Businesses use Tiyaro DeepQuery to create Large Language Model-based applications customized with data from internal systems of record
Tiyaro DeepQuery is an unsupervised LLM engine that rapidly learns from massive amounts of data sourced from the enterprise's chosen systems. It provides precise answers, analyses, and actions for internal and customer-facing business operations.
Broad functionality across three key dimensions: enterprise use cases, user populations, and data connectivity
Fastest time to accurate results due to automated data sourcing, data engineering, and learning
Explainable AI outcomes, with strict data privacy and user privilege safeguards, including private deployment option
Compatible with a wide variety of open and proprietary LLMs, including ChatGPT, GPT4, Bard, Cohere, AI21, and more
Complex field service procedures are required on equipment shipped and installed in more than 80 countries. Vague reports often prevent field technicians from fixing issues on their first visit. In addition, the diversity and complexity of equipment makes it hard for technicians to recall exact remediation procedure on-site.
An app built with DeepQuery narrows down customer-reported issues, even vaguely reported ones, by root-causing the issue based on past service records. It then comes up with an accurate remediation procedure based on formal product service manuals and informal service summaries.
Customer success offer for their small- and medium-sized customers is a portal with various assets. Education assets are seldom used because customers prefer just-in-time learning in the context of a specific problem. Basic search function doesn't answer the customer questions effectively and quickly. Community forum posts take time to be answered. These reduce customer efficiency and revenue expansion opportunities.
An app built with DeepQuery answers product questions quickly using content from a variety of sources, and provides automated answers to customer forum posts when such answers are possible.
IT helpdesk serving employees dispersed across the USA aims to reduce operational costs while dramatically lowering time to accurate problem resolution. Previous attempts with AI chatbots didn't deliver expected results while incurring inordinate amount of manual data labelling and training, AI expertise, time and costs.
An app built with DeepQuery recommends resolution steps for incoming IT tickets.
Product portfolio has dramatically expanded, making it harder for the company's 100,000+ developer ecosystem to keep up. Developer meetups and training modules didn't address the problem since developers can't retain and recall information between time of training and time of actual need.
An app built with DeepQuery answers product questions quickly using content from a variety of sources, and provides automated answers to developer forum posts when such answers are possible.
Real-time operations decision making at the store level is hampered and centralized because access to analytics requires special tools and training.
An app built with DeepQuery answers natural language operations questions using already-collected business data, while making sure that answers contain information at the employees' privilege level. It democratizes the ability to be curious about the business, increases employee engagement, and brings the best operations optimization ideas to the table faster.