Developer Tools
Build an A/B testing and experimentation platform
Kliv is an AI app builder. Describe the A/B testing platform you need — sticky assignments, events, metrics, guardrails, and decisions — and it builds the app around your data.
Just enter your idea into the text box and AI will build it for you
What is Kliv?
Kliv isn’t an experimentation suite you sign up for and configure — it’s an AI app builder that builds software for you.
You describe the app you need in plain words, and Kliv builds it: the data, the logic, the screens, and the workflow. What you get is a real working app that’s yours. You own it, run it on your own domain or accounts, and keep changing it later by asking. It isn’t a template with your name on it.
On this page, that app is a custom A/B testing and experimentation platform. Kliv builds all kinds of web apps; this is one example.
Why build your own experimentation platform?
Many experimentation tools are priced for traffic, not for how many tests you actually run. A team may run six experiments a quarter and still pay for every tracked user. The harder problem is data: assignments often live in the vendor’s warehouse, away from the orders, subscriptions, and retention numbers that decide whether a test mattered.
Kliv builds the experimentation platform around your own data. Experiments, assignments, events, metrics, and decisions are rows in your database, next to the revenue and customer records they need to explain.
What the system knows
An experiment needs facts that stay put. These are the records Kliv builds around.
Experiments and variants
Each experiment has a hypothesis, variants, traffic split, owner, start date, stop date, and minimum sample size. It is a record your team can inspect, not a note in someone’s head.
Sticky assignments
A visitor’s variant is written once and read back on every return. A redeploy or split change does not quietly move that visitor into another variant.
Events and metrics
Signup, first order, cancellation, retention, revenue — the events that decide experiments are defined clearly and counted per variant.
The results board
Dashboards show per-variant conversion, sample sizes, and whether the test has enough data. A scheduled job rolls results up on the cadence you choose.
Guardrails
Rules define who may launch, ramp, stop, or archive a test. Every ramp and stop is logged with the person, time, and numbers behind the decision.
An example: Nadia’s meal-kit growth team
Here’s how one growth team might use it. It’s only an example — you would describe your own experiments, metrics, and decision rules.
The hypothesis is written down
Nadia’s team wants to test whether showing delivery dates before checkout increases first orders. They record the owner, primary metric, variants, and minimum sample before traffic sees anything.
Visitors are assigned through an API
The site calls the assignment endpoint and starts with 10% of new visitors. Each assignment is stored, so a returning visitor sees the same variant.
Events accumulate against each variant
Signup and first-order events land as rows. The nightly rollup updates the dashboard, so the team watches the same numbers instead of each person running their own query.
The team records the decision
When the sample is reached, variant B is up 9% on first orders. Nadia records the decision on the experiment with the numbers as they stood that day.
The winning variant ships with history intact
Variant B ramps to everyone and the experiment archives with assignments, events, and the decision log. Two quarters later, “did we test this?” has a linkable answer.
Describe the experiment platform you want
Kliv builds from your description, so the more detail you give, the closer the first version. Name the experiments, the events, the traffic rules, and the decision process. Here are three to build on:
Growth experiment tracker
Splits, sticky assignments, dashboards, and launch rules.
“Build an experimentation platform for our meal-kit growth team. Support experiments with two to four variants, percentage traffic splits, sticky visitor assignments through an API, signup and first-order conversion events, minimum sample sizes, nightly results rollups, and a decision log for launch, ramp, stop, and archive.”
Pricing test manager
Pricing page variants tied to revenue.
“Build a pricing experiment tool for our SaaS site. Test Starter at $19, Pro at $49, and Team at $129 a month, assign visitors sticky variants, record checkout starts, paid conversions, and first-month revenue per variant, and require a written decision before any price goes to 100% of traffic.”
Newsletter subject-line lab
Campaign variants, send sizes, and open events.
“Build an experiment tracker for our weekly newsletter. Let editors create subject-line variants, set the send size for each variant, import open and click events per campaign, compare results only after the minimum sample is reached, and archive the final subject line with the decision notes.”
How it comes together
Assignment is a function you own. The split logic, exclusions, and holdouts live in a server-side function with a real API. Your site, store, or email system calls it, and the answer is stored as a row.
Revenue joins stay simple. Assignments and outcomes live in the same database, so “did variant B improve six-month retention?” is a query over your own rows, not a reconciliation job between two exports.
Peeking becomes a rule. Minimum samples, nightly updates, and stop decisions live on the experiment record. The platform enforces the discipline your team wants from its tests.
Kliv’s analytics already covers visitors, conversion, revenue per channel, and custom events. Your experimentation platform adds controlled comparisons on top of that measurement layer. See Kliv analytics.
Common questions
What exactly is Kliv?
Kliv is an AI app builder. You describe the A/B testing platform you need, and Kliv builds a real app with the database, API, screens, and rules for running experiments.
Do I get a real app, or a template?
A real app. The experiment records, assignment endpoint, event tables, dashboards, and decision log are built around your workflow, not a generic form you fill in.
Can I change it after it’s built?
Yes. You can ask Kliv to add a metric, change the launch rules, add a holdout group, or update the dashboard without starting over.
Why build this instead of subscribing to an experimentation suite?
If your main problem is that assignment and revenue data need to live together, a custom app can be simpler. Your assignments, events, and outcomes are rows in your database, joined to your own revenue tables.
How is assignment stickiness guaranteed?
The visitor’s variant is stored on first assignment and read back later. It is stored state, so a redeploy or traffic split change does not silently reshuffle users.
Can experiments run on sites not hosted by Kliv?
Yes. The assignment endpoint is an API. Your storefront, landing pages, or email tooling can call it, and events can post back the same way.
What stops someone from ramping a test too early?
You define who can ramp, stop, or archive tests. Minimum sample sizes and decision notes live on the experiment record, and every change is logged with a name and time.
Can results join to revenue and retention?
Yes. Because assignments and events live in your database, you can join them to orders, subscriptions, retention, or any other rows your app records.
Will it replace analytics?
No. Analytics tells you what happened. Experimentation adds controlled assignment and comparison, so you can test whether a change caused an outcome.
Can I move off Kliv later?
Yes. The app’s code syncs to your own Git repository, so leaving is a git pull, not a rebuild.
Do I need a separate admin tool?
No. The platform is its own admin: your team creates experiments, checks results, records decisions, and manages rules in the app itself.
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Make decisions from stored assignments
Describe the variants, metrics, traffic rules, and guardrails in detail. Kliv builds the experimentation platform that keeps the test and the outcome in the same database.
