One workflow from data intake to follow-through
Sparkzville keeps deterministic trade math under every number, and uses grounded AI exactly where interpretation helps — not as a black box.
Data intake & readiness
Classify incoming trade files, map your columns, and validate rows. Three starter datasets unlock a usable trade model — no cleanup project required.
Command center
Landed-cost pressure, the top sourcing move, what changed, and the next action to validate — surfaced in one operating view.
Scenario analysis
Compare the current path against a modeled alternative with deterministic trade math. Save the paths worth revisiting.
Supporting data
Drill from any recommendation into the exact shipments, products, and lanes behind it — filterable and paginated.
Trade reports
Freeze the current picture into a durable brief with audience-aware narration for leadership, operations, and procurement.
Workspace Copilot
Ask grounded questions across the workspace. Every answer cites the recommendations, scenarios, and reports it draws from.
Six surfaces, one decision rhythm
Each surface moves you from connected data toward a decision you can act on and explain.
Data intake
Start with the files that unlock first value
Shipment history, product context, and tariff reference establish a usable baseline fast.
Command center
See the top decision move first
Landed-cost pressure, recommendation priority, and readiness in one operating view.
Scenario analysis
Compare the current path against the strongest alternative
Pressure-test sourcing and routing tradeoffs before committing.
Alerts & watchpoints
Keep trade thresholds visible between reviews
Signals stay active so the team knows what to follow through on next.
Reports
Turn the current picture into a durable brief
Stakeholder-ready snapshots that preserve the exposure story and modeled opportunity.
Copilot & trust
Use AI where interpretation helps most
Grounded answers with citations, while deterministic trade math stays the final authority.
Put it to work on your own data
Connect a few starter datasets and see your first recommendation.