AI-Powered Apps

The AI task someone does by hand, every single morning.

Someone on your team pastes support tickets into their AI to sort them, one at a time, before they can start their day. It works, and it will never stop needing them. On Runwork your own agent turns that into something that runs on its own, on your own AI provider keys, for the whole team.

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The Challenge

The AI part still needs a person to sit there and paste things in
It stops the moment that person is on holiday
Everyone has their own prompt for the same job, and they all differ
Nobody wants to hand a vendor the keys to their AI spend
The good prompt someone worked out is in a chat history nobody else reads

The Runwork Solution

Your own agent turns the manual step into something that runs by itself
It runs in the team cloud, on a schedule or on an event, without anyone watching
One version of the prompt, kept as a team skill, so everyone gets the same answer
Your own Anthropic, OpenAI or Google key, so the AI spend stays on your account
Every run is logged, so you can see what it decided and why

What your team gets

Ticket triage

Sorted, tagged and routed as they arrive, using the way your best agent already does it.

Document extraction

The fields you actually need, pulled out of PDFs, into records the team can query.

Draft replies

Written in your tone, waiting for a human to approve rather than a human to start.

Renewal risk scoring

Every account scored on a schedule, so success sees the list before the call.

Meeting notes into actions

Notes come in, the follow-ups land in the tracker your team already opens.

Search across everything

Ask across your own documents and records, and get an answer with its source.

A Day in the Life

8:00 AM: The queue is already sorted. Overnight, an agent your team put together triaged the tickets that came in: tagged by product area, urgency scored, the nine that need a person flagged with context attached. Support opens their day on a sorted queue instead of a pile.

9:30 AM: It uses your rules, not generic ones. The reason the triage is any good is that the way your best support person judges urgency is written down as a skill. They taught it once. Now every run uses it, and so does anyone on the team who asks their own AI about a ticket.

11:00 AM: Documents turn into records. A batch of signed contracts lands in file storage. An automation picks them up, pulls out the dates, values and renewal terms, and writes them into your records. Nobody retyped anything.

2:00 PM: Someone changes how it thinks. Support notices the urgency rule is too aggressive on billing questions. They tell their own AI, it updates the skill, and the next run behaves differently. No ticket to engineering, no prompt hidden on somebody's laptop.

Every night: it runs on your account. The AI calls go through your own provider key, so the spend and the data path are yours. Every run is logged, so when someone asks why a ticket was escalated, there is an answer.

Frequently Asked Questions

Does Runwork provide the AI model?

You bring your own. Save your Anthropic, OpenAI or Google key and every AI call your apps and agents make routes through it, on your account and your billing. That keeps the model choice, the spend and the data path in your hands, and it means switching providers later does not mean rebuilding anything.

Who writes these agents and automations?

Your own AI tool does. Someone describes the job to Claude, ChatGPT, Cursor or whatever they already use, and their agent builds it against your workspace and deploys it. Runwork is where it runs and where the whole team can reach it. There is no separate Runwork chatbot to learn.

How does an agent know how our business works?

Through skills. What one person teaches their AI about how you judge urgency, or what a good reply sounds like, gets saved once and used by every run and every teammate's AI. That is the difference between a generic model and one that answers the way your team would.

What stops it doing something we did not intend?

Agents run with the permissions of the workspace they live in, so you decide what data and which integrations they can touch. Every run is recorded in the audit log with what it did and when. For anything sensitive, put a human approval step in the workflow so nothing goes out unreviewed.

What happens when a better model comes out next month?

Point your key at it. The skills, records, integrations and automations your team built stay where they are, because they live in Runwork rather than inside one AI vendor. That is the whole reason to keep them somewhere neutral.

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