Close the gap between data and product.
Data → Decisions → Shipped features. Automatically.
Capture what your users do, understand it, and ship the improvement — on one continuous loop, at a velocity teams have never had.
From raw event to deployed feature — one continuous loop
- Product analytics
- Feature flags
- A/B experiments
- Personalization
- Autonomous codegen
The three pillars of the Loop
Every product improvement comes from the same three things — done well, and done continuously.
Capture & structure
It starts with the data. Every action your users take is captured cleanly and organized into one query-ready model.
- Browser & server tracking
- One unified user identity
- Clean, query-ready schema
Understand & decide
See what's working and what isn't — then let AI design the next experiment or change to try.
- Trends, funnels & retention
- Experiments with real statistics
- AI proposes the next move
Ship — automatically
Approved changes go live as flags, variants, and shipped code, behind your safety gates.
- Flag & variant rollouts
- Pull requests on your repo
- Audit & one-click rollback
Velocity = Capture + Decide + Ship
One loop, from raw signal to shipped feature
- 1
See what users do
Every action in your product is recorded
- 2
Understand it
Spot what's working and what isn't
- 3
Improve it
AI tests and builds the next change
- 4
Ship to users
The better version goes live
From well-structured data to shipped feature
Capture and structure your product data exceptionally well — then watch it flow straight into experiments, flag changes, and shipped code. The gap between insight and implementation, gone.
Data, captured and structured
Our first priority: capture every event cleanly, unify user identity, and model it into a query-ready schema. Well-structured data is the foundation that makes everything downstream — analytics, experiments, agents — actually work.
The autonomous Loop
Agents don't just surface insights; they design experiments, adjust flags, personalize, and open PRs — behind safety gates, autonomy levels, audit, and automatic rollback.
Analytics that ship with you
Funnels, cohorts, retention, and experiment stats (frequentist, Bayesian, sequential) on a ClickHouse-backed engine.
Feature flags, evaluated locally
Identical bucketing across browser, server, and service. No hot-path round-trip. No per-check fee, ever.
Autonomous codegen
Approved proposals become branches and pull requests on your repo via a sandboxed coding agent, merged on green CI under your autonomy gate.
Truly open source (MIT)
Self-host the entire platform with one command. No lock-in, no source-available gotchas.
Safe by design
Every agent action passes a safety validator and an autonomy gate (suggest-only → full-auto), with full audit and one-click rollback.
One admin console
Flags, analytics, experiments, and agent approvals in a single open-source console — a pure API client you self-host alongside the stack, with every action reproduced as curl.
Drop in the SDK, start the Loop
Track events and read experiment variants with a few lines. Same API surface across languages.
import { APDL } from '@apdl-oss/sdk';
const apdl = APDL.init({
endpoints: { ingestion: '...', config: '...' },
auth: { clientKey: 'proj_apdl_0123456789abcdef' },
autoCapture: true,
});
apdl.track('purchase_completed', { revenue: 49.99 });
if (apdl.getVariant('new-checkout-flow') === 'treatment') {
// show the treatment experience
}APDL vs. stitching point tools together
Most teams run an analytics tool and a flag tool and bolt on experiments. APDL is all of it — plus agents that act on it.
| Capability | APDL | Analytics tool | Flag tool |
|---|---|---|---|
| Product analytics | |||
| Feature flags | |||
| Experimentation | |||
| Autonomous agents | |||
| Open source (MIT) |
Open core, priced to match how you run it
Self-host for free, let us run it for you, or get dedicated infrastructure and compliance.
Open Source
Self-host everything
Free
Developers & OSS-first teams
- Full MIT platform
- All core features
- Self-host docs
Cloud / Team
Managed, sign up & integrate
Usage-based
Startups & growth teams
- Managed hosting, no ops
- Higher limits
- Team roles
- Usage-based billing
Enterprise
Dedicated infrastructure
Custom
Large & regulated orgs
- Dedicated / isolated infra
- SOC 2 · GDPR · HIPAA
- SSO / RBAC
- SLAs & dedicated support
Flag evaluations are always free. The open-source platform is free forever — we never move existing OSS features behind a paywall.
Frequently asked questions
What exactly is APDL?
It's one open-source platform that captures how people use your product, turns that into insight, and then ships improvements — flags, experiments, and code changes — automatically. Analytics, feature flags, experiments, and AI agents in a single tool.
Do I need to be technical to get value from it?
No. The data, the insights, and the proposed changes are presented in plain language. Your team decides how much the AI is allowed to ship on its own; engineers wire up the SDK once and then mostly review.
Is it really open source?
Yes — the core platform is MIT licensed. Self-host the whole thing for free with no feature paywalls, or let us run it for you in the cloud. We also offer a separate, closed-source edition as part of our paid plans — with the extra capabilities fast-growing teams and enterprises need.
How do the AI agents ship changes safely?
Every agent action passes a safety check and an autonomy gate you control — from suggest-only all the way to full-auto. Everything is audited and reversible with one click.
How is this different from an analytics tool plus a flag tool?
Those tools tell you what happened and let you flip switches by hand. APDL closes the loop: it acts on the data — designing experiments, tuning flags, and opening pull requests — so insight becomes a shipped change without the handoffs.
Can I keep my existing stack?
Yes. Drop in the SDK alongside what you already have and start sending events in minutes. You can adopt one piece at a time.
The people behind the Loop
APDL is built by a small team that believes product development should optimize itself — open source, and out in the open.
Our Vision
A world where every product learns from its users and continuously becomes more useful, safely, intelligently, and as quickly as their needs evolve.
Our Mission
To build the open, trusted product-development loop that unifies behavioral data, analytics, experimentation, and AI-powered delivery, helping teams turn evidence into safe, measurable product improvements with unprecedented speed.
Jahaan Rawat
Co-founder
Creator of APDL — building and driving the autonomous loop that lets products improve themselves.
Kirill Sukhikh
Co-founder
Engineer working across the APDL platform — from event ingestion and analytics to the autonomous agents and codegen pipeline that turn decisions into shipped code.
Your competitors still ship on guesswork.
Close the loop today — turn your product data into shipped features, automatically. Try it free in the cloud, or self-host the whole platform.