For department leads and administrators
Turn your workflow into an AI agent
Tell us how the work gets done today, in your own words. We turn it into an agent that runs on the systems you already use, and you keep every bit of control.
- About 10 minutes
- No technical knowledge needed
- One workflow per form
ibl.ai turns a department's everyday workflow into an AI agent from a plain-English description: you tell us what kicks the work off, where the information lives and what done looks like, and our engineers connect the agent to those systems in place. The agent runs on your own infrastructure or ours, on any model, and with ibl.ai you own all the code and the data.
How we work with your team
How does ibl.ai learn your workflow and build with your team?
Seven stages, from the form below to the next workflow. The people who do the work today take part in every one of them.
- 1
You describe one workflow
Pick the one your customers, patients, students or staff would feel first, and tell us how it runs today in your own words.
- 2
We read it and reply within one business day
A person reads every submission and comes back with two or three follow-up questions or a short call invitation.
- 3
A 30-minute call with your team
We walk through the workflow with you and whoever owns the systems, agree what the agent should and should not do, write down the number that will show it worked, and name the person on your side who will run it.
- 4
We build on a safe copy of your data
The first version reads your systems in place through connectors, in a test copy wherever one exists, and the person who does the work today tries it before it touches anything live.
- 5
Your team sets the bar
You write down questions with the answers your team agrees are correct. Every version of the agent is scored against them, and one below the bar does not go live.
- 6
You run it
The agent runs in your environment, and with ibl.ai you own all the code and the data. Skills are plain-English files your team edits without waiting on us, and we train the person who runs it.
- 7
The next workflow
The connectors, the test set and the skills carry over, so the second workflow starts further along than the first, and your team can lead it.
Questions your team will have
What will your team want to know before an agent joins it?
Straight answers to the questions we hear from every department, so nobody is surprised later.
Question 1
“Where do we start?”
With one workflow, the one where people would feel the difference first. Run it, learn from it, then decide what comes next.
That is why the form takes one workflow at a time, and why the best first candidates are frequent, rule-bound work.
Question 2
“Can't we just build this ourselves?”
For reports, research and drafting, often yes, and we encourage it. The hard part begins when the work touches a system of record, personal data or payments, or has to run every day without someone watching it.
That is what ibl.ai's engineers build with your team: connectors scoped to each person's role, with every action logged.
Question 3
“How will we know it worked?”
By a number your team names before anything is built: requests answered within a day, hours per onboarding, days to close. The form asks for it, the call writes it down, and we read it together after the first month.
Question 4
“How do we know the AI is right?”
Your team writes a test set: questions with the answers it agrees are correct. Every version of the agent is scored against it, a version below the bar does not ship, and every step you mark for approval stays with a person.
Question 5
“Who owns it and keeps it running?”
You do. On ibl.ai you own all the code and the data: the agent runs in your cloud or on-premise, and its skills are plain-English files your team can read and change.
We name the person on your side who will run it before we start, and we train them before handover.
Question 6
“Is our data safe?”
It stays in your environment: your cloud, on-premise or fully air-gapped. Each agent can redact, mask or block personal information before the model sees a message, and Incognito keeps a conversation out of history and memory.
Ask us where the data goes, who can see it and what the model provider keeps, and we answer all three in writing.
Question 7
“Do we have to fix our data first?”
Only the data the first workflow touches. Connectors read each system in place, scoped to each person's role, so no warehouse project comes first, and the next workflow reuses them.
Question 8
“What happens to our people?”
They lead. The person who does the work today describes it, tries the first version and approves what goes live. Skills are written in plain English, so the people who know the process edit them, and a hands-on workshop teaches your team to build the next agent.
Question 9
“Which model should we use?”
Whichever fits today; you will change it. ibl.ai is model-agnostic, so an agent picks from dozens of providers and a switch keeps its prompts, data and connectors. What your team keeps is the workflow, the test set and the data.
Question 10
“Do I need to involve IT before I submit?”
No. Describe the workflow as you know it and name the systems in your own words. We ask IT the technical questions later, and the form has a place to tell us who to ask.
Question 11
“What if the workflow spans several systems?”
That is the normal case. Each system gets its own connector and the agent works across them, following the eight-step process on the architecture page.
Question 12
“Will the agent act on its own?”
Only where you say it may. Every step that needs a person's approval stays a human step, and the form asks you to name those steps so the first version is built around them.
Question 13
“What happens if it works?”
Your team decides who runs it, who improves it and what to do with the time it gives back. Analytics show usage and cost per agent, and the next workflow starts from the connectors and skills already built.
What will you be asked?
Four short sections. Every question has an example beside it, and nothing asks for a system's technical name.
Section 1
About you
So we know who does the work and who can say yes.
Example answer: Dana Reyes, Registrar's Office, Lakeview University. I manage the people who do it.
Section 2
The workflow
A step-by-step story, as if you were training a new hire, plus how often it happens and what goes wrong.
Example answer: Transcript requests: 40 a week, about 15 minutes each. A request arrives by email, we look the student up, check holds, generate the PDF and send it.
Section 3
Where the information lives
Which systems, files and tools the workflow touches, and how people reach them today.
Example answer: Banner for the record, a shared Excel on SharePoint for the log, Outlook for the request itself.
Section 4
Rules, risk and success
Which steps need a person's approval, whether anything is sensitive, the one number that will tell you the agent worked, and the person on your side who will run it.
Example answer: A manager approves anything with a hold. Student records, so FERPA. The number is requests answered within a day; today it takes about three. Sam Ortiz, our office manager, will run it.
How can ibl.ai reach the information your workflow uses?
Every system can be reached one of five ways. You do not need to know which applies, but if you do, say so: it saves a meeting. Our engineers confirm the rest with your IT team.
| Route | In plain words | What to tell us | A sign it exists |
|---|---|---|---|
| API | The system offers a door for software to read and write through, with your permission. | The vendor's name and whether anyone has used its API before. | A vendor page called Developers or Integrations; an app already connected to it. |
| Database query | Your IT team can ask the system's database directly, usually read-only. | Who on the IT team runs reports or queries today. | Reports that arrive from IT as spreadsheets nobody clicked through a screen to make. |
| Scheduled export or data dump | The system drops a file on a schedule: nightly, weekly, at term end. | What the file is, where it lands and how often. | A CSV or Excel that appears in a shared folder every morning. |
| Browser automation | There is no door, so the agent logs in and clicks the same screens a person would. | Which screens, and whether a second sign-in step is involved. | A vendor portal with a login and no export button. |
| Staging or test environment | A safe copy of the system to practice in before anything touches live data. | Whether one exists and who can grant access to it. | An address with test, staging or sandbox in it that IT uses before upgrades. |
Whichever route applies, the data is read in place through a connector scoped to each person's role. Nothing is copied into a warehouse first. See the eight-step agentic development process and the Ontology connectors that do this work.
Which workflows make good first agents?
The best first agent is a workflow that is frequent, well understood by the person who does it, and bounded by a clear rule for when a human must step in.
Registrar
Transcript and enrollment verification requests
Frequent, rule-bound, and the record already lives in the SIS.
Human resources
New-hire onboarding paperwork
The same ten steps for every hire, across HRIS, email and the equipment desk.
Finance
Invoice matching and vendor questions
Three-way matching is a lookup, and the exceptions are what people should see.
IT
Help-desk ticket triage
Categorize, look up the account, answer the known half, route the rest.
Research or operations
Grant and compliance reporting
Pulls the same figures from the same systems every quarter.
Clinical or client services
Intake scheduling and reminders
Calendar, record and message in one loop, with a person approving changes.
What happens to the information you share?
Your submission is read by the ibl.ai team only and used to scope the agent. Do not paste passwords, personal records or anything you would not email a colleague; names of systems and a description of the work are all we need.
When the agent is built, it runs where you choose, in your cloud, on-premise or fully air-gapped, and with ibl.ai you own all the code and the data: the skills your team writes, the connectors and the agent itself sit in your own environment, not inside a vendor's product.
Describe your workflow
One workflow per form. If the form does not load below, open it in a new tab. Open the form in a new tab
Prefer to talk it through?
Book a 30-minute call and describe the workflow out loud; we take the notes.