Autonomous AI discovery built for telco.
Continuous AI discovery across your billing, CRM, network, and ticket 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 KPI deck, answered before your next month-end review.
Built for Heads of CVM, Heads of Network Operations, VPs of Revenue Assurance, and Heads of Data at network operators, MVNOs, and integrated carriers.
The cohort-level questions that never make it past the weekly KPI deck, running overnight against your data.
Your team owns the plan ladder, the network, the bill, the field operations. The agent runs unattended against your billing, CRM, network, and ticket data — and ships findings into a queue your team triages. Every claim carries the underlying queries and the data-validated number, not the CRM-reported or BI-dashboard-reported one.
See how the agent reads cohort × network × billAggregate ARPU looks fine. The cohort underneath is where the base actually moved.
Three benchmarks every CVM and network operations leader recognizes. Each points to the same gap: your data already holds the signals — ARPU cohort drift, network-tied churn, mis-classified downgrades — but they only surface when somebody asks the right question. Most months, nobody does.
Three findings the agent surfaced, paired with the recommendation it shipped.
Three shapes the CVM and network teams see every quarter: a single-cohort ARPU drift the aggregate concealed, a cross-system network-tied churn the regional dashboard rolled up, and a mis-classified churn pattern the CVM dashboard averaged into the wrong bucket. Same auditable trail every time — severity, the underlying queries, the cohort definition, the cost of the run.
Point the agent at your billing and network 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 cohort that quietly stopped growing, and the plan move behind it.
ARPU broken down by acquisition cohort, plan, tenure, and self-service path. The agent reads billing, plan history, usage, and self-service navigation together — and surfaces the cohorts whose ARPU shape changed before the month-end deck catches it. Splits drift into three causes: plan move, usage drop, and promo or credit decay. The deep dive lives in how the agent decides what to investigate.
- ARPU by acquisition cohort, plan, and tenure
- Family-plan dilution and add-line economics
- Unlimited-to-mid-tier downgrade pattern detection
- Handset-cycle revenue and upgrade-window drift
- Promo and credit decay impact on cohort ARPU
Built for operators 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 CRM, your billing system, your network-monitoring dashboard, or whatever data tools and BI you already standardized on.
Sees what nobody had time to query for
The agent picks which cohorts, plans, towers, blocks, and ticket clusters to investigate from your billing, CRM, network, and ticket 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 month-end 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 CRM-reported or BI-dashboard-reported numbers the data can't validate, no "trust the AI."
Reads your data, not just the CRM and the network-monitoring dashboard
Billing plus CRM, network telemetry, ticket data, MNP records, and provisioning logs — read together. The patterns the CRM can't see (because it only sees customer state) and the patterns the network-monitoring dashboard can't see (because it only sees alarms and telemetry) 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.