The parameter control plane
One platform for experiments, feature flags, and adaptive optimization
Optimize checkout conversion, pricing, or feature rollouts — automatically. One system instead of fragmented tooling.
- Define once, control behavior across web, mobile, push, and backend
- Resolved locally in the SDK — sub-millisecond, no network round trips
- Automatically allocate traffic to better-performing variants with adaptive bandits
What teams build with Traffical
From checkout optimization to AI model tuning — Traffical controls the parameters that drive your business, not just feature toggles.
Optimize checkout pricing and incentives
Problem: Your discount strategy is hard-coded. Changing it means a deploy. You have no idea if 15% or 25% converts better.
How Traffical solves it: One parameter controls discount levels across web, app, and email. Test pricing variants with adaptive optimization — Traffical shifts traffic to the one that maximizes revenue.
+12% revenue per session with zero deploys
Optimize checkout pricing and incentives
E-commerceYour discount strategy is hard-coded. Changing it means a deploy. You have no idea if 15% or 25% converts better.
One parameter controls discount levels across web, app, and email. Test pricing variants with adaptive optimization — Traffical shifts traffic to the one that maximizes revenue.
+12% revenue per session with zero deploys
Tune product ranking and recommendation weights
E-commerce · MLYour ranking algorithm has magic numbers buried in code. Tuning them means a PR, a review, and a deploy.
Expose boost thresholds, scoring weights, and relevance parameters to Traffical. Tune in production without code changes. Let bandits find the optimal values automatically.
Ship ranking improvements in minutes, not sprints
def rank_products(items):
boost = traffical.get("ranking.boost")
# Currently: 0.55
return sort(items, boost)Find the highest-converting onboarding flow
SaaSYour onboarding has 7 steps. You think it should be 5. You have no data to back the decision.
Control step count, content, gamification, and paywall placement as parameters. Run experiments across the entire funnel with layered isolation.
3x faster iteration on activation funnels
A/B test prompts, models, and AI parameters
AI · MLYour AI feature ships with hardcoded temperature, system prompts, and token limits. You iterate by deploying.
Control model temperature, prompts, max tokens, and fallback thresholds as parameters. A/B test prompt variants. Let contextual bandits personalize per user segment.
Ship prompt improvements without touching code
Maximize cart recovery across channels
E-commerce · CRMYour cart recovery emails use the same subject line and discount for everyone. You have no idea which offer drives more revenue.
Optimize email timing, subject lines, discount levels, and push copy with adaptive bandits. Traffical learns per-segment what converts best.
Per-segment optimization without manual rules
Safely adjust fraud and risk thresholds
Fintech · MarketplaceYour fraud detection threshold is set to 0.7. Too aggressive and you block good users. Too lenient and you eat losses.
Expose scoring thresholds, approval rules, and verification triggers as parameters. Use layered isolation to test safely on a percentage of traffic.
Reduce false positives without increasing fraud
Define once. Use everywhere.
Feature flags are booleans — on or off, for one surface. Traffical parameters are typed configuration values that power experiments, rollouts, and optimization. Change one parameter and every connected surface updates instantly — website, mobile, push, and algorithms.
From decision to deployment in one flow
Drag the knobs to control parameters across web, mobile, push, and algorithm surfaces. Switch modes to see manual control, A/B testing, and AI-powered optimization.
All users see the same values
No duplication
One parameter feeds every surface
No runtime API calls
Config bundles resolved locally
Works at the edge
Served from Cloudflare KV globally
One system. From experiment to full rollout.
Other tools make you choose between feature flags and experiments. Traffical unifies them. Start with a flag, graduate to an A/B test, let AI optimize — all on the same parameter, without switching tools.
Feature Flags
Turn features on or off for specific segments. The simplest parameter policy.
Progressive Rollouts
Ship changes safely with canary releases and gradual ramps. Monitor health at every stage.
A/B Tests
Compare variants with statistical rigor. Measure business impact before committing.
Adaptive Optimization
Let the platform learn which variant performs best and shift traffic automatically.
Start as an experiment. Automatically shift traffic to the winner. End in an optimized rollout — no manual intervention required.
Stop wasting traffic on losers
Static A/B tests keep a fixed 50/50 split for the entire experiment — even after one variant is clearly ahead. Adaptive optimization starts the same way, but shifts traffic toward the winner as evidence builds.
Static A/B Test
Adaptive Optimization
Developer-first, always
SDKs for every framework. A CLI that makes config version-controlled. Your biggest advantage: parameters resolved locally, with zero network dependency.
Sub-millisecond latency
Resolved locally in the SDK
No network calls at runtime
Config bundles synced ahead of time
Config-as-code
CLI-driven, version-controlled YAML
Integrate in minutes
import { useTraffical } from "@traffical/react";
function CheckoutPage() {
const { params, track } = useTraffical({
defaults: {
"checkout.discountPercent": 15,
"checkout.ctaColor": "#1E6EFB",
"checkout.urgencyLevel": "low",
},
});
return (
<Button
color={params["checkout.ctaColor"]}
onClick={() => track("purchase")}
>
Buy Now — {params["checkout.discountPercent"]}% Off
</Button>
);
}Config-as-code with the CLI
Your warehouse. Your metrics. Full control.
Traffical computes experiment metrics directly against your data warehouse. No black-box analytics. No data extraction. No third-party storage.
Send exposure & outcome data to your warehouse
Run significance tests on data you own
Use your existing BI stack as-is
Why Traffical
Most teams stitch together a dedicated flagging product and a separate experiment platform. Traffical unifies flags, experiments, and optimization in one system — with an architecture built for performance.
| Capability | Flagging service | A/B testing product | Traffical |
|---|---|---|---|
| Feature flags | ? | ||
| Progressive rollouts | |||
| A/B experiments | ? | ||
| Adaptive optimization (bandits) | ? | ||
| Typed parameters (not just booleans) | ? | ||
| Universal parameter control | |||
| Local SDK resolution (no API calls) | ? | ||
| Warehouse-native metrics | ? | ||
| Config-as-code (YAML + CLI) |
? = varies by vendor or requires add-ons
A typical flagging service covers toggles. A typical A/B product covers experiments. Traffical covers both — plus adaptive optimization — in one place.
Built by engineers from Zalando and Contentful
Years of experience building experimentation platforms at scale — now available as one product.
Stop hard-coding decisions.
Start controlling them.
Join teams using Traffical to optimize pricing, tune algorithms, and personalize experiences — without deploying code.