Autonomous AI discovery built for learning platforms.
Your platform already holds the enrollment events, lesson activity, completion records, engagement logs, and subscription state. DecisionBox runs discovery against all of it overnight — and ships a ranked list of findings by the time you sit down to plan the next cohort.
Built for VP Student Success, Heads of Growth, and Heads of Product at platforms where learners enroll, progress, and either finish or leave.
Your dashboard shows the average. Not the cohort underneath.
The patterns that move retention — the lesson that breaks a cohort, the acquisition channel feeding high-D1 / low-D30 learners, the flagship course quietly losing completion under a 12-course catalog average — only show up when enrollment, lesson activity, completion, and subscription state are read together.
DecisionBox runs them while you sleep. An autonomous AI agent picks what to investigate, runs the analysis, and re-checks every number against your data from a second angle before it ships. By morning you have a ranked list of findings — each with the affected count, the cohort, and a numbered recommendation.
How the agent picks what to investigateWhat stays hidden when the questions don't get asked.
Three benchmarks every learning-platform leader already accepts. The discovery loop runs against all of them in a single pass — and surfaces where your numbers diverge from the curve.
Three findings the agent surfaced on a learning platform's data.
One overnight run. The agent picked the analysis areas, ran them against the platform's data, re-checked every number, and ranked what to fix. Three shapes of finding — one cliff in a single course, one pattern across acquisition and retention, one anomaly the catalog average was hiding.
Point it at your platform data. See what the discovery loop ranks before your next cohort review.
Same data, five sets of questions.
One discovery run produces findings ranked for the team that owns the metric. Each panel is what the agent investigates in your data — questions it picks on its own.
Where the funnel quietly breaks.
Your acquisition dashboard shows volume by channel. It doesn't show which channels feed cohorts that activate and never return. The agent reads source quality against every downstream stage — and ranks where the volume is dying. Pair with Find growth opportunities.
- Which channels feed signups that never start lesson 1
- Where the funnel narrows between signup and first paid course
- Which segments show the steepest trial-to-paid drop-off
- Where landing-page variant × cohort retention diverges
- Which referrals carry the strongest 30-day stickiness
Three properties your next cohort review can defend.
DecisionBox is built so the findings on the cohort-review call are not the kind that fall apart under a follow-up question.
Sees what nobody had time to look for.
The agent picks the questions. It reads every cohort against every lesson, every channel against every retention curve, every flagship course against the catalog underneath. The investigations nobody had time to formally request happen anyway — overnight, ranked by morning.
Every number you'd defend in the cohort review.
An independent re-check runs every number from a second angle. If the agent can't reproduce its own claim, the finding is dropped or adjusted with the corrected number, visibly. Every step of the agent's reasoning stays attached to the finding for review.
Your learner data stays where it already lives.
Run DecisionBox on your own infrastructure or as a managed service — either way, student names, email addresses, and learning records never leave your environment. The platform is open source, so your team can see exactly how it works and what it touches.