How TaskUs Reduces Handle Time 20% with Pinecone-Powered TaskGPT

TaskUs has been working in AI for over a decade, providing outsourced customer experience services to some of the world’s most demanding technology brands. As generative AI began reshaping what was possible in customer support, TaskUs built TaskGPT: a modular GenAI platform that allows clients to deploy AI-powered support tools customized to their specific knowledge bases, data, and compliance requirements.

The original approach stored embeddings in flat files and ran cosine similarity searches using Python. It worked for early-stage use cases but broke down as demand grew. As more clients onboarded and TaskGPT’s capabilities expanded—covering fintech, healthcare, and consumer tech—the need for a purpose-built vector database became clear. Latency, scalability, and multi-tenant data isolation were all at risk with the flat-file approach.

TaskUs selected Pinecone as the vector database foundation for TaskGPT’s two core products: AssistAI and Prompto. AssistAI is a RAG-powered knowledge assistant trained on each client’s documentation and historical interactions—built using a proprietary ingestion module called ChatBoTify, which chunks client knowledge bases and stores embeddings in Pinecone alongside metadata. Prompto uses Pinecone for intent-matching and recommendation, surfacing relevant responses to support agents in real time. Pinecone namespaces keep each client’s data isolated. LLM inference runs via Amazon Bedrock with multiple foundation model options tailored to client needs.

Across client deployments, TaskGPT has produced a 20% reduction in average handle time and a 5% improvement in customer satisfaction scores. Agents resolve issues faster because AssistAI surfaces the right answer in seconds rather than requiring manual knowledge base navigation.

TaskUs’s architecture shows how BPO companies are evolving into AI platform providers. By building TaskGPT on Pinecone, they offer clients a production-grade GenAI system without the infrastructure burden—turning their decade of CX expertise into a data asset that continuously improves retrieval quality.