Agentic analytics for quick commerce

Find the quick-commerce revenue leaks before the delivery window closes.

Bicycle watches zone order rate, delivery SLA, dark store availability, substitution rate, and slot utilisation across the segments that matter. When a number moves, it explains the likely cause and recommends the next action to the team that can fix it.

Start for free Book a demo

No credit card required

retail / on-time delivery agentAgent live
Detect
On-time reach
Bengaluru · packing stage · last 30 min
-12ppvs same-hour baseline
Explain · likely causes, ranked
  • 1Demand spike: order-to-picker ratio 1:11 against a planned 1:7high
  • 2Picker attendance below plan at three storescontributing
  • 3Seconds per SKU above the 30-second benchmark on the evening shiftcontributing
business causetechnical cause
Act · recommended next step
Recommend to regional ops: reallocate pickers across the three affected stores and adjust the evening shift plan. Scoped, previewed, logged.
Evidence: order logs · picking-system events · roster and attendance · delivery-agent routing · per-stage SLA thresholds

A dashboard shows the drop. Bicycle finishes the investigation and recommends the fix.

Where quick-commerce revenue breaks

Start with a single revenue KPI critical to your business.

Bicycle comes with quick-commerce industry intelligence already inside. Pick one Bicycle-recommended KPI and watch it run: the KPI it watches, the causes it checks, the action it recommends to the owner, and what it keeps.

Pick a revenue problem
Order Volume Agent Drop
Zone order rateNorth Zone · 18:00–22:00 slot -18%vs same slot last 7d
Order volume drop · North Zone evening slot
1 / 1
D · DetectDetect · what moved
Zone order rate crossed -15% below 7-day slot average. Triggered at 18:42.
Order volume monitor · North Zone · evening slot segment
Alert · Zone order ratejust now
North Zone · 18:00–22:00 slot-18%
SignalZone order rate crossed -15% below 7-day slot average. Triggered at 18:42.
Order volume monitor · North Zone · evening slot segmentSubscribe
E · ExplainExplain · why it moved
Slot capacity
→
Ops system
→
Evening slot capacity cut from 280 to 210 orders after shift pattern change; not reflected in app availability
App availability display
→
Frontend API
→
App still showing Available on evening slots that were over-capacity in backend
Dark store staffing
→
HR system
→
Two picker slots unfilled; throughput ceiling hit at peak load
Confidence
High
Ruled out
demand signal normalexternal weather not a factorcompetitor pricing unchanged
Cause summaryranked
1Evening slot capacity cut from 280 to 210 orders after shift pattern change; not reflected in app availability
2App still showing Available on evening slots that were over-capacity in backend
Ruled outdemand signal normal · external weather not a factor
High confidence
A · ActAct · what to do next
Suppress evening slot in-app for North Zone until capacity restored
→
Ops system
→
preview · 1-click
Notify ops lead to reinstate picker shifts
→
Slack
→
auto · audit-only
Backfill unfilled orders to adjacent zone where capacity exists
→
Ops system
→
approval required
Owner: Ops team
PreviewApproval
Action previewOps team
Suppress evening slot in-app for North Zone until capacity restoredOps system
Notify ops lead to reinstate picker shiftsSlack
PreviewApproval
L · LearnLearn · what the loop keeps
Accepted cause saved: Slot capacity change to app availability mismatch is a recurring pattern; auto-check added to staffing change workflow. Run the same check across all zones when staffing patterns change.
Decision memory · routed to Ops team
Saved to memorysaved
Accepted causeSlot capacity change to app availability mismatch is a recurring pattern; auto-check added to staffing change workflow
Next runRun the same check across all zones when staffing patterns change
starts from the answer
Setup
1
Rapid activation
Ecommerce, catalog, inventory, pricing, order, payment, fulfillment, already connected. No rebuild.
2
Vertical native context
Speaks SKU, category, search term, cart, checkout, returns. Answers arrive in your language.
→
Detect
3
Always-on KPI intel
Watches conversion at segment level, SKU, channel, device, city. Surfaces before review.
→
Explain
4
Multi-factor cause
Checks stock, price, promo, search relevance, checkout, payments, together, not in turn.
5
Defensible answers
Every finding cites the segment, the dimension, and the source system. Trust the number.
→
Act
6
Governed actions
Routes to merchandising, inventory, or payments, with preview and approval.

A quick-commerce problem not listed here? Book a demo and we will map it.

The cost of finding out late

A dashboard shows the drop. It never finishes the investigation.

Before Bicycle

On-time delivery slips across a city. The manual chase begins.

The dashboard shows the breach. The team still checks picking queues, attendance, shift plans, and routing by hand, across five systems, and the answer arrives the next day.

  1. Store managers check their own picking queues
  2. Regional ops compares stores by hand
  3. Workforce planning pulls the roster against actual attendance
  4. Analytics reconciles five systems into a next-day spreadsheet
  5. Nobody can separate demand from staffing from speed while the peak is still running
Hours to days. Revenue keeps leaking.
With Bicycle

Bicycle starts the investigation the moment the KPI moves.

It detects the drop, ranks the affected keywords and cities, tests business and technical causes in parallel, attaches the evidence, and routes the issue to the owner with the next step.

  1. Detect the movement, ranked by impact
  2. Test business and technical causes together
  3. Attach the evidence, rule out the rest
  4. Route the safe action to the owner
  5. Learn the accepted cause for next time
Minutes. The owner gets the cause and the move.
Capabilities · purpose-built for retail

Trained on the KPIs, patterns, and root causes of retail.

Six capabilities, set up once and run continuously. Business teams move faster, analysts keep governance, everyone trusts the number. Pick one to go deeper.

Multi-factor cause analysis

"What caused this?" turns into a three-day Slack thread across data, product, ops, and engineering, and the answer still arrives without evidence.

What Bicycle does

1

Open the cause summary. Ranked business and technical causes. The likely driver is named, with confidence and impact.

2

See the evidence. Logs, deploys, pricing changes, supplier behavior, and search relevance, with the data attached.

3

Know what was ruled out. The negative findings close the debate before it starts.

What it looks like for your team

In practice

"Mobile checkout dropped 4% on iOS." Cause: Apple Pay button failing since 9:42am, release v4.18.2. Ruled out: pricing, inventory, promo, weather.

Capabilities work together in one continuous loop. Detect → Explain → Act → Learn.

Who runs this in retail

Business teams need answers fast. Analysts ensure conclusions are trustworthy.

Same governed intelligence underneath. The retail agent for the teams who own the number, the controls for the team who owns the data.

Business teams · act on it

See what changed, why, and what is safe to do.

The move finds you, ranked by revenue impact, with the cause and the next step attached. No dashboard hunt, no three-day ticket.

Ecommerce & Site Merchandising→ Category Managers & Buyers→ Merchandise Planning & Replenishment→ Marketplace & Merchant Account Teams→
Sub-verticalsQuick-CommerceMarketplacesFashion Specialty
Data & Analytics · keep it trusted

Govern the definitions, evidence, and safe actions.

Analysts review first-pass causes instead of rebuilding them. Leaders give the business self-service inside one governed boundary.

Ecommerce Analysts & BI→ Analytics Engineering & BI Eng→ Digital / Product Analytics→ BI Analysts & Senior Data Analysts→
Jump to your roleBusiness teamsData & Analytics teams
What Bicycle becomes for your retail operation

The agentic analytics layer on top of your stack.

Your stack stays the system of record. Bicycle turns retail signals into alerts, triage, stories, dashboards, chat, and governed actions on top of it. No rip-and-replace.

Bicycle the agentic layer
Surfaceswhat business teams touch
AlertsTriageStoriesDashboardsChatActions
Governancethe self-serve safety rail
access · approved definitions · evidence · audit · rollback
AgentsAI · your business analyst

Frames the question, orchestrates the investigation, writes the story, recommends the next step. It reasons.

Search conversionInventory availabilityPromo profitabilityReturn spikeCheckout friction
IntelligenceAutoML · your data analyst

Detects movement, ranks drivers, computes confidence, forecasts. The numbers are calculated, not generated.

Pattern EngineCause EngineDriver TreesForecastingImpact Ranking
Business modelthe semantic layer
SKUCategoryStoreCitySearch termBasketOrderInventoryPromo
Connectorsthe bridge, in and out
↑Signal ↑Cause ↓Action ↑Knowledge
reads ↑ · acts ↓ · Bicycle sits on top
Your stack · system of record
SnowflakeBigQuerydbtEcommerce eventsOrders & catalogPricingObservabilitySlackJiraBI+ more

And the rest of what you run: warehouse · ecommerce events · catalog · pricing · observability · BI · Slack

Bicycle runs on top of your existing systems. The retail pack supplies the starting KPIs, causes, stories, and action paths.

Vibe Analytics · self-serve start

Start with one delivery KPI in your quick-commerce stack.

Bring one revenue-critical quick-commerce KPI, connect a trusted source, and watch Bicycle turn a prompt plus your data into a quick-commerce analytics agent. Zero to a working agent in about 15 minutes.

Prompt + data
"Watch search to purchase conversion by city and category, and tell me why it moves."
Snowflake · connected
Analytics agent
Builds the model, detects movement, tests business and technical causes.
Outputs
AnswerStoryAlert
Teams usually start withSearch conversionInventory availabilityCheckout friction
Start for free See what is included

Grow into the full product when your team is ready.

Test ride Bicycle on a quick-commerce KPI.

Bring one delivery or availability metric that matters. We will show how Bicycle detects the move, explains the cause, and recommends the next step on top of the stack you already run.

Start for free Book a demo

No credit card required