TaskGPT.
The AI suite
that paid for itself.
End-to-end design of an enterprise AI productivity suite built on OpenAI, PaLM 2, and LLaMA, used by 35,000 operators daily across 14 countries. Call handling time dropped 20%. The product earned its own cost.
35,000 operators. Millions of customer queries. A playbook designed before AI existed.
TaskUs agents handle complex enterprise support across 14 countries: insurance claims, fintech queries, healthcare triage. The existing tooling was built for a pre-AI world: multiple tabs, manual copy-paste, static knowledge bases. Resolution time was high. Operator cognitive load was higher.
The brief was not "add AI to the product." It was redesign the entire operator workflow assuming AI is native to every step, from query classification to response generation to quality evaluation. A full paradigm shift, not a feature addition.
"Operators should think less about finding answers and more about delivering them."
The PCAF framework: a new language for AI-native design.
Before designing screens, I authored PCAF, the Persona-Centric Analysis Framework, to replace assumption-led research at TaskUs. Every AI interface decision traces back to an evidenced operator persona: how suggestions surface, how operators accept or override them, and how confidence is communicated without overwhelming attention.
Every screen in TaskGPT traces back to a PCAF principle. This ensured consistency across EvaluateUs, the LevelUp gamification system, and the core TaskGPT suite: three products that share an operator audience but serve very different moments in the workflow.
Journey mapping with 35K+ operators
Shadowed agents across 6 site locations. Mapped every tool switch, every manual lookup, every moment where the current workflow forced a wrong decision.
PCAF: persona-centric analysis
Built the Persona-Centric Analysis Framework so every AI interface decision traces to an evidenced operator persona, not an assumption. Now used across all TaskUs AI products.
Multi-model AI suite
Designed the full product across OpenAI, PaLM 2, and LLaMA, with each model serving a different operator task with appropriate confidence indicators.
LevelUp gamification
Designed the LevelUp engagement layer (challenges, leaderboards, streaks) that lifted operator engagement 40% and reduced attrition on high-volume queues.
Operators use the AI. Admins configure it.
TaskGPT is two products in one interface. Operators live in the assistant during live customer conversations. Behind them sits the admin plane: chatbots configured per client, usage and consumption dashboards, and data-dense feedback reporting that tells the team what the AI is actually doing in production. Designing both directions at once, configuration on one side and lived workflow on the other, is what made the suite hold together.
20% cost reduction.
35K daily users. One framework that changed how TaskUs builds AI products.
TaskGPT shipped across 14 countries and 35,000 daily operators. Call handling time fell 20% within the first two quarters, a reduction that translated directly to operational cost savings for enterprise clients.
The PCAF framework, originally built for TaskGPT, was adopted company-wide as the standard for AI interface decisions. EvaluateUs, the performance evaluation suite, and the LevelUp gamification layer both shipped under it. The design system outlived the project it was born from.