OpenAI Automation
You have nine hundred support transcripts in a folder and a question you can only answer by reading all of them.
Batch work like this is exactly why the API exists, and exactly why you never get round to it. AI agents drive your OpenAI account for you — transcribing audio, embedding a folder into a vector store, generating images, pushing a batch job through — and report the size and cost before anything runs at scale.
You
Ask in plain language, get the real numbers
Your agent queries OpenAI itself rather than guessing, and shows you what it ran.
Connect OpenAI
One click, read access first. Your agent can see the same data you can and nothing you can't.
Ask the question you actually have
No query language and no dashboard hunting. The agent works out which OpenAI data answers it and goes and gets it.
Check the working, then share it
You get the answer along with what it queried, so you can sanity-check the number before it goes in front of anyone.
What your agent can pull from OpenAI
- Transcribe audio and generate speech from text
- Embed documents into a vector store and search across them
- Generate and edit images from a written brief
- Run and monitor batch jobs over a large input set
- Screen text through moderation before it gets used anywhere
Reporting that stops landing on you
The reports someone rebuilds every week, assembled from the source each time.
Your agent transcribes each file, reports total duration and cost before starting, and returns text you can actually grep.
Your agent embeds the set into a vector store, searches it for the question you asked, and quotes the passages it drew the answer from.
For a large input set, your agent submits a batch, tracks it to completion, and tells you what it cost — rather than you polling for status.
Three things worth asking on Monday morning
Turn a folder of recordings into searchable text
Your agent transcribes each file, reports total duration and cost before starting, and returns text you can actually grep.
Answer a question across hundreds of documents
Your agent embeds the set into a vector store, searches it for the question you asked, and quotes the passages it drew the answer from.
Push a batch job through and watch the spend
For a large input set, your agent submits a batch, tracks it to completion, and tells you what it cost — rather than you polling for status.
Teams without an analyst on call
Support and research teams sitting on recordings
Hours of calls contain the answer to a question you keep guessing at. Transcribing and searching them is a two-hour scripting job you have deferred four times; your agent treats it as one request.
Small teams without an ML engineer
You want embeddings over your own docs, not a chatbot someone else hosts. Your agent builds the vector store, loads the files, and searches it — no notebook, no dependency install, no key in a dotfile.
More integrations in AI
If your stack has more than OpenAI in it, an agent can work across the rest of it too.
Ahrefs
A competitor takes your best page's position and nobody notices until the quarterly review, because checking means logging in and clicking through four reports.
Google Analytics
You want last month's organic traffic by landing page, and twenty minutes later you are still fighting the dimension picker.
Metabase
There are four questions called “Revenue”, and the one feeding the exec dashboard is the one nobody remembers writing.
OpenAI questions, answered
What people ask before putting an AI agent to work — answered straight.
That is the point of the connection. Transcription, embeddings, image generation, and batch jobs all become requests you make in a sentence, so the one-off data job stops needing a throwaway script and a virtual environment.
Ready to automate OpenAI?
Connect your tools, put an agent to work, and approve what matters. Free to start.