Agentic analytics · for data & analytics teams

Give your analysts
an unfair advantage.

Bicycle brings that same leverage to analytics that cursor brought to developers: governed answers, recurring investigations, and next-step recommendations, running across the stack you already use.

Cursor didn't replace engineers. It multiplied what one team could do, and the teams that adopted early pulled away.

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app.bicycle.ai checkout · analytics agentAgent live
Build Tune Govern
From our clients

What teams say.

“Focusing on aggregate metrics might boost our bottom line, but true profit growth often lies in the details. Bicycle empowers us to spot hyper specific opportunities and pinpoint key drivers. This shift from broad strokes to precision targeting is transforming our supply chain strategy.”

Rakshit Daga
Rakshit Daga
CPTO, bigbasket

“With over 45,000 restaurant locations leveraging the UrbanPiper software and processing millions of orders every week, Bicycle has been pivotal in our approach to data-driven decision-making.”

Saurabh Gupta
Saurabh Gupta
Co-Founder/CEO, UrbanPiper

“Bicycle AI gives us real-time visibility into settlement performance that we simply didn’t have before. We can spot issues early, understand why they’re happening, and take action before they impact our customers.”

Farai Alleyne
Farai Alleyne
SVP, Technology Operations, Billtrust

“Our supply chain and logistics technology relies on real-time data from a variety of sources. Bicycle supplements our data science efforts by providing proactive, event-driven insights to help deliver a superior customer experience.”

Blair Koch
Blair Koch
Chief Digital Officer, ACERTUS
The operating model is breaking

The force multiplier for your analytics team.

Faster onboarding, fewer repeat investigations, and decisions backed by evidence, with your team governing every definition, answer, and action.

Without Bicycle With Bicycle
repeat
Analysts repeat the same "why did this move?" work
Every time a KPI drifts, they rebuild the cut, the drivers, and the evidence from scratch.
reuse
Recurring investigations become reusable agents
Tune the driver tree once; it runs on every future move, with evidence attached.
sprawl
Every new business unit adds more
More KPI definitions, more dashboards, more ad-hoc asks, more Slack investigations to staff.
one model
One governed model, not one per team
New units inherit approved KPIs, patterns, and cause paths instead of starting blank.
shadow
The business waits, or builds shadow analytics
Spreadsheets and side queries with no lineage, no review, no shared definition.
self-serve
The business self-serves inside guardrails
They get answers with lineage; you keep definitions, permissions, and actions.
blamed
Data & Analytics owns trust but is criticized for delays
You're accountable for the number and the queue at the same time.
both
More trusted results, delivered faster to the business
The ad-hoc queue moves faster than the questions arrive, and every 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.

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.

Checkout conversion dropped on a key segment. 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.

Checkout conversion · iOS · mobile webTop 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
HighCheckout conversion droppediOS · Mobile web · US2.3%-1.8pp vs baselineTop segmentiOS · Mobile web · US$58Kat riskEcommerce Opsowning 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
Checkout conversion · root cause analysisfirst pass by Bicycle↓ -1.8pp
Triaged to one segment · iOS · Mobile web · 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
Apr 30 release → page load time (p95)primary84%
Paid traffic mix shiftsecondary31%
Price & promo changesruled out
Recommended actionsscoped
Create a ticket on Jirascoped
Alert #retail-revenue on Slackscoped
Roll back Apr 30 releasepreview
scoped · previewed · reversible · logged
+ Detected 8:42 · first pass 8:43 · top segment iOS · Mobile web · returning
Adjust modelApprove & publish
Validate evidence · iOS · mobile web · US8:42 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
iOS · Mobile web · US
-1.8pp
2
iOS · Mobile web · West
-1.2pp
3
iOS · Mobile web · Returning
-0.9pp
Evidence attached
Funnel trendPDP to purchase
Channel mixpaid vs organic
Device performanceiOS vs Android
Release activityApr 30 @ 2:15 AM
Ruled out · not drivers
Price changesno overlap with window
Promo changeseligibility unchanged
Inventory availabilityin stock
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 · checkout conversion8:42 AM↓ -1.8pppublish
Your conclusion84% confidence
Checkout conversion dropped for iOS mobile web users in the US, primarily driven by an Apr 30 release that increased page load time (p95). Paid traffic mix shift is a secondary contributor.
Recommended next steps
1Roll back the Apr 30 release for iOS mobile web.
2Monitor conversion and page load time.
3Re-test and re-enable with a performance 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: Checkout conversion dropped · iOS · Mobile web · US
Bicycle applied your approved playbook.
Detected pattern
Release impact
→
Applied playbook
iOS mobile web
→
Likely driver
Page load time (p95)
→
Recommended action
Roll back release
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 checkout investigation playbook · run 2
ReviewView playbook
The analyst move changes: less reassembling the investigation, more reviewing the first pass and tuning the loop.Build once · review the rest
Same modelDifferent wins for each role

More from the team you already have.

Every new business unit, market, and exec question used to mean more analyst hours. Bicycle works each KPI move once, on the model both teams trust, and reuses it.

Data & Analytics leaders
Head of Data · Head of Data & AI · Head of AI

Grow coverage, not headcount.

New units, markets, and KPIs used to each add to the queue. Now they inherit approved definitions, patterns, and cause paths instead of starting blank. Your team governs the model; the model scales the coverage. You stay in control of trust, access, and what becomes official.

New business units inherit approved KPIs and cause paths instead of starting from scratch.
One governed model feeds alerts, stories, dashboards, and chat, so definitions don't drift.
Role-based access down to the ontology: who reaches which KPIs, dimensions, and segments.
Every published answer carries its definition, lineage, and audit trail, ready before the board asks.
See the governance model
Governance · one modelgoverned
Conversion −4.2% · iOSpublished
definition · conversion = orders ÷ sessions
lineage · 4 sources, validated
Units on the model
EU142 KPIs · 38 cause pathsinherited
APAC142 KPIs · 38 cause pathsinherited
LATAMnew unit · 0 rebuilt from scratchinheriting
Who can ask & act
Adminedit · publish · actall access
Analysttune · publishscoped
Businessask · subscribeguardrails on
✓audit · 2 approvals · logged 14:32
Analysts & BI teams
Senior BI Analyst · Analytics Engineer · Ecommerce Analyst

Stop compiling. Start reviewing.

Bicycle detects the change, tests the likely drivers, and packages the evidence. You validate, tune, and publish, instead of rebuilding the same analysis every time the business asks why.

Review, not rebuild: first-pass cause analysis arrives with evidence and the wrong drivers ruled out.
Your logic becomes an asset: driver trees codify what your best analysts already know, reused on every future move.
Ad-hoc asks and new dashboards stop hitting the queue, so your week goes to the harder analysis.
Publish across surfaces: a validated answer flows to alerts, stories, dashboards, and chat.
Walk through a day in the life
Triage · cause analysisawaiting review
Conversion −4.2% · iOS1 segment
Ranked drivers
Apple Pay wallet regression · v4.18.288%
iOS checkout flow31%
Pricing changeruled out
Promo & weatherruled out
ValidatePublish →

Same governed model underneath both. Not a different number in every tool.

Vertical use casesSame spine · tuned to your domain

You don't start from a blank model.

Pre-built analytics agents activate for the KPIs where revenue actually breaks. Data & Analytics tunes them instead of defining every KPI, driver tree and alert from scratch.

Same capability spine, tuned to your domain. Not a separate product per vertical.
Trust & governanceEvery answer carries its proof

Trust the number, and everything behind it.

01
01 The answer
Defensible numbers.·Evidence-backed causes.·Governed actions.

Every Bicycle number is defensible by default. Every alert, story, dashboard and chat answer arrives carrying its definition, lineage and evidence, plus what was checked and ruled out. Self-service runs inside one governance plane Data & Analytics controls.

See it on your data
app.bicycle.ai · published answergoverned
The answer
Conversion −4.2%iOS · mobile checkout
Published to alert + data story · finding #2241 · last 24h
Governed
answer
Definitioncvr = orders ÷ sessions · approved, governed
Lineageevents→sessions→4 sources→source query
EvidenceApple Pay regression · v4.18.2 · error trace · session recordings
Ruled outpricingpromoinventory 5 of 8 drivers closed
AccessUse-case scoped · roles enforced at publish and act
ApprovalsAnalyst verified definition & lineage · 2 sign-offs before publish
AuditFinding #2241 · 17 events logged · answers, changes and actions
RollbackAction previewed and approved · reversible, inside guardrails
Defensible · one click from its proof · stand behind it
02
Deployment

Runs wherever you want your data to live.

Start on Bicycle's cloud, or run Bicycle inside your own. Either way, your team keeps governance and the data stays where your policies require.

SaaSFastest to start
Bicycle hosts and runs the service on GCP in the United States, multi-tenant with built-in tenant isolation. Connect your sources and go. When data lives in your own cloud or data center, VPC peering reaches it without moving it.
BYOC · Bring Your Own CloudFor enterprise & regulated data
Bicycle runs inside your own cloud account on AWS, GCP, or Azure, in any region, so raw data never leaves your environment. You provision a dedicated sub-account and keep full control at the account level: egress, allowed services, and quotas. Approvals and access logging run through your own IAM.

Both models are SOC 2 Type II and GDPR. BYOC availability is scoped with our team during security review.

03
Security & privacy

Your data trains nothing and never leaves the runtime.

Bicycle was built so a security review is a short conversation: here is what the AI sees, what it never sees, and where every answer is evaluated.

The AI never sees your raw data.
Bicycle uses AI to draft structured artifacts: mappings, rule-sets, KPI logic, pattern checks, and recommendation logic. Those artifacts run inside the secure runtime, against your data. The raw records are never sent to the model, and Bicycle does not train any model on your data.
Zero Data Retention on top of that.
Bicycle uses OpenAI with Zero Data Retention: nothing is retained after processing, and OpenAI is contractually prohibited from training on prompts or responses. A second layer over an architecture that does not send your data in the first place.
Encrypted, governed, access-controlled.
AES-256 at rest, TLS in transit. Role-based access control, tenant-aware permissions, SSO, and tenant isolation. Durable and risky changes follow approval and leave an audit trail.
SOC 2 Type II and GDPR.
A SOC 2 Type II certified operating environment, GDPR, and GCP infrastructure in the United States. Highly available and fault-tolerant, with continuous monitoring, disaster-recovery exercises, and regular penetration testing. The full SOC 2 report is available under NDA.
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.
Book a demo

analyst-reviewed · governed from day one

Book a demo