Use Cases
One control plane, endless possibilities
From checkout optimization to AI model tuning — Traffical controls the parameters that drive your business. Not just feature toggles.
Optimize checkout pricing and incentives
The problem
Your discount strategy is hard-coded. Changing it means a deploy. You have no idea if 15% or 25% converts better.
With Traffical
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.
Key strength: One parameter → many surfaces
Tune product ranking and recommendation weights
The problem
Your ranking algorithm has magic numbers buried in code. Tuning them means a PR, a review, and a deploy.
With Traffical
Expose boost thresholds, scoring weights, and relevance parameters to Traffical. Tune in production without code changes. Let bandits find the optimal values automatically.
Key strength: Algorithm tuning in production
def rank_products(items):
boost = traffical.get("ranking.boost")
# Currently: 0.55
return sort(items, boost)Find the highest-converting onboarding flow
The problem
Your onboarding has 7 steps. You think it should be 5. You have no data to back the decision.
With Traffical
Control step count, content, gamification, and paywall placement as parameters. Run experiments across the entire funnel with layered isolation.
Key strength: Multi-step parameter control
A/B test prompts, models, and AI parameters
The problem
Your AI feature ships with hardcoded temperature, system prompts, and token limits. You iterate by deploying.
With Traffical
Control model temperature, prompts, max tokens, and fallback thresholds as parameters. A/B test prompt variants. Let contextual bandits personalize per user segment.
Key strength: AI-era parameter control
Maximize cart recovery across channels
The problem
Your cart recovery emails use the same subject line and discount for everyone. You have no idea which offer drives more revenue.
With Traffical
Optimize email timing, subject lines, discount levels, and push copy with adaptive bandits. Traffical learns per-segment what converts best.
Key strength: Cross-channel adaptive bandits
Safely adjust fraud and risk thresholds
The problem
Your fraud detection threshold is set to 0.7. Too aggressive and you block good users. Too lenient and you eat losses.
With Traffical
Expose scoring thresholds, approval rules, and verification triggers as parameters. Use layered isolation to test safely on a percentage of traffic.
Key strength: Safe backend experimentation
Your use case here?
Every configurable value in your product — pricing, thresholds, prompts, weights, timings — can be a Traffical parameter. If you can express it as a value, you can experiment with it.