Retail and eCommerce
Stop Guessing Why Conversions Dip or Baskets Shrink
Bicycle AI analyzes browse, search, PDP, cart, checkout, inventory, and payments in real time, explains shifts by category and SKU, and routes fixes to the right owner.
Bicycle AI analyzes browse, search, PDP, cart, checkout, inventory, and payments in real time, explains shifts by category and SKU, and routes fixes to the right owner.
Typical detection → mitigation
Slice by device & region
Using your current stack
Every change logged
Track availability, pricing drifts, and on-shelf accuracy by region/store/SKU. Surface revenue at risk instantly and ship guarded fixes without waiting on dashboard refreshes.
Keep app/checkout flows healthy across versions, validate promo logic by cohort, and deploy failovers or rule updates safely with simulation and auto-rollback.
Plug raw tables—no semantic rebuild—compose agent checks on demand, enforce RBAC/PII and approvals, and give teams a governed action layer tied to operational systems.
Correlate search, PDP, and cart latency, facets, redirects, and image/CDN errors to SKU-level conversion. Recommend substitutes or promotion swaps for impacted cohorts.
Shelf vs system mismatches, inbound/fulfillment delays, dark-store micro-inventory; trigger operational tasks automatically.
Mis-pricing, bundle/coupon misfires, dynamic promotions; recommend reversible fixes or backup offers.
Checkout latency, payment declines, BIN/device issues, high traffic bottlenecks; auto-failover, throttle, and rollback.
Validate launches, discount rules, limited-time drops; prevent revenue leakage from misfires.
BOPIS, curbside, same-day delivery; catch mis-allocations, picker/slot issues, SLA breaches.
Size/color returns, warranty/upsell attachments, accessory combos; optimize reorder and promotion strategies.
3DS spikes, gateway failures, app version issues, declined BINs; recover lost orders fast.
On-shelf availability dips for a top SKU in a key city
Search-to-PDP conversion drops for a specific size/color
Checkout failures spike during peak hours
Weekly or daily averages show minor declines.
Aggregated metrics hide the size/color break.
Line graphs show general decline; root cause unclear.
Some stock alerts appear; no insight on root cause.
Some page or product alerts appear; no guidance for action.
Payment or cart errors logged; no correlation with revenue at risk.
Errors in inbound or shelf updates logged, but no next step.
CDN/image errors visible; no automated remediation.
App logs show gateway declines; no actionable guidance.
Flags the SKU × store cohort. Shows dollars at risk and proposes actions like shelf audit, reorder, or hero swap.
Identifies cohort and image/CDN issues; suggests substitute, promo swap, or PDP refresh.
Flags the device, BIN, or gateway cohort; recommends routing, auto-throttle, or rollback to recover orders fast.
“Trigger shelf audit for 12 stores; swap hero SKU; $38K protected.”
“Restore images; promote available sizes; conversion back to baseline.”
“Route iOS BIN 123 to Gateway B; recover 18% of approvals; audit attached.”
Multi-seller platforms where feed quality, buy-box, price/inventory parity, and search-to-purchase flow drive GMV.
Brand-owned ecommerce with launches, limited drops, and tight control of pricing, inventory, and experience.
Store vs online stock accuracy, BOPIS/curbside SLAs, delivery promises, and picker/slot capacity decide revenue.
Dark-store micro-inventory, rider supply, ETA drift, substitutions, and catalog freshness drive conversion.
Size–color availability, PDP speed during spikes, returns, and accessory attach affect sell-through.
High-ticket availability, pickup/delivery windows, financing/warranty attach, and price-match control define basket value.
Bulky/seasonal items where carrier capacity, delivery slot, and service attach (install/haul-away) matter.
Blended 1P/3P model requiring seller service-level monitoring, endless-aisle feeds, vendor-direct accuracy, and promo discipline.
Connects directly to your data (warehouse, PIM, OMS, gateways). Bicycle AI is an agentic action layer on top of your stack—no rip-and-replace or heavy engineering required.
Full audit trails ensure governance and reversibility. We enforce RBAC/PII controls and never train models on your proprietary data.
Integrates seamlessly with Shopify/Headless stacks, CDNs, and operational tools (Jira, Slack) to route and deploy fixes with auto-rollback safeguards.
Bicycle AI instantly flags failure cohorts (e.g., specific BINs, devices, or app versions) and automatically triggers guarded fixes—like rerouting traffic to a healthier gateway or applying a temporary promo—to recover lost orders in real time.
Yes. We go beyond reporting shelf vs. system mismatches. Bicycle AI quantifies the revenue at risk and automatically routes operational tasks (e.g., shelf audit, micro-inventory restock) to the right store or logistics owner via ticketing systems.
Bicycle AI pinpoints the SKU-level root cause (e.g., CDN errors, facet misconfigurations). It suggests and can auto-deploy reversible actions—like a hero SKU swap or promotional substitution—to restore conversion without manual triage.
We are designed to plug directly into your current systems (Shopify, PIM, OMS, gateways, logs) in 2–3 weeks to deliver first revenue-protecting insights. No rip-and-replace or semantic layer rebuild is required.
Bicycle AI is an agentic analytics layer. It doesn't just show dashboards; it composes checks that protect revenue and gives your teams a governed action layer to deploy fixes, rollbacks, and failovers directly, tied to operational systems.
Bicycle is an Agentic Analytics platform engineered for high-transaction businesses to optimize revenue
