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Industries · SaaS & B2B

Autonomous AI discovery built for SaaS & B2B software.

Run an autonomous AI discovery on your product, billing, and CRM data — find the renewal drifting, the expansion ninety days old, and the activation step nobody owns, before the next QBR.

Built for VPs of Product, VPs of Growth, CROs, and CS leadership at PLG, sales-led, and hybrid SaaS companies.

CRITICALCohort drift
Mid-market NRR fell 11pp over three cohorts.
118% → 107% NRR across Q1–Q3 cohorts · 14 accounts…
From a real DecisionBox run
DecisionBox for SaaS

Three systems, one customer journey, one discovery loop.

Your product, billing, and CRM data already holds the answer to most of the questions worth asking — where revenue is leaking, why the metric moved, and which opportunities are sitting unattended. DecisionBox runs them overnight. The agent decides what to investigate, writes the queries, validates every number against your data, and ships ranked findings that reconcile to the deck Finance signs off on.

See how every number gets checked
The cost of missed insights

The questions nobody had time to ask are the ones costing renewals.

Three benchmarks SaaS leaders are quietly paying right now. Each one points to the same gap: the data already has the answer; nobody has time to ask. Every quarter of waiting compounds into revenue you can’t recover.

Capacity gap
70%+
Of data-team hours go to stakeholder tickets, not exploratory analysis. The interesting questions never get asked.
Locally Optimistic + dbt Community surveys, 2024
Detection window
2 quarters
Typical lag between a retention or expansion signal appearing in the data and the team acting on it. Most renewals don’t wait that long.
Reforge State of Growth, 2025
Forecast pull
1 in 3
B2B SaaS finance teams missed their NRR forecast last fiscal year. The signals were sitting in the data. The bandwidth to investigate them wasn’t.
Bessemer State of the Cloud, 2025
From real runs

Three findings, three actions, one overnight pass.

Three pairs the agent surfaced in real SaaS discoveries. Each finding ships with a numbered recommendation attached — not a chatbot you have to drive. See how the autonomous discovery loop produces ranked, validated outputs.

HIGHActivation cliff
Paid-search single-seat workspaces skip the week-1 invite and churn at 3.4× the rate.
3.4× churn · 1,840 workspaces · 21% retention
Product-events scan
ship this one first
P1Recommendation
Invite nudge
Trigger a day-5 invite nudge for paid-search single-seat workspaces;
Expected impact
+8–12pp day-30 retention · 1,840 workspaces
Target: paid-search single-s…2 concrete actions
CRITICALCohort drift
Mid-market NRR fell 11pp over three sequential cohorts.
11pp NRR drop · 3 cohorts · 14 seat-capped accounts
Product usage + billing events + CRM activity read…
cross-system pattern
P1Recommendation
Expansion sweep
Run an expansion sweep on the 14 seat-capped mid-market accounts before next renewal;
Expected impact
+4–7pp NRR · 14 accounts · 90-day window
Target: mid-market seat-capp…2 concrete actions
MEDIUMHidden conversion gap
Aggregate trial-to-paid looks flat.
4.1× lift · 24h window · 43% no-integration
ICP-fit score read against integration timing
averaged-away anomaly
P2Recommendation
Integration nudge
Trigger a CS-routed integration nudge at hour 18 for trials above the ICP-fit threshold;
Expected impact
+12–18pp trial conversion · ICP-fit cohort
Target: ICP-fit trials in…2 concrete actions

Each pair above is from a real DecisionBox run. Source data is anonymized for the public site.

See the renewal already drifting and the expansion ninety days old, before your next QBR deck even drafts.

Use cases

For self-serve, sales-led, and PLG all at once.

Same engine, same data, different analysis areas. Pick the surface that matters this quarter.

Find the step where activation actually breaks.

The dashboard shows the funnel. It doesn’t show which sub-segment quietly drops out, which integration step blocks 40% of trials, or which signup cohort the team forgot to nudge. The agent finds those, and pairs each finding with an action the product team can ship the same week.

Sample analysis areas
  • Activation funnel cliffs by signup source, plan, and persona
  • Time-to-first-value spread across cohorts
  • Integration-step abandonment by ICP fit
  • Invite, import, and configuration completion rates
  • Self-serve vs sales-led activation gap
Why DecisionBox

Built for SaaS teams who need to read every query.

Three things every SaaS review surfaces. Every DecisionBox deployment ships with all three.

Sees what nobody had time to look for

You’ll see findings the team didn’t have time to look for. The agent decides what to investigate; you review. Not a chatbot you drive — a ranked backlog you didn’t have to write.

Every number you’d defend in a QBR

Every claim re-queried against your data before it ships. Every SQL query and reasoning step is logged and visible — the kind of paper trail finance and the data team both ask for before trusting a number.

Your data stays with you

Your credentials never leave your environment. The agent reads what’s already there; nothing exfiltrates. Every query and decision is logged for the team that has to defend the numbers downstream.

The next QBR question, already answered.