Data in one end. Finished work out the other.
A platform for running AI work, with permissions, budgets, schedules, audit trails, and failures you can actually see.
It's rarely the model.
The script works on someone's laptop. Nobody else can run it, and nobody should, because it holds the API keys.
Nobody knows what the AI spend will be until the invoice lands. There is no ceiling on it and no breakdown of it.
When a run dies at 2am it dies quietly, and you find out days later from the gap in the results.
There is no record of what ran or who it ran for, so it never gets past being a pilot.
Instead of one giant job, you get a sequence of small ones that each do a single thing.
A job brings raw material in: a search, an API, a spreadsheet, a mailbox.
A cheap pass discards what doesn't qualify, so costly work only touches survivors.
The survivors get the expensive treatment: full page reads, AI extraction, lookups.
Finished work leaves the line as a spreadsheet, a draft email, or a record in another system.
The stations never hand work to each other directly. They all read and write one shared, persistent store, and every record carries a status. A job's rule is: pick up everything marked X, do my part, mark it Y.
Which model runs at each stage is a setting you control, and the difference shows up in both the bill and the results.
A first-pass filter skimming thousands of short snippets wants a fast, cheap model. The stage that reads a full page and makes a judgment call wants a careful one. Because the work is split into stages, each one gets what it needs.
The stages that touch sensitive material can run on a model hosted on your own hardware, while the rest of the line uses a commercial provider.
The model is a setting on the job rather than something buried in code, so tuning a stage takes a minute in the interface and no deployment.
Cost is tracked per model and per job, so you can see what each stage is actually spending and move the ones that aren't worth it.
Your organization decides which models exist in the first place. A super admin registers the providers and models that are allowed, and no job can reach anything that isn't on that list.
Because the model is set per job, one department can run the same job against an in-house model while another uses a commercial one, without anybody forking the code.
Because the work lives in the store rather than in flight, you never have to start over. If too much junk is getting through, you tighten the filter and re-run just that stage against data you already paid to collect.
Everything a business needs around the AI, so the only thing left to build is your actual work.
It's the same platform underneath either way, and the only difference is what runs on it.
Prospecting is live today. It's a complete pipeline that finds organizations matching a description you write in plain English, filters out the noise, reads the survivors in full, pulls contact details, and drafts the first outreach email.
If your process isn't prospecting, the platform doesn't much care what a station does. We write the job that does your work and put it on the line, where it gets every control on this page automatically.
One prospecting run, start to finish.
searches, written by the platform from a plain-English brief
results collected, then read and sorted without anyone reading them
qualified organizations with a usable contact email
Most of what enters gets thrown away on purpose, which is how a funnel works. Nobody had to open 435 tabs to get to the 39, and every rejection is recorded, so you can see why something was dropped and change the rule that dropped it.
Setting up a pipeline by hand means writing search queries and two sets of AI instructions, and keeping four jobs consistent. The wizard replaces all of that with an interview.
Who are you trying to find? What makes one a great match? What should be thrown out on sight? You answer in plain English, and the AI asks follow-up questions where your answer would sharpen the search.
From your brief it writes the search queries, the sites to ignore, the quick reject rules and the detailed reading instructions. Then it runs some of those queries against live search results and scores how relevant they came back. If the score is poor it rewrites them and tries again.
You get a campaign folder with every stage configured and connected, plus a one-click button to run the full pipeline and another to wipe the data and start clean. You watch the build happen on a live checklist.
The interview, the drafting, the live test and the final review together take one to three minutes and cost a few cents in AI usage. The skill that used to live in the head of whoever set these up by hand is now in the wizard, so anyone on the team can start a campaign and the quality doesn't depend on who did it.
Pick the arrangement that matches how much your data is allowed to travel.
If you can run a small Linux instance, you can run Conveyor. It sits inside your network on your own hardware, and your data never leaves the building.
If you'd rather not run a server, we host your instance on a secure, dedicated deployment and handle the updates, the backups and the monitoring.
Point it at OpenAI or Anthropic, or at a model running on your own hardware. Files stay on local storage either way, because there is no cloud bucket anywhere in the design.
Anywhere the work is repetitive, high in volume, and mostly judgment calls about text.
The cases where we'd tell you to do something else.
A plain rule would do the job. If the test is "reject anything over $500", that's an if-statement. There's no reason to pay a model to do arithmetic, and a rule you can read is easier to trust than one you have to sample.
The answer has to be explainable. If you have to show an auditor or a regulator exactly why a decision went the way it did, a deterministic system you can point at beats a model you can only describe in general terms.
Being wrong is expensive. Where an error carries real regulatory or safety consequences, a stage that's right most of the time is a liability rather than a saving.
The process already works. If it's well structured and stable today, adding AI mostly adds cost, complexity and risk. When the change wouldn't materially improve the outcome, the right move is to stop.
Somebody is waiting for the answer. This is batch machinery that runs for minutes or hours. If a customer is at the other end of the request, you want an API call inside your application instead.
The volume is low. If you work through thirty of something a month, a person with a spreadsheet will be quicker to set up and cheaper to run than any pipeline.
If an off-the-shelf product already covers your workflow, buy it. And if what you want is answers out of your own documents, that's Docora, not this. The longer version of how we decide is in A Practical Decision Framework for Choosing AI Solutions.
If it's repetitive and nobody wants to do it, there's a good chance it belongs on a line.
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