English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The goal of serving and delighting customers in a personal and near humanlike manner is very high on automation agendas of most Enterprises. Last fewyears, have seen huge progress in Natural Language Processing domain which hasled to deployments of conversational agents in many enterprises. Most of thecurrent industrial deployments tend to use Monolithic Single Agent designs thatmodel the entire knowledge and skill of the Domain. While this approach is oneof the fastest to market, the monolithic design makes it very hard to scalebeyond a point. There are also challenges in seamlessly leveraging many toolsoffered by sub fields of Natural Language Processing and Information Retrievalin a single solution. The sub fields that can be leveraged to provide relevantinformation are, Question and Answer system, Abstractive Summarization,Semantic Search, Knowledge Graph etc. Current deployments also tend to be verydependent on the underlying Conversational AI platform (open source orcommercial) , which is a challenge as this is a fast evolving space and no oneplatform can be considered future proof even in medium term of 3-4 years.Lately,there is also work done to build multi agent solutions that tend toleverage a concept of master agent. While this has shown promise, this approachstill makes the master agent in itself difficult to scale. To address thesechallenges, we introduce LPar, a distributed multi agent platform for largescale industrial deployment of polyglot, diverse and inter-operable agents. Theasynchronous design of LPar supports dynamically expandable domain. We alsointroduce multiple strategies available in the LPar system to elect the mostsuitable agent to service a customer query.

LPar -- A Distributed Multi Agent platform for building Polyglot, Omni Channel and Industrial grade Natural Language Interfaces