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Industries · Marketplaces

Autonomous AI discovery built for marketplaces.

Run an autonomous AI discovery on your supply, demand, fulfillment, and review data — find the seller concentration risk, the hub-and-spoke imbalance, and the satisfaction-masked retention gap, before the next ops review.

Built for VPs of Marketplace, Heads of Seller Growth, Heads of Demand, and COOs at two-sided platforms.

CRITICALHub-and-spoke imbalance
64% of orders ship cross-state.
+76% freight, +7 days transit, −0.18 review points…
From a real DecisionBox run
DecisionBox for marketplaces

Supply, demand, fulfillment, reviews — read both sides at once.

Marketplace dashboards split the world cleanly: a supply view, a demand view, a take-rate report. The patterns worth catching — where take-rate is leaking, why GMV moved, and which seller cohorts are quietly accelerating — live across all of them. 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 both sides of the business sign off on.

See how every number gets checked
The cost of missed insights

The patterns hiding between supply and demand are the ones costing the take-rate.

Three benchmarks marketplace operators are quietly paying right now. Each one points to the same gap: the data already has the answer; nobody has the bandwidth to read both sides at once. Every quarter of waiting compounds into GMV you don’t recover.

Capacity gap
~67%
The share of global e-commerce GMV projected to flow through marketplaces by 2027. Concentration is rising; visibility into supply geography and seller cohorts hasn’t kept pace.
a16z Marketplace 100, 2024
Concentration risk
Top 10%
Of sellers drive roughly 80% of GMV on most marketplaces. When the top of the long tail wobbles, the aggregate dashboard reads "healthy" until it doesn’t.
a16z marketplace research, 2024
Liquidity pull
~70%
Of new marketplace buyers never make a second purchase. The acquisition spend was paid on both sides; the second-purchase trigger never fired. Habit-formation signals sit in the cohort data.
Marketplace Pulse benchmarks, 2024
From real runs

Three findings, three actions, one overnight pass.

Three pairs the agent surfaced in real marketplace 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.

CRITICALSeller concentration
The top 5% of sellers generate 53% of GMV.
53% GMV from top 5% · 8 of 20 single-state · 78%…
Cross-validated against seller GMV, location, and…
ship this one first
P1Recommendation
Supply de-risking
De-risk the single-state dependency: proactively recruit 10+ sellers in the at-risk state.
Expected impact
−38pp concentration · 10+ sellers recruited · 90-day…
Target: at-risk single-state…2 concrete actions
CRITICALHub-and-spoke imbalance
64% of orders ship cross-state.
64% cross-state · +76% freight · +7 days transit
Pattern visible only when supply geography, freight…
cross-system pattern
P1Recommendation
Geographic rebalance
Open a seller-recruitment campaign in the four highest-demand undersupplied states.
Expected impact
+12pp on-time · −R$10/order freight · +0.3 review pts on…
Target: undersupplied…2 concrete actions
MEDIUMSatisfaction-masked retention
97% of customers placed exactly one order.
97% one-time · 3.93% → 1.86% repeat rate · 4.13 vs…
The driver is habit non-formation, not…
averaged-away anomaly
P2Recommendation
Second-purchase trigger
Launch a day-21 second-purchase nudge for first-time buyers above the ICP-fit threshold.
Expected impact
+2–4pp repeat rate · ICP-fit first-time cohort · 60-day…
Target: ICP-fit first-time…2 concrete actions

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

See the supply concentration tightening and the buyer cohort drifting flat, before your next ops review.

Use cases

For two-sided platforms, vertical marketplaces, and managed networks — all at once.

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

Where supply is short, where demand is unanswered.

The supply dashboard shows total sellers. The demand dashboard shows total searches. Neither shows the geography or category where the two don’t match. The agent reads both sides against each other and flags the imbalance — the same shape of finding that drives growth-opportunity discovery.

Sample analysis areas
  • Demand-to-seller ratio by region and category
  • Search-to-listing match rate by query intent
  • Cross-state and cross-border fulfillment imbalance
  • Category-level supply gaps masked by category averages
  • New-seller onboarding throughput vs incoming demand
Why DecisionBox

Built for marketplace teams who need to read both sides at once.

Three things every ops 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 bandwidth to pull. The agent decides what to investigate; you review. Not a chatbot you drive — a ranked backlog of supply gaps, take-rate drifts, and cohort breaks you didn’t have to write.

Every number you’d defend in an ops review

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 supply, demand, and finance all expect before trusting a number in next Monday’s deck.

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 ops review question, already answered.