Nyckel and Contextual join forces to make building an AI workflow even easier
Contextual, an AI orchestration platform, and Nyckel, a leader in classification APIs, have joined forces to make building a fully-automated AI workflow even easier. Customers of Contextual can now use Nyckel’s platform to categorize content and then use that information in subsequent business decisions (see the case study here).
Use cases include:
Lead Prioritization and Routing
Imagine a sales team struggling to manage a high number of leads, making it important to identify which ones are actually worth pursuing. With Contextual + Nyckel, these teams can build win/loss models using historical sales data, then automatically tag incoming leads as, say, “Low-Value,” “Mid-Value,” and “High-Value.” This allows the sales team to identify and prioritize the right leads in just seconds.
Support Ticket Processing
Customer support teams often face challenges in processing a large volume of support tickets. Using Contextual + Nyckel, support teams can build models for easily categorizing their tickets. Example applications include (1) using image classification to tag screenshots as mobile or desktop and automatically adding that to the ticket, (2) categorizing tickets as high-priority or not based on the issue, and (3) analyzing user sentiment across different products.
Service Work Order Classification
In industries where timely service is critical, the ability to classify the urgency of work orders can significantly improve customer satisfaction. With Contextual + Nyckel, service teams can easily analyze both the language used in problem descriptions and any uploaded images, allowing them to immediately know what’s critical to address.
These are just a few real-world applications. Nyckel’s platform is input-agnostic, allowing Contextual customers to build auto-tagging models around any data they want, including product images, text reviews, aerial photos, headshots, email content, and much more.
Interested in a group demo? You can schedule one here.