Decisions come from connected data.
Orders, ads, fees, and returns feed one model. No siloed reports; the same numbers drive every workflow.
Dashboards · forecasts · decision logsWe engineer every step of your Amazon supply chain.
Verified manufacturers, audited lines, and container-level control before a single unit ships.
AI-powered commerce intelligence
East Star engineers the operating layer behind marketplace brands — inventory, advertising, pricing, and cash decided together. Instrumented, automated, and accountable to numbers we publish.
The problem
Most brands buy tasks. A listings person. An ads person. A supply-chain spreadsheet. Each one optimized alone; none of them informing the others. Revenue grows. Profit doesn't.
Optimized quarterly, in isolation from pricing and stock position.
Bids managed to ACOS targets that ignore contribution margin.
Reorders chased after the stockout, at air-freight prices.
Disconnected services. Connected competitors.
The brands taking share run one intelligence layer where every decision informs the next.
The operating system
Orders, ads, fees, and returns feed one model. No siloed reports; the same numbers drive every workflow.
Dashboards · forecasts · decision logsEvery recurring decision runs on a documented playbook. Improvisation is a bug, not a talent.
Playbooks · SOP libraries · workflow mapsAutomation coverage is measured and reported like a KPI. Every pipeline has a named human owner and an override path.
Coverage 71% — trailing 90dTargets defined up front. Forecast vs. actual, published every engagement — including the misses.
Scorecards · forecast-vs-actualThe operating system
Orders, ads, fees, and returns feed one model. No siloed reports; the same numbers drive every workflow.
Dashboards · forecasts · decision logsEvery recurring decision runs on a documented playbook. Improvisation is a bug, not a talent.
Playbooks · SOP libraries · workflow mapsAutomation coverage is measured and reported like a KPI. Every pipeline has a named human owner and an override path.
Coverage 71% — trailing 90dTargets defined up front. Forecast vs. actual, published every engagement — including the misses.
Scorecards · forecast-vs-actualOutcomes — 2025 engagements
+31% contribution margin in 2 quarters
Demand system replaced reactive reordering; ad spend re-weighted to margin, not ACOS. Constraint: 1 hero SKU stayed capacity-limited through Q3.
Read the system1 reporting layer across 23 P&Ls
EBITDA variance between brands cut from 19 pts to 7. Constraint: 2 brands exited the program in phase 1.
Read the system"First partner that told us what wouldn't work — then proved what would."
COO, 8-figure home brand · forecast accuracy 94.2%
The audit is 14 days, read-only data access, and ends in a report you keep either way.
Engagement model
Read-only data access. Ends in a profitability model of your account and a system roadmap. Keep both either way.
System architecture, playbooks, and the metrics we accept judgment on — stated before price.
Pipelines, dashboards, and automations deployed. Coverage reported weekly.
Strategists run the system. Forecast vs. actual, published monthly — including the misses.
The instrument
| Action | Impact/mo | Status |
|---|---|---|
| Approve PO — AQ-HEATER-50W | $18,400 | Attention |
| Reprice AQ-LED-BAR-90 | $9,120 | Margin −2.4% |
| Bid rule 12 — sync to margin | $4,860 | Running |
| Listing test — AQ-FILTER-20 | $2,300 | Running |
Every number names its period and comparison basis. A number without both is a rumor.
Forecast is always dashed. One drawing convention that carries the brand promise into every chart.
The work queue ranks actions by margin impact. One recommended action, marked in copper. One metric is down — a credible dashboard shows it.
From the insights desk
Proposals state metrics before price. Reports show forecast vs. actual. Start with the audit.