Engineering scaled with DevOps. Product scaled with no-code tools. Bicycle lets data teams scale the same way—consistent definitions, continuous detection, and governed self-serve on top of your existing stack.
Inconsistent schemas, siloed sources, brittle joins → low trust and slow delivery.
“Who owns this KPI/table?” becomes debate, duplication, and risk.
Dashboards/models don’t get used because context wasn’t captured early.
POCs stall on monitoring, drift, and ongoing maintenance overhead.
Too many point solutions → integration replaces analysis time.
Without outcomes tied to revenue/cost/risk, support fades.
“We don’t need more dashboards. We need consistent definitions, faster RCA, and outputs the business can act on—safely.”
Three capabilities: onboard a shared ontology, run continuous pattern + cause analysis, and deliver governed self-serve outputs (alerts, stories, Q&A) to the business.
Data + Knowledge → Ontology + Use Case Agents
New domains take weeks: reverse-engineer schemas, rebuild semantic layers, re-implement KPI logic, and track down the context living in dashboards and docs.
Bicycle ingests your tables plus KPI catalogs, dashboard artifacts, and SOPs to propose an ontology (events, dimensions, cohorts, KPIs) and the first set of use case agents—then your team reviews and refine.
Faster time-to-first-use-case (days/weeks, not quarters)
Consistent KPI definitions across teams and tools
Less rework when schemas evolve
Institutional memory that survives turnover
Data + Knowledge → Ontology + Use Case Agents
Reactive analysis repeats: one-off queries, ad-hoc tests, and inconsistent RCA across segments and teams. The same investigations happen again and again.
Bicycle runs continuous AutoML scans across the ontology—anomalies, drifts, trends, forecasts, mix shifts, funnel breakpoints—ranked by impact. When a pattern triggers, the cause engine tests business and technical factors and returns ranked hypotheses with evidence trails.
Fewer bespoke anomaly pipelines to build and maintain
Consistent, auditable RCA instead of “it depends on the analyst”
Less alert noise—prioritized by impact and persistence
Faster handoff to business owners with clear evidence
Data + Knowledge → Ontology + Use Case Agents
Without guardrails, self-serve leads to wrong definitions and risky data handling. With too many guardrails, the data team becomes the permanent bottleneck.
Bicycle provides a governed layer: approved KPIs, validated slices, role-based access, and auditable outputs. Business teams get alerts, stories, and Q&A tied to the ontology—without bypassing governance or privacy.
Higher adoption of analytics outputs (less shelfware)
Fewer repetitive “urgent asks” and Slack archaeology
Clear ownership and consistent semantics across teams
A practical path from insight → action with approvals and logging
You already have a warehouse, pipelines, BI, and ticketing. Bicycle sits on top and turns data + docs into a consistent ontology, continuous detection, and governed outputs for the business.
No rip-and-replace. Start with a priority use case and expand.
Track time-to-value and outcome metrics per use case: faster onboarding, faster RCA, higher adoption, and measurable revenue/cost/risk impact.
To first useful use case agent (not quarters)
Time from detection → “likely cause” for recurring incidents
Business self-serve adoption (alerts/stories/Q&A)
Duplicate pipelines and KPI re-implementations
KPIs differ across tools and teams
RCA repeats because context isn’t captured
POCs don’t operationalize due to drift/maintenance
Adoption stalls: business doesn’t trust or can’t act
One ontology for consistent definitions
Continuous pattern + cause engine with evidence trails
Governed alerts/stories/Q&A that drive adoption
Outcome tracking per use case (revenue/cost/risk)
Book a 30-minute working session. Start with one priority use case, ingest a sample dataset plus KPI docs, and see the first agents, patterns, and ranked causes Bicycle produces.
Bicycle is an agentic analytics platform designed for B2C revenue optimization
