Services

Growth Experimentation & Commercial Optimization

When growth decisions rely on opinion, not evidence.

Quick view

Who it’s for: Teams serious about compounding growth, not one-off wins.

Category: Optimization & Scale

Experiment design + governanceConversion rate optimization

Context / why this problem exists

Without structure, experimentation becomes random and learning is lost.

When this is the right solution

  • Growth levers are unclear
  • Teams debate instead of test
  • Scale requires discipline

How OUTLIER approaches it

We build test-and-learn engines with governance.

What changes after

  • Faster learning
  • Compounding performance
Book a working sessionView more servicesNot sure? Build a disciplined growth engine

Recent proof points

LINE Platform

LINE mini app overhaul readiness

LINE Platform

Validated

Validated (Lab tested)

LINE mini app overhaul readiness

Prepared a production-ready overhaul framework to migrate standard LINE LIFF experiences into a scalable LINE mini app — designed around habit loops, rewards, and partner activation.

  • End-to-end mini app architecture replacing standard LIFF flows
  • Reward logic and habit loop mechanics tested in controlled environments
  • Integration patterns for retail partner participation and campaign scaling
  • Deployment playbook ready for brand rollout
Services

GenAI support copilot with RAG

Services

Validated

Digital services company

GenAI support copilot with RAG

Designed an intelligent, on-brand GenAI copilot that turns internal knowledge into real-time, personalized support — learning continuously from customer behavior while operating within strict governance and legal guardrails.

  • Controlled RAG engine trained only on approved internal knowledge
  • Built-in guardrails to ensure brand safety, accountability, and legal compliance
  • Human-in-the-loop escalation for sensitive or high-impact interactions
  • Self-learning feedback loop using real user input and behavior
  • CRM-integrated intelligence to tailor responses by customer profile and context