Autonomous AI discovery built for media & streaming.
Continuous AI discovery across your subscriber, viewing, billing, and content data. Every finding ships with the underlying queries, the cohort definition, and the cost of the run. The cohort-level questions that never make it past the weekly subscriber deck, answered before your next subscription review.
Built for Heads of Subscriber Growth, Heads of Retention, VPs of Content Strategy, and Heads of Analytics at streaming video, audio, and digital subscription services.
The cohort-level questions that never make it past the weekly subscriber deck, running overnight against your data.
Your team owns the content slate, the pricing, the acquisition mix. The agent runs unattended against your subscriber, viewing, billing, and engagement data — and ships findings into a queue your team triages. Every claim carries the underlying queries and the data-validated number, not the billing-system-reported or SDK-reported one.
See how the agent reads content × subscription behaviorThe subscriber count looks fine. The cohort underneath is where the book actually moved.
Three benchmarks every streaming retention and content leader recognizes. Each points to the same gap: your data already holds the signals — content-driven retention, trial conversion by source, plan-mix economics — but they only surface when somebody asks the right question. Most weeks, nobody does.
Three findings the agent surfaced, paired with the recommendation it shipped.
Three shapes the subscription team sees every quarter: a single-source trial conversion cliff the aggregate concealed, a cross-system content-driven retention pattern the dashboard couldn't see, and a plan-mix mirage the annual share averaged into a win. Same auditable trail every time — severity, the underlying queries, the cohort definition, the cost of the run.
Point the agent at your subscriber data for a week. Triage what it finds Monday morning.
Five surfaces the team already owns, one continuous discovery.
Same engine, same data, five categories of work your team is already doing — or wants to do, if the queue ever clears. Pick the surface that matters this quarter.
The trial cohort that signed up and never opened the app.
Trial-to-paid conversion drift by acquisition source, plan offered, content surfaced, and onboarding cohort. The agent reads trials, first-session viewing, billing events, and content recommendations together — and surfaces the cohorts whose conversion shape changed before the weekly subscriber deck shows it. The deep dive lives in how the agent decides what to investigate.
- Trial-to-paid conversion drift by acquisition source and creative
- First-session engagement as a conversion predictor
- Onboarding-step drop-off (account creation → first watch → second session)
- Plan-selection patterns across trial cohorts
- Win-back conversion scoring for re-engaged trialists
Built for streaming services who want a cohort analyst running overnight, not three dashboards to reconcile.
Three things every finding from DecisionBox carries. None of them changes how your team works in your subscription billing system, your product analytics SDK, your content recommendation surface, or whatever data tools and BI you already standardized on.
Sees what nobody had time to query for
The agent picks which cohorts, acquisition sources, content titles, and plan segments to investigate from your subscriber, viewing, billing, and engagement data. Findings ship into a queue your team triages in the morning. The cohort-level question stops sitting in the data waiting for the next QBR.
Every number you'd defend at the subscription review
The underlying queries the agent ran, the cohort definition, the data window, the cost of the run, the validation checks. Your team re-runs anything, extends anything, overrides anything. No black boxes, no billing-system-reported or SDK-reported numbers the data can't validate, no "trust the AI."
Reads all your data, not just the billing system and the analytics SDK
Subscription billing plus viewing, content metadata, marketing spend, and engagement — read together. The patterns Recurly, Chargebee, or Stripe Billing can't see (because they only see subscription state) and the patterns Mixpanel or Amplitude can't see (because they only see events) surface when your data is read end-to-end. Snowflake, BigQuery, Databricks, Redshift, PostgreSQL. Open source, AGPL v3. The agent meets you on the stack you already run.