Complex use cases in Service Management across sales, customer service, HR and IT interactions require flexible non-linear, multi-round conversations to deflect to a Virtual Assistant. Rulai’s MITIS™ (Mixed Initiatives and Tasks for Intelligent Services) technologies make it possible out-of-the-box.
Enterprises can utilize their existing knowledge such as manuals, websites, databases, and business processes to rapidly and accurately deploy Virtual Assistants on tailored use-cases thanks to Rulai’s AI Knowledge Module.
Often, company knowledge is fragmented inside of an organization. There may not even be consumable data for emerging business areas. Rulai’s AI enables cold-starts and improvements-as-we-go with an Ensemble Approach for unsupervised learning, transfer learning, active learning and deep learning technologies and proprietary data.
Rulai’s omni-channel engagement center, complemented by its domain and use-case specialized toolkits for optimal voice recognition and understanding, empower personalized customer journeys across every channel.
Rulai’s foundational conversational virtual assistant framework consists of a messaging connector, a natural language processing engine, and a dialog manager that takes user intent and outputs a response. Unlike other chatbots, Rulai’s dialog manager can robustly handle the complex and cumbersome conversations that are commonly encountered during the customer journey. This is the result of a highly-differentiated technological approach for the dialog manager’s back-end
Rulai’s architecture also differentiates itself by bringing in an Omni-channel Engagement Center and an AI Knowledge System with an expansive set of adapters. This architecture enables enterprises to rapidly consolidate and build off legacy systems to deploy at scale across customers and services while maintaining low overhead.
This powerful backend embeds seamlessly into existing websites using Rulai’s native Conversational UI with just a single-line snippet or integrates into apps, messaging, and call systems through API.
Our conversation design console allows your domain experts to:
“The platform offers strong dialogue management capabilities and allows business users to define dialogue workflows that can be deployed via drag-and-drop techniques, with no coding required. ”
-Gartner 2018 Cool Vendors in AI for Conversational Platforms
“Rulai differentiates by targeting a technical business audience. This doesn’t limit its power — it still supports complex, nonlinear conversations.”
– The Forrester New Wave™: Conversational Computing Platforms, 2018
People find it frustrating to engage with chatbots due to their rigid, manually coded decision trees for dialog management.
Context switching is needed to deliver a user-friendly experience. Rulai’s third-level dialog manager handles context switching with our proprietary Mixed Initiatives and Tasks Intelligence System (MITIS™) technology, enabling chatbots to follow more than a single thread out of box.
Rulai chatbot can also detect and respond to multiple intents, resulting in a more natural interactive experience.
| Level | Characteristics | Examples | Required Technologies |
|---|---|---|---|
| 1st | Single round Q&A | FAQ, question/answer chatbots | Question answering engine or search engine |
| 2nd | Simple linear/tree multi-round conversations | Change address, menu-driven chatbots | + Multi-round dialog manager |
| 3rd | Flexible non-linear conversation | Sales and marketing chatbots | + Advanced NLU + Advanced Dialog manager |
Rulai team members have published over 400 research papers and filed over 100 patents in AI. We published the first research on neural network (i.e. deep learning) for language models with dialog systems (1999 & 2000)
1st for several months Netflix Challenge (2006, 20k+ teams)
1st, DARPA Supervised Topic Detection and Tracking, 2004
1st, 2016 & 2017 ACM Multimedia Challenges
1st, 2015, ImageCLEF Challenge
