Bicycle detects the move, explains the business and technical causes, and recommends a safe next step. It plugs into the data stack you already use.
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A KPI at rest carries no new information. Attention should start the moment it shifts, and only then.
Seeing the dip is the easy part. Knowing why it moved and what to do next is the hard 95%.
Carry a signal all the way to a confident decision. That is what analytics should be about.
A number sitting still tells you nothing new. And a number that just moved is still only a signal, not a decision. The real work is the distance between "something changed" and "here is what to do about it." Closing that gap is what analytics should be about. It is what Bicycle is about.
Every time a revenue-critical KPI moves, Bicycle runs one loop end to end. Not an agent for the sake of being an agent. An agent loop with a single job: turn a signal into a decision, and get sharper at it every cycle.
A KPI rarely moves for one tidy reason. The cause can sit in the business or buried deep in the stack, so Explain has to test both at once, then prove which one it was. This is the step where every other approach quietly hands the work back to you.
Governed by KPIs like auth rate, approval rate, payment completion.
Influenced by business factors like pricing, routing rules, fraud policy settings.
Governed by metrics like gateway latency, processor uptime, error rates.
Influenced by technical factors like routing changes, feature flags, network health.
Bicycle tests business and technical drivers in parallel, powered by the multi factor cause analysis. Deterministic statistics, not a language model's guess. That engine is the hardest part to build, and the reason every answer arrives with evidence instead of a confident-sounding sentence.
Every team already has dashboards, data pipelines, or an AI chatbot. Bicycle works on top of all of them. See exactly how it differs.
Two things you set up once, on the stack you already have, and then the same Detect → Explain → Act → Learn cycle runs every time a revenue-critical KPI moves.
Connect existing systems. No migration, no rebuild. Bicycle reads what you already have and proposes a reviewable starting point.
Apply the vertical pack and the context that makes outputs read like a peer wrote them, not a system. Override anything.
Once setup is done, the agent runs every day.
Watch every KPI and slice. Surface meaningful movement before anyone has to ask.
Test business, technical, partner, and external drivers in parallel. Return the likely cause with evidence.
Recommend the next step. Route the owner. Preview, scope, approve, log. Every action inside governance.
Capture accepted causes, outcomes, and reusable context. The next explanation is faster and more relevant.
Same setup. Same sequence. The block sits on persona pages and vertical pages with the right lens preselected.
Bicycle combines fast activation, vertical context, continuous KPI intelligence, cause analysis, defensible answers, and governed action paths into one operating system.
Capabilities work together continuously to drive better outcomes every time.
Explore the playbookBusiness teams can self-serve because D&A controls the foundation, the intelligence layer, and the action boundaries. Neither side learns the other's job.
Review the events, dimensions, KPIs, joins, and context Bicycle proposes. Approve the model before the business sees it.
Tune pattern detection, driver trees, cause connectors, thresholds, and recommended actions as the system learns.
Set roles, access, approvals, action permissions, audit trails, and rollback paths.
See which KPI moved, where it is concentrated, and how big the impact is.
See the likely cause, supporting evidence, and what Bicycle checked but ruled out.
Take or request the next step inside approved guardrails.
Bicycle is the agentic layer that turns business signals into alerts, stories, dashboards, chat answers, triage, and governed actions. It reads your stack; it does not replace it.
Connect existing systems. No migration. Bicycle reads what you already have and proposes a reviewable starting point.
Apply the vertical pack and business context so outputs read like a peer wrote them, not a system.
Frames the question, orchestrates the investigation, writes the story, recommends the next step. It reasons.
Detects movement, ranks drivers, computes confidence, forecasts. The numbers are calculated, not generated.
And the rest of what you run: warehouses · streams · observability · payments · tickets · docs · BI · operational systems.
Bicycle runs on top of the systems you already use, and your data can stay where it already lives.
Bicycle detects the move, finds the cause, and recommends the next step on the revenue KPIs critical to your business.
Bicycle handles the recurring investigations so your team works on what matters. Defensible chains, audit logs, governed self-serve.
Bicycle gives leadership signal-to-decision in one read: the move, the cause, what's being done, what changed since last week.
Try the self-service entry point first. Ask a business question, connect a trusted data source, and see how Bicycle turns prompt plus data into an analytics agent.
No credit card required
Walkthrough · 30 minutesSee it on a use case that mirrors yours. A use case in your vertical, pre-loaded. Bring the question. We bring the example.
BI shows what happened. Bicycle explains why and what to do next. It sits on top of the BI you already trust, runs the investigation across business and technical signals, and recommends the next step.
Most AI analytics starts when someone asks. Bicycle starts when the business changes. Chat is one surface inside Bicycle, not the product. The Analytics Agent watches the KPI before you knew to ask.
No. Bicycle works on top of the stack you already use. Data & Analytics teams stay in control of definitions, evidence, publishing, and access. Business users self-serve through that governed layer, not around it.
Warehouses, event streams, BI assets, dbt models, payment systems, supplier systems, catalog and inventory, logs, incidents, tickets, runbooks, Slack, and email. No migration required. Data can stay where it already lives.
First useful answer in days, not months. The two-week trial delivers one working agent for one KPI by day 14: connect by day 2, first proposed agent by day 4, review and publish by day 10, measure value by day 12.
No. Your raw data is never sent to the AI model and never leaves Bicycle's secure runtime. AI generates structured artifacts (mappings, rule-sets, KPI logic); those are evaluated inside the runtime against your data, and Bicycle does not train any model on your data. OpenAI runs with Zero Data Retention. SOC 2 Type II and GDPR.
Bring one KPI that matters. We'll show how Bicycle detects movement, explains the cause, and recommends the next step.
No credit card required