Compare Flywheel
Your tools document experiments and run tests. Nothing governs the evidence they produce.
Flywheel is the governance layer your current stack is missing.
It replaces the patchwork of spreadsheets, project tools, and workarounds with a single platform for governance, synthesis, and decision tracking.
Flyweel vs Your Current Stack
Most organisations manage experiments and research across a combination of general-purpose tools. Each does its job. None provides governance, synthesis, or decision tracking.
Google Sheets / Excel
Tracks experiments in rows and columns. Flexible but unstructured.
Cannot enforce required fields, detect methodology changes, or connect results to decisions.
Asana / Jira
Manages experiment and research tasks and timelines. Good for workflow.
No concept of hypotheses, success metrics, decision rules, or governance standards.
Airtable / ClickUp
Flexible databases that can be shaped into experiment trackers.
No audit trail on field changes. No collision detection. Documentation can be backdated.
Dovetail / Condens
Organises research data: tagging, transcription, and search. Good for the research team.
Research stays within the repository. No connection to experiments or decisions.
Notion / Confluence
Documents experiments and research in rich text pages with free form editing
Pages are not structured data. Cannot query, compare, or synthesise across studies.
Shared drives / Slides
Stores research reports, documents and experiment presentations.
Research gets buried after presentation. No discoverability, no synthesis, no impact tracking.
Slack / Email
Ensure inclusivity in design by optimizing for diverse user needs.
Conversations disappear. Knowledge is trapped in threads. Nothing is searchable or structured.
| Capability | Your current stack | Efestra Flywheel |
|---|---|---|
|
Strategic alignment |
Experiments and research exist independently of business objectives. No structural connection. |
Every experiment and study connects to a strategic objective and decision owner before it begins. |
|
Governance enforcement |
Relies on people following process manually. Nothing prevents shortcuts. |
Required fields, approval workflows, and quality gates enforced by the platform. |
|
Decision tracking |
No system captures which decisions were informed by experiments or research. |
Full trail: which experiments and research informed which decisions. Specialists notified when their work drives action. |
|
Quality oversight |
Post-hoc hypothesis changes and metric drift are invisible. |
Automatic detection of methodology violations with full audit trail. |
|
Research synthesis |
Manual analysis across studies. Findings stay in the repository or the shared drive. Executives rarely see them. |
AI-assisted synthesis connects findings across experiments and research into certified briefs delivered to decision owners. |
|
Research-to-decision connection |
Research repositories organise studies but do not track whether findings influenced action. They solve findability, not influence. |
Every study has a named decision owner. Research reaches executives in the format they need. Impact is measured, not assumed. |
|
Collision detection |
No visibility into whether concurrent experiments overlap. |
Automatic collision detection across all running experiments. |
|
Institutional memory |
Experiment results in archived tickets. Research in shared drives. Knowledge leaves when people leave. |
Searchable knowledge base covering both experiments and research. Full history. Nothing gets lost. |
|
Executive visibility |
Executive Requires someone to build a report manually. Often monthly or quarterly. |
Live dashboards showing governance health, decision influence, and verified impact across both experiments and research. |
The real cost of your current stack is not the subscriptions. It is the evidence that never reaches decisions, the governance failures nobody detects, and the duplicate work nobody prevents.
Flyweel vs Your Testing Tool
Your testing tool is excellent at running experiments.
Flywheel sits before and after the testing tool's execution layer and governs what happens before, during, and after the test.
| Spreadsheets & Documents | Efestra Flywheel | |
|---|---|---|
|
Core purpose |
Run experiments. Execute variations, collect data, calculate significance. |
Govern the evidence. Ensure it is trustworthy, synthesised, and reaches decisions. |
|
Ideation and planning |
Some offer hypothesis backlogs with prioritisation scoring (VWO Plan, Optimizely Collaboration). Focus is on which test to run next. |
Evidence-gap-driven planning. Ideas connect to business objectives and decision owners. Backlog reflects strategic needs, not just execution capacity. |
|
What happens before launch |
Limited. Some allow hypothesis fields and approval workflows, but evidence quality checks are not enforced. |
Implementation tracking, predicted vs actual comparison, decision recording. |
|
Research integration |
None. Testing platforms do not handle qualitative research or UX studies. |
Research and experiments governed in one system. AI synthesis connects them. |
|
Strategic alignment |
Some allow tagging. No structural connection to business objectives. |
Every study maps to objectives and decision owners. Coverage gaps are visible. |
|
Executive value |
Dashboards show test volume, win rates, and velocity. Activity metrics. |
Dashboards and reports show decisions influenced, governance health, and verified business impact. |
|
Institutional memory |
Results stored but not synthesised. Past findings not connected to current work. |
Full knowledge base with cross-study synthesis and duplicate detection. |
Your testing platform is excellent at execution of tests.
Flywheel connects to your testing tools and adds the governance layers they were never designed to provide.
Flywheel vs. other approaches
Some organisations attempt to solve governance through spreadsheets, consultancy, or by building their own solution. Each has trade-offs.
| Approach | What it provides | What it cannot do |
|---|---|---|
|
DIY tracker in Notion, Airtable, or Jira |
A summary of whatever document you paste in. Useful for individual productivity. No connection to business context, objectives, or stakeholders. |
Documentation is not governance. Fields are optional, not enforced. No collision detection, no tamper-proof audit trail, no synthesis across studies, and no connection between evidence and decisions. Breaks down when teams scale or people leave. |
|
Automations and integrations layered on top |
Zapier workflows, Slack notifications, Airtable automations, and Jira triggers stitched together to create alerts and routing. Teams invest real effort building these and often believe they have solved governance. |
Automations notify. They do not enforce. A Slack alert when a hypothesis field is empty does not prevent the experiment from launching without one. A Jira workflow cannot detect collisions, create tamper-proof audit trails, or prove that a metric was not changed after launch. These systems track activity, not integrity. When leadership asks whether the evidence is trustworthy, notifications are not proof. |
|
Consultancy-designed programme |
External expertise for programme design, methodology training, and maturity assessment. Good for diagnosis and establishing best practice frameworks. |
Governance recommendations without enforcement. The advice is sound but the infrastructure to sustain it does not exist once the consultant leaves. Methodology decays within months without systematic enforcement. |
|
Consultant-built custom tracker |
A consultant helps build a custom tracker in Airtable or Jira with automations layered on top. Better than a raw DIY setup because the schema is designed by someone who understands experimentation. |
Still lacks tamper-proof audit trails, collision detection, synthesis, and decision tracking. Fragile: breaks when the original builder is unavailable. The automations create an illusion of governance without the enforcement that makes it real. |
|
Internal engineering build |
Complete control over features and workflow. Fits your exact process. Can be deeply integrated with internal systems and data infrastructure. |
Engineering cost of 6 to 12 months minimum. Ongoing maintenance burden. No synthesis capability. Opportunity cost of building governance infrastructure instead of product. Rarely includes the decision-layer features that make governance valuable to executives. |
See how Flywheel compares to your specific setup
Book a discovery call and we will map your current stack against the Decision Intelligence Stack.
