Agentic analytics · for analytics engineering teams
Catch the artifact before the panic. Business movement, not data noise.
When GMV drops overnight, Bicycle separates a real demand move from a late dbt run or a renamed column, ranked with the lineage.
Cursor multiplied what one engineer could do. Bicycle does the same for your analysts.
No credit card required
Detect
Explain
Review
The operating model is breaking
The force multiplier for your analytics engineers.
Faster onboarding, fewer repeat investigations, and decisions backed by evidence, with your team governing every definition, answer, and action.
Without Bicycle
With Bicycle
repeat
The same "is this number real?" question comes back every week
GMV drops overnight, a metric looks off, a dashboard owner panics, and you re-check freshness, source diffs, and lineage from scratch.
reuse
You build each freshness check once, then reuse it
Tune the driver tree once; Bicycle reruns it every time GMV moves, ranking real movement against the artifact with the lineage attached.
sprawl
Every new model and source adds more to maintain
More tables, more KPI definitions, more dashboards, more "is this real?" pings landing on your queue.
one model
New sources inherit one governed model
New tables and markets inherit approved KPIs, patterns, and cause paths instead of starting from a blank check.
shadow
Dashboard consumers build shadow analytics while they wait
Side spreadsheets and ad-hoc queries on GMV and freshness, with no lineage, no review, no shared definition.
self-serve
Dashboard consumers self-serve inside guardrails
They get answers with lineage on the metrics they read; you keep definitions, permissions, and actions governed.
blamed
You own whether the number is real but get blamed for the delay
Accountable for whether GMV is right and for clearing the queue, at the same time.
both
Trusted answers, delivered faster than the asks arrive
The recurring queue moves faster than the questions come in, and every published answer is defensible.
The capabilities
One governed system, six capabilities behind every answer.
Set up once, run continuously. Each capability is reviewable and governed by your team, so the business self-serves on a number it can trust. Pick one to go deeper.
Capabilities work together in one continuous loop. Detect → Explain → Act → Learn.
The playbookBuild · Tune · Govern
Your team builds the intelligence layer once. Bicycle runs it, tunes it, and lets you govern it as it scales.
Buildthe trusted foundation
→
Tunethe intelligence layer
→
Governself service and safe actions
A day in the lifeFirst pass by Bicycle · the call is yours
Retail investigation queue
The morning review starts with the first pass done.
GMV dropped on a few stores. Bicycle has already checked the segments, ranked the likely causes, attached the evidence, and kept the definition governed. Your team reviews the answer, publishes the story, and tunes what should happen next time.
GMV · 3 stores · regionalTop segment below baseline. Affected segment and evidence already assembled for analyst review.
Queue · alerts needing review
HighGMV dropped3 stores · regional · US2.3%-1.8pp vs baselineTop segment3 stores · regional · US$58Kat riskAnalytics leadowning team8:42 AM
MedAdd to cart rate downAll devices · Paid traffic5.6%-0.7pp vs baselineTop segmentPaid traffic · US$31Kat riskGrowth Marketingowning team8:15 AM
MedPayment success rate downAll devices · Credit cards92.1%-1.2pp vs baselineTop segmentCredit cards · US$44Kat riskPaymentsowning team7:02 AM
LowInventory in-stock droppedShoes · 3 warehouses94.3%-0.3pp vs baselineTop segmentShoes · West region$12Kat riskInventoryowning team6:55 AM
+ 4 alerts · 1 high · sorted by revenue at risk
SnoozeOpen investigationTriaged to one segment · 3 stores · regional · US
2.3% conversion
baseline 4.1% · ▼1.8pp · returning users · high volume
1 of 6 segments affected · 5 within expected range
Likely driversranked · confidence
Regional promo ended → real demand moveprimary81%
Late dbt run dropped 3 storessecondary38%
Organic demand dipruled out
Recommended actionsscoped
Create a ticket on Jirascoped
Alert #retail-revenue on Slackscoped
Flag metric as stalepreview
scoped · previewed · reversible · logged
+ Detected 8:42 · first pass 8:43 · top segment 3 stores · regional
Adjust modelApprove & publishImpact overview
2.3%
conversion rate · -1.8pp vs baseline 4.1%
1.24Maffected users
5.83Msessions
Top affected segments
1
3 stores · regional · US
-1.8pp2
3 stores · regional · West
-1.2pp3
3 stores · regional · freshness
-0.9ppEvidence attached
Funnel trendPDP to purchase
Channel mixpaid vs organic
Source freshnessfresh vs late run
dbt runlate @ 2:15 AM
Ruled out · not drivers
Price changesno overlap with window
Promo changeseligibility unchanged
Organic demand dipruled out
New vs returningboth affected equally
Definition governed · Source read-only · sources fresh
+ Reviewed by analyst on duty · 8:51 AM
Send backConfirm evidenceYour conclusion84% confidence
GMV dropped on 3 regional stores in the US, primarily because a regional promo ended, a real demand move. A late dbt run that dropped 3 stores is a secondary, pipeline contributor.
Recommended next steps
1Flag the metric as stale, scoped to GMV on the 3 stores.
2Monitor GMV and source freshness.
3Backfill the late run and set a freshness guardrail.
Attach & sharegoverned
Impact summary
Evidence bundle
Next steps
Share with
Ecom leadershipEcommerce OpsGrowth Marketing+2
+ Published to Slack · #retail-revenue · 8:54 AM
Save draftApprove & publishBicycle applied your approved playbook.
Detected pattern
Freshness impact
→
Applied playbook
GMV freshness
→
Likely driver
Late dbt run
→
Recommended action
Flag as stale
Caught 74 min earlier · 7:28 AM vs 8:42 AM on the first run
No re-investigation · pattern auto-applied, analyst review optional
+ Memory: retail GMV-freshness playbook · run 2
ReviewView playbookMore roles on Bicycle · same loopYour team's KPI · your screens
Different teams own different metrics. Bicycle keeps the investigation governed.
Move from this analytics engineering view to the retail workflows around search conversion, category margin, app funnels, and revenue performance.
Retail
Analytics Engineering & BI EngineeringMetric and pipeline trust: real movement vs artifact · you are here
Ecommerce Analysts & BI TeamsSearch conversion and the recurring investigation
Digital & Product AnalyticsApp funnel conversion and feature engagement
BI & Senior Data AnalystsRevenue and GMV cross-cut by region and category
Category Managers & BuyersCategory margin and sell-through
Vibe Analytics
Build your first governed investigation.
Start with one recurring metric in your queue: why checkout conversion, payment success, or activation moved. Bicycle watches the governed KPI, checks likely causes across the warehouse, payments, app & web, and events, and hands your team a reviewable first pass.
What to bring
A recurring investigation: checkout conversion, payment success, search conversion, or activation rate.
An approved read-only path to the relevant sources: the warehouse, event streams, app & web, payments, or ad networks.
The KPI definition, default segments, and reviewers who decide what becomes trusted.
What we do
Configure the investigation path around your KPI, segments, and evidence rules.
Monitor device, OS, channel, region, and segment movement.
Prepare a first-pass cause analysis with evidence, ruled-out paths, and governed definitions.
No credit card required · analyst-reviewed · governed from day one