> ## Documentation Index > Fetch the complete documentation index at: https://docs.flokitai.com/llms.txt > Use this file to discover all available pages before exploring further. # What FloKit does > Understand FloKit's role in the growth stack, what data it connects, and how teams use recommendations safely. FloKit is a growth intelligence layer for consumer subscription apps. It connects acquisition spend, attribution, subscription revenue, product events, and paywall outcomes so growth teams can understand which cohorts are paying back and what action to take next. Use this guide when you need a practical overview before starting setup, inviting teammates, or explaining FloKit to growth, data, finance, or product stakeholders. FloKit growth loop connecting acquisition, conversion, pricing, and retention ## The short version FloKit helps answer four operating questions: | Question | What FloKit uses | What the team gets | | ------------------------- | -------------------------------------------------------------------- | --------------------------------------- | | Which cohorts pay back? | Revenue, attribution, spend, identity mapping | CAC, ROAS, LTV, and payback by cohort | | Where is spend leaking? | Campaign, ad set, creative, country, offer, and source data | Prioritized waste and opportunity areas | | What should we do next? | Payback curves, funnel events, guardrails, and confidence thresholds | Action queue recommendations | | Can automation stay safe? | Spend caps, excluded campaigns, approval policies, rollback triggers | Human-reviewed or controlled execution | FloKit does not replace your MMP, subscription source, warehouse, or ad accounts. It sits above them and joins the pieces into one payback-aware operating view. ## Core concepts ### Payback, not just installs Install volume can look healthy while revenue quality is weak. FloKit evaluates cohorts by the time it takes subscription revenue to cover acquisition cost. That gives growth teams a more durable signal than CPI, trial starts, or day-1 conversion alone. ### Identity stitching Subscription, attribution, and product systems often use different identifiers. FloKit joins anonymous IDs, app user IDs, subscription customer IDs, and MMP customer user IDs so revenue can be connected back to acquisition source. ### Action queue FloKit turns payback findings into recommended actions. Examples include budget shifts, creative pauses, audience exclusions, offer tests, and paywall experiments. Each recommendation includes the reason, expected impact, confidence, and guardrail checks. ### Guardrails Guardrails define what FloKit is allowed to recommend or execute. They include spend caps, excluded campaigns, minimum confidence thresholds, approval rules, and rollback triggers. Guardrails should be configured before write access is enabled. ## What FloKit connects FloKit data fabric connecting acquisition, the FloKit agent, and revenue truth | Area | Examples | Why it matters | | ------------------------- | ------------------------------------------------------- | -------------------------------------------------------------------- | | Subscription and revenue | Stripe, App Store Connect, Google Play | Builds observed revenue, renewal, refund, and churn history | | Attribution and MMP | AppsFlyer, Adjust | Connects installs and campaigns to subscription outcomes | | Ad platforms | Meta, Google, TikTok, Apple Search Ads | Adds spend, bids, budgets, campaigns, ad sets, and creative metadata | | Product and funnel events | Paywall views, trials, purchases, onboarding milestones | Explains why cohorts convert or churn | | Warehouse | BigQuery, Snowflake, Redshift | Lets finance-approved data become the reporting source of truth | ## What teams use FloKit for ### Growth * See CAC and payback by campaign, country, creative, and offer. * Find budget waste that is hidden by blended ROAS. * Review recommendations before changing live spend. ### Data * Validate source mappings and identity joins. * Compare FloKit outputs with warehouse or finance reporting. * Monitor data quality issues before teams act on reports. ### Product * Understand how onboarding, paywalls, trials, and offers affect retained revenue. * Prioritize tests that improve payback, not only conversion. * Keep pricing and paywall changes under human control. ### Finance and leadership * Read payback windows and cohort LTV in terms that map to spend decisions. * Review whether acquisition growth is creating durable value. * Set operating thresholds before scaling. ## Recommended first workflow Start with App Store Connect, Google Play, or Stripe. Revenue is the anchor for every payback report. Add AppsFlyer or Adjust so FloKit can join installs and campaigns to revenue outcomes. Confirm that user, subscription, and attribution IDs join cleanly before trusting campaign-level payback. Compare FloKit metrics against your source of truth and resolve gaps before making decisions. Configure spend caps, excluded campaigns, confidence thresholds, and rollback rules. Start with read-only recommendations. Approve, reject, or defer actions until the team trusts the signal. ## What FloKit does not do by default FloKit does not automatically change pricing, rewrite paywalls, modify subscription offers, or exceed budget guardrails. Write access must be configured explicitly, and teams should begin in read-only mode. ## Where to go next * [Customer onboarding](/guides/customer-onboarding) * [Set up FloKit without an app release](/guides/no-app-release-setup) * [Get your first payback report](/guides/first-payback-report) * [Set safe guardrails before automation](/guides/safe-guardrails)