Agentic analytics · for data engineering teams

Escalate only on real movement.
The artifact, caught before the board.

When the approval rate looks wrong overnight, Bicycle separates a real issuer decline from a settlement-batch or model artifact, with the lineage.

Cursor multiplied what one engineer could do. Bicycle does the same for your data engineers.

No credit card required

app.bicycle.ai checkout · use-case agentAgent live
Detect Explain Review
The operating model is breaking

The force multiplier for your data 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 rate real?" question comes back every week
Approval rate looks wrong overnight, and you trace the freshness, the settlement diff, and the lineage from zero, by hand.
reuse
You build the metric-trust agent once, then reuse it
Tune the real-vs-artifact driver tree once; Bicycle reruns it every time a rate looks wrong, with the lineage already attached.
sprawl
Every new pipeline and model version adds more to trace
More batch steps, more KPI definitions, more dashboards, more ad-hoc "is this number real?" asks landing on your queue.
one model
New pipelines inherit one governed model
New pipelines and metrics inherit approved KPIs, freshness patterns, and real-vs-artifact cause paths instead of starting blank.
shadow
Consumers build shadow reconciliations while they wait
Side spreadsheets and ad-hoc queries on approval and settlement, with no lineage, no review, no shared definition.
self-serve
Consumers self-serve inside guardrails
Analytics and risk reporting get answers with lineage on the rates they read; you keep definitions, permissions, and actions governed.
blamed
You own the pipeline but get blamed for the number
Accountable for whether the approval rate is real and for clearing the trust queue, at the same time.
both
Trusted rates, delivered faster than the escalations arrive
The pipeline-trust 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.

Approval rate dropped on the overnight batch. 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.

Approval rate · overnight batch · txn_approvals_dailyTop segment below baseline. Affected segment and evidence already assembled for analyst review.
8:42 AM-1.8ppreview queue
Retail investigation queue8:42 AM↓ -1.8ppreview queue
Queue · alerts needing review
HighApproval rate droppedOvernight batch · txn_approvals_daily2.3%-1.8pp vs baselineTop segmentOvernight batch · txn_approvals_daily$58Kat riskAnalytics + Risk reportingowning 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 investigation
Approval rate · root cause analysisfirst pass by Bicycle↓ -1.8pp
Triaged to one segment · overnight batch · txn_approvals_daily
2.3% conversion
baseline 4.1% · ▼1.8pp · returning users · high volume
1 of 6 segments affected · 5 within expected range
Likely driversranked · confidence
Issuer cohort declineprimary79%
Settlement batch step failedsecondary52%
Fraud eventruled out
Recommended actionsscoped
Create a ticket on Jirascoped
Alert #retail-revenue on Slackscoped
Flag txn_approvals_daily as stalepreview
scoped · previewed · reversible · logged
+ Detected 8:42 · first pass 8:43 · top segment overnight batch · txn_approvals_daily
Adjust modelApprove & publish
Validate evidence · overnight batch · txn_approvals_daily8:42 AM↓ -1.8ppreview more
Impact overview
2.3%
conversion rate · -1.8pp vs baseline 4.1%
1.24Maffected users
5.83Msessions
Top affected segments
1
Overnight batch · txn_approvals_daily
-1.8pp
2
Overnight batch · settlement diff
-1.2pp
3
Overnight batch · model version
-0.9pp
Evidence attached
Funnel trendPDP to purchase
Channel mixpaid vs organic
Freshness checkfresh vs stale
Settlement batchcutoff @ 2:15 AM
Ruled out · not drivers
Price changesno overlap with window
Promo changeseligibility unchanged
Fraud eventnone detected
New vs returningboth affected equally
Definition governed · Source read-only · sources fresh
+ Reviewed by analyst on duty · 8:51 AM
Send backConfirm evidence
Publish answer · approval rate8:42 AM↓ -1.8pppublish
Your conclusion84% confidence
Approval rate dropped on the overnight batch, primarily a real issuer cohort decline. A settlement-batch artifact that double-counts retries is a secondary contributor.
Recommended next steps
1Flag txn_approvals_daily as stale for the affected batch window.
2Monitor approval rate and settlement-batch freshness.
3Reprocess the batch 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 & publish
Reused next time · operating memoryToday 7:28 AM↓ -1.6ppauto-applied
Similar situation detected: Approval rate dropped · overnight batch · txn_approvals_daily
Bicycle applied your approved playbook.
Detected pattern
Artifact impact
Applied playbook
approval rate
Likely driver
Issuer cohort decline
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: payments pipeline-trust playbook · run 2
ReviewView playbook
More roles on Bicycle · same loopYour team's KPI · your screens

Different teams own different metrics. Bicycle keeps the investigation governed.

Move from this payments data engineering view to the payments workflows around approval rate and the recurring investigation.

Payments
Payments Data EngineeringMetric trust on approval rate: real movement vs pipeline artifact · you are here
Payments AnalyticsApproval rate and the recurring investigation
Vibe Analytics

Build your first governed investigation.

Start with one recurring question 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

No credit card required Start for free