Bicycle AI continuously analyzes revenue KPIs, explains what changed and why, and routes safe fixes into your existing tools—in minutes, not days.
Teams can spot a dip. The challenge is everything that happens next—and in high-transaction businesses, delays cost real revenue.
Bicycle AI detects meaningful changes, shows likely causes with evidence, and routes recommended actions to the right owner.
Not more dashboards. Actionable outputs in the formats teams actually use.
What changed, where, revenue at risk, top likely causes, and the recommended next step. Routed to the right owner.
What's driving this quarter's conversion or margin movement. Narrative outputs for leadership reviews.
Ask "why did APAC bookings drop?" and get contextual answers with evidence. Slice data safely, export for stakeholders.
One closed loop: Signal → Insight → Action. Here's how Bicycle produces those outputs.
Bring in your data (warehouse, streams, DBs) and knowledge (docs, KPI catalogs, dashboards, SOPs).
Bicycle auto-builds an ontology: events, dimensions, journeys, cohorts, KPIs—with business context baked in.
Continuous AutoML finds shifts and ranks by impact. Agentic RCA tests business + technical drivers and returns ranked causes.
Deliver alerts, stories, and Q&A. Trigger workflows or reversible actions—with approvals and full audit trail.
The design decisions that matter for revenue-critical operations.
When something moves, Bicycle returns the likely causes, the recommended next step, and reversible execution with full audit trail.
No rip-and-replace. Connects to your warehouse, streams, and operational tools, then pushes outputs back into Slack, Jira, and your workflows.
Business and technical causes in one view. Connect transactions, supply chain, app performance, and external signals in a single model.
Preview actions before executing. Require approvals for higher-risk changes. Full audit trail. Your data never trains our models.
Pre-wired models for Retail, Payments, and Travel understand SKUs/margins, BINs/issuer logic, fare classes. Faster time-to-value.
The design decisions that matter for revenue-critical operations.
Raw schema → ontology → use case agents
Connect data + internal knowledge (docs, KPI catalogs, dashboard screenshots)
Discover and stitch events, dimensions, and journeys across systems
Build KPI definitions + KPI tree + cohorts with business annotations
Always-on detection + multi-factor cause analysis
Continuous AutoML across KPIs, cohorts, and journeys
Impact-ranked patterns (spikes, drifts, mix shifts, funnel breaks)
Agentic RCA orchestrates tool calls to test competing drivers
Business adoption with guardrails
Alerts (tactical), stories (strategic), conversational Q&A (ad-hoc)
Route to the right owner by slice (product, merchant, supplier, region)
Approvals, audit trail, and reversible actions
And the data systems behind them.
Scale coverage without becoming the bottleneck
Standardize KPIs, reduce ad-hoc requests, measure ROI per use case
Turn recurring questions into reusable agents with governed outputs
Reduce pipeline complexity with ontology-first definitions
Find revenue leaks and opportunities while there's time to act
See what moved, why, and recommended actions—by market, product, partner
Isolate funnel breaks across segments, validate fixes with measured impact
Route issues to the right owner and execute playbooks with audit trails
Bicycle integrates with your current data stack, so you don't need to re-architect your systems or workflows.
Trigger → Cause → Outcome.
Conversion dip on mobile checkout
Checkout conversion dropped for Android mobile web in two regions
Checkout conversion dropped for Android mobile web in two regions
Recovery in hours; shared evidence trail reduced escalations
Approval rate drop for a BIN cohort
Approvals fell for a specific issuer BIN range on one gateway
Bank response shifts correlated with partner outage window
Approvals stabilized; concise incident story with next steps
Supplier errors on city-pair cluster
Approvals stabilized; concise incident story with next steps
Supplier API degradation + caching behavior for those routes
Improved bookability; clear audit trail of what changed
Built for teams that need to meet strict security and compliance requirements.
Compliant with enterprise security and privacy requirements
Role-based access control with full audit trail on every action
Track changes to definitions, thresholds, and ownership over time
SaaS or deploy in your cloud (AWS, GCP, Azure)
Zero AI training on your data. Configurable retention policies
Data encrypted in transit and at rest
In a 30-minute session, we'll connect sample data, pick one use case, and show a Detection → Cause → Action Loop.
No. BI explains what happened. Bicycle is built for what comes next: detect meaningful change, explain likely causes across business + tech drivers, and route safe actions.
Start with one priority use case and a narrow slice. Most teams aim for first wins in weeks by reusing existing sources and KPI definitions.
At minimum: the revenue-critical event stream or tables (orders/bookings/payments) plus a KPI definition source (dashboards, docs, or catalogs). More signals improve RCA accuracy.
Actions can be previewed, limited to a slice, and routed for approval. Everything is logged and reversible, so teams move quickly without creating new risk.
No. Bicycle sits on top of your warehouse/streams and operational tools, and pushes outputs back into Slack, ticketing, and automation systems.
Bicycle is an Agentic Analytics platform engineered for high-transaction businesses to optimize revenue
