An AI-assisted operating system for experimentation teams.
Connect research, ideas, experiments, results, imports, and daily decisions in one workflow, so your team knows what to test next and why it matters.
Understand the problem
Connect customer observations to an insight worth acting on.
A daily loop from signal to shipped learning
Replace scattered docs, old spreadsheets, and isolated A/B test reports with a system that keeps context available at the moment teams make decisions.
A fictional Northstar Store team opens its morning briefing, reviews research about delivery costs and returns, and asks the Agent to explain what to focus on next.
ConversionLab Agent and daily briefing
Start each day with a brief that highlights stalled tests, missing decisions, and the next actions worth taking. Ask the Agent to explain the context, find related learning, or turn the recommendation into follow-up work.
Connected experiment memory
Link insights, ideas, experiments, variations, decisions, and results so the full history stays available when teams plan new work.
Bring existing work into the system
Use Source Import and Airtable mapping to move historical research, backlogs, and experiment logs into ConversionLab without flattening the context.
Prioritize and plan the next tests
Score ideas with shared models, align them to the roadmap, and keep business context visible before experiments reach execution.
Read results and keep the learning
Analyze variants, check execution health, record decisions, and preserve learning outside the A/B tool that happened to run the test.
Every new test should make the next decision easier
ConversionLab gives growth, CRO, and product teams one reliable process for turning evidence into decisions and decisions into reusable learning.
Context types connected from insight to decision.
Daily brief pointing to the next action.
Learning trapped inside an A/B vendor.
Shared memory
Reusable learning
Every result feeds the next test.
Insight captured
Checkout friction
Research and survey evidence becomes reusable context.
Insight added
Payment hesitation
Survey notes show shoppers pausing before payment.
New insight from result
Mobile still hesitates
The result exposes a fresh segment question.
Follow-up insight
Returning users skip wallet
Segment behavior opens another follow-up.
Idea prioritized
Simplify checkout form
The team turns that evidence into a scored change.
Idea created
Wallet checkout
Evidence becomes a scored mobile checkout idea.
Idea refined
Reassure wallet users
The new insight sharpens the next idea.
Next test queued
Saved-wallet default
The next prioritized idea is ready for backlog.
Experiment shipped
5-field form
A concrete test is created with metric and audience.
Experiment created
Express checkout test
The idea turns into a queued test for mobile users.
Experiment drafted
Wallet reassurance copy
A second test is drafted from the same chain.
Result recorded
Winner, +4.2 pts
The outcome is saved with the decision it supports.
Result logged
+4.2 pts saved
The lift is attached to the checkout context.
Result recorded
+1.6 pts on mobile
Mobile lift is saved with segment detail.
Decision reused
Agent-ready learning
Briefs and agents can pull the learning forward.
Brief updated
Reuse checkout learning
Tomorrow's brief points to the next move.
Decision reused
Trust copy in brief
The learning becomes guidance for future work.
Insight captured
Checkout friction
Research and survey evidence becomes reusable context.
Insight added
Payment hesitation
Survey notes show shoppers pausing before payment.
New insight from result
Mobile still hesitates
The result exposes a fresh segment question.
Follow-up insight
Returning users skip wallet
Segment behavior opens another follow-up.
Idea prioritized
Simplify checkout form
The team turns that evidence into a scored change.
Idea created
Wallet checkout
Evidence becomes a scored mobile checkout idea.
Idea refined
Reassure wallet users
The new insight sharpens the next idea.
Next test queued
Saved-wallet default
The next prioritized idea is ready for backlog.
Experiment shipped
5-field form
A concrete test is created with metric and audience.
Experiment created
Express checkout test
The idea turns into a queued test for mobile users.
Experiment drafted
Wallet reassurance copy
A second test is drafted from the same chain.
Result recorded
Winner, +4.2 pts
The outcome is saved with the decision it supports.
Result logged
+4.2 pts saved
The lift is attached to the checkout context.
Result recorded
+1.6 pts on mobile
Mobile lift is saved with segment detail.
Decision reused
Agent-ready learning
Briefs and agents can pull the learning forward.
Brief updated
Reuse checkout learning
Tomorrow's brief points to the next move.
Decision reused
Trust copy in brief
The learning becomes guidance for future work.
Frequently asked questions
A quick overview of pricing, packaging, and AI-assisted workflow choices for ConversionLab.
No. You can start on Free without a card. Explicitly selected paid plans start with a 14-day trial.
Core pricing is based on plans, users, projects, and agency client workspaces. AI is included under fair use at launch.
Team includes 3 full users, 20 viewers, 5 projects, unlimited records, collaboration, roles, integrations, and team AI context.
Yes. You can start on Team and upgrade when needed.
Ask AI about ConversionLab
Ask your favorite AI about ConversionLab’s strengths, limitations, and fit for your team.
I'm evaluating ConversionLab (conversionlab.app), experimentation management software that connects research, ideas, experiments, and results for in-house experimentation teams and CRO agencies. What does it do, what are its strengths and weaknesses, and who is it best for?
See what your experimentation loop should look like
We’ll walk through your current tools, history, and team process, then show where ConversionLab fits.