checked against the form's own running count. The customer on a Sage 50 invoice draft is read back before the draft is handed to you. Before reporting "done", the AI has to point to the evidence for each part of the task.
Save, Post and Delete wait for you
Built-in routines and saved shortcuts never save, post or delete on their own. Those steps stop and ask. In your own workflows, you decide where a person signs off.
What worked becomes a shortcut
A run that succeeded can be saved as a shortcut that replays its steps, with each click tied to the control's name. A shortcut that fails three times in a row is retired.
What stays flexible: the AI still reads each task and writes a plan for it, so a workflow copes when a screen moves or a document looks different. You approve the plan before anything happens, or let workflows you trust run on their own schedule. Runs on the same computer go one at a time.
Every run, on the record.
With a general agent, what happened is whatever the chat transcript says. Fleet AI writes down every run as it goes, so you can check it, and so can your own AI.
What each run keeps
- The plan, and who approved it
- Every action, with how long it took and whether it worked
- A screenshot of each step, with the clicks marked on it
- The AI's notes on what it was doing
- Every checkpoint, hand-off and blocked action
- What each step cost, and which AI model did it
- The files it produced, and a plain-English summary
You review it
One page per run: step through it screenshot by screenshot next to the list of actions, see where the time and money went, and download what it produced. A run that went well can be saved as a workflow in one click.
Your AI reads it
An AI app connected over MCP can start a run, then read a short digest of what it did and what went wrong, look at the screenshot of any step and fetch the files it produced. It works from the record instead of guessing. Connect your AI →
What isn't stored
Your passwords: they are typed straight into the app and never logged. The AI's private reasoning: you see its notes and its actions, not its thoughts.
Real runs, not estimates.
Lower cost on an Access bank reconciliation. A screen-only AI agent ticked 7 of about 87 rows before it was stopped; Fleet AI finished the whole reconciliation, correctly.
Fewer AI steps to tick five rows on the same Access screen: 2 instead of about 10, done in 21 seconds. The screen-only agent also ticked one row twice.
To tick 84 rows, each one checked against Access's own running count.
Faster renaming 64 drawings in a construction takeoff app: 7.4 seconds with no mismatches, against nearly 13 minutes for an AI agent clicking through the screen.
Single runs measured on Fleet AI’s production platform, August–October 2026. The screen-only agent and Fleet AI used different AI models.
Not tied to one AI company.
Fleet AI works with models from Anthropic, OpenAI and Google, and many others through OpenRouter. A strong model writes the plan and a faster, cheaper one does the hands-on work. You can choose the model for each of those roles in Settings, and a workflow can use its own.
Bring us the task your team hates most.
15 minutes. If it’s a fit, we build the first workflow with you, and you judge the result.
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