AI in Action

A large franchisor streamlined operations with Quepasa RAG, centralizing 5,000+ documents into a unified knowledge base. An AI bot provides quick, accurate answers, reducing query resolution time by 75% and improving content accuracy to 95%, enhancing overall service quality.

A fabric wholesaler optimized B2B support with Quepasa RAG, unifying product data and enabling an AI-powered query bot. Response times dropped by 80%, manual lookups were minimized, and sales conversions increased by ~15%.

A dynamic community optimized knowledge sharing with Quepasa RAG, integrating chats, transcripts, and profiles into a searchable system. Search time dropped by 80%, engagement increased by 25%, and redundant questions fell from 50% to 10%.

Visa and legal consultancies streamlined research with Quepasa RAG, a centralized AI-powered legal repository. It reduced research time by 60% and cut follow-up clarifications from 70% to 25%, improving efficiency and client satisfaction.

No-code/API platforms simplify the creation of custom search agents. RAG Platform API enables search customization without coding through an intuitive drag-and-drop interface. As a result, development time is reduced by 90%, and 80% of changes can be made by non-technical users, decreasing IT workload.

A Brazilian telecom company (8,000 employees, founded in 1990) faced high service delays and costs due to inefficient troubleshooting. A RAG-powered mobile app cut repeat site visits by 25 points and reduced customer downtime by 40%, enhancing service efficiency.

A Canadian insurance company (4,000 employees, founded in 1970) faced slow policy access and missed cross-selling opportunities. A RAG chatbot cut call-handling time by 50% and increased cross-selling success from 12% to 20%, driving efficiency and revenue growth.

A US private hospital network (2,000 employees, founded in 1995) faced inefficiencies due to scattered patient data. Implementing a RAG-powered EHR integration cut documentation time by 60%, improving workflow and patient care.

A UK e-commerce company (5,000 employees, founded in 2008) struggled with high support demand and slow response times. A RAG-based chatbot, integrated with its knowledge base, reduced response time by 83% and increased customer satisfaction from 70% to 92%.

A German industrial equipment manufacturer (3,500 employees, founded in 1980) faced slow technician training and production downtimes. A RAG-powered system cut onboarding time by 33% and reduced downtime by 20%, improving efficiency and cost savings.

A Singapore investment firm (1,200 employees, founded in 2002) faced slow risk assessments and missed market opportunities. A RAG-powered assistant cut reporting time by 80% and sped up trade execution by 30%, improving decision-making.

An Australian ed-tech startup (250 employees, founded in 2015) struggled with student engagement and slow feedback. A RAG-driven tutoring tool boosted retention by 25 points and improved test scores by 15%, enhancing learning outcomes.