AI systems for education.
The workflow pattern we see in this industry, and what we would build for it.
01
Application arrives
Applications, transcripts, and aid documents land in the queue. Staff check each file for completeness by hand.
02
Documents chased
Missing items are chased one email at a time. Applicants go quiet while they wait.
03
Questions answered one by one
Advising, registrar, and aid teams answer the same routine questions all day.
04
Risk found late
Struggling students surface at midterms. The engagement signals were there weeks earlier.
An enrollment and advising agent.
An agent that screens applications for completeness, chases missing documents, answers routine advising and aid questions, and flags at-risk students to a human advisor early. Your staff keep the final say on every admit and every intervention. The agent removes the queue, so people do the counseling.
10 to 14 weeks from kickoff to a live production system. We stay through adoption.
Eval harness
Checks agent outputs against a test set before release.
Reliability gates
Hard checks the system must pass before it ships.
Live production system
The working system in your stack, not a report.
Adoption Mile
To the 70 percent weekly-active bar by day 90.
Educator time on administration, not instruction
40 to 60 percent
IllustrativeHigher-ed students at risk of dropping out
15 to 25 percent
IllustrativeRetention lift from earlier intervention
Varies by institution
Illustrative
Ranges are industry observations for orientation. They are not ClearForge results. Our results live on the proof page.
See the proofStart where every engagement starts. The fixed-fee Diagnostic maps this workflow on your campus in 2 weeks.
Starting point · Forge Diagnostic