0-code mobile analytics
The data names itself.
One line in. Drengr captures your app's real network traffic and behavior, redacts PII on-device, and discovers and names the business events by itself — no tracking plan, no data team, no track() calls.
flutter pub add drengr_flutter_sdkFlutter SDK available today — iOS, Android & Web built, in final verification.
The problem
The people running a company can't actually see it.
The founder, the CTO, the investor — everyone gets reality filtered through one data team in the middle.You instrument a hypothesis, ship it, wait weeks — and find the guess was wrong. Clarity became a luxury you ration by how much talent you throw at the middle layer.
- 01PM writes a tracking spec
- 02Eng instruments 40 events
- 03QA verifies each one fires
- 04The event still ships wrong
- 05You find out three weeks later
One line in your app. The data names itself.
Five hand-offs and three weeks — or one line.That's the whole choice.
The magic
Install it. Run your app. Watch it name itself.
No config, no tracking plan. Drengr shows you the distinct things your app actually does — and turns a raw exchange into a named business event, on its own.
0-code auto-discovery
Install it, run your app, and it shows you the Ndistinct things your app actually does — zero config, zero tracking plan.
The why behind the what
Behavior + wire + performance in one. “Users rage-quit onboarding at step 3” — rage-taps, dead-taps, frozen frames, captured by default.
You stay in control
Drengr proposes the mapping; a human accepts it. The LLM never decides — it only ever names a finding once.
This is the real console on sample data. Click anything — the filters, the CSV export, all of it works.
Why it's different
Not “we see the wire.” We name the meaning.
Plenty of tools capture a response body. The moat is auto-deriving the business event behind it — across behavior, wire, and performance. AI here is a one-time authoring step, not a runtime dependency — so every claim below states its mechanism.
Auto-understanding
The event names itself across behavior + wire + perf. That's the one thing no other tool does on its own.
How we know · The LLM proposes a name once per new request shape; a human accepts; a deterministic rule runs forever after. Your 10M events/day cost zero LLM tokens.
Verifiability > certainty
“100% you have to trust. Ours you can check.”
Every number drills to the real exchange that produced it.
How we know · Accepted rules compile to ClickHouse materialized views — same input, same output, re-runnable, no model in the hot path.
Near-LLM-free
Deterministic math does the counting and detection. The LLM only ever names a finding once — never computes, never decides.
How we know · Per-tenant daily AI budget cap — spend bounded by construction.
The accuracy doctrine
Exact on what happened, honest error-bounds on what it means — and we never fake-precise a projection.
How we know · Every proposal carries a confidence score and fires only after k≥5 distinct installs exhibit the shape.
Private by construction
Your customers' PII never reaches us readable — it's redacted on-device, before it ever leaves the phone.
How we know · PII redacted on-device — field-name + value scrubbing, Luhn/JWT/bearer detection — before anything reaches our servers or the model.
The mission
Operate at Google frequency.
Google can afford elite PMs and data scientists who guess better and rebuild the apparatus faster. Everyone else can’t. Drengr removes the rationing — an elite data org’s output, without the org.
Proof, not promises
Don't take the headline's word for it — every claim on this page is something you can click and check.
Filters, CSV export, the named events — all real, on sample data.
Open the demo→Propose once → human accept → deterministic rule. The mechanism is documented, not a black box.
Read how it works→Field-name + value scrubbing, Luhn/JWT/bearer detection — the redaction pipeline is public.
Read the security page→We don't train models. What compounds is the accepted-mapping corpus: every human accept/edit/reject makes the next proposal better. The model is a commodity; the reviewed annotation library is the asset.
Objections, answered
The questions you're already asking.
What leaves the device?
Redacted, projected analytics scalars only. Field-name and value scrubbing plus Luhn/JWT/bearer detection run on-device — raw PII is never readable server-side.
Why not Firebase, Amplitude or Mixpanel?
They need a tracking plan and instrumentation before they show you anything. Drengr captures and names events without one — and they never see the response body.
Which platforms?
Flutter is available today. iOS, Android and Web SDKs are built and in final verification.
What does the AI actually do?
It proposes a name once per new request shape. A human approves it. A deterministic rule handles everything after — no model in the hot path.
What does it cost?
Free during the current rollout. When metering ships, you'll set your own spend caps — never a surprise bill.
Stop guessing what to measure.
flutter pub add drengr_flutter_sdkFlutter today · iOS, Android & Web in final verification — get notified