Spire has designed & created unique ethical and unbiased artificial intelligence platform
Powerful Features of the Spire.AI Domain Intelligent Super-Platform
Multi-Lingual Multi-StructuredThe structure of data in demand and Supply documents vary across organisations from data being highly structured to highly unstructured to a mix of both. Today's platforms need to comprehend unstructured data as efficiently as structured and need to provision for analysis and configuring use of such data in the right mix so as to deliver desired outcomes with as much accuracy as possible.
The SpiroBot® platform can comprehend unstructured data in more than 104 languages and process multi-structured demand & supply documents through automatic language detection, translation and contextualization using our domain intelligent SkillSpace™ that hosts world's largest graph based skill cloud framework
The system creates weighted context of demand and supply data based on factors like recency, frequency, proficiency and inter-relatedness of the skills at different weighted levels which are then used for further processing and quantification in the applications
The accuracy of extraction of skills automatically from unstructured demand & supply data is the ultimate core for any successful AI application. Such extraction must be domain intelligent, i.e. the extracted skills must be meaningfully connected as well as weighted to replicate the context of the role represented in the demand data and the profile represented in the supply data.
The Spire.AI platform operates highly configurable algorithms that identify and weigh primary, secondary and tertiary skills across roles. These weights are generated in real-time for each demand document being processed and the appropriate weight distribution algorithm is set based on the initial benchmarking exercise for every client and is recalibrated on periodic basis.
Most of the tools are still stuck with age-old boolean search offerings.
What the users really need today is for their search algorithms to interpret the content by building 'context' around their searched words. In today's fast paced innovative business scenario, the context of skills is rapidly growing and even changing in many cases and therefore the system must be able to 'read the context in the mind' of the user.
Spire.AI offers game-changing contextual search capabilities embedded in its applications, which are further enhanced by unique offerings like Factorial Search wherein multiple search parameters are automatically factored and the system triggers multiple contextual search combinations simultaneously, using these factors, to deliver desired results in the form of various combinations the user wanted to try, as if the system 'read the mind of the user'.
This is year 2021. One of the simple and innocent ask of the users is - "Is there no system intelligent enough, in 2021, to suggest automatically what's available in the supply vis-à-vis what's being demanded? Is there still really a need for us to execute a search - can the system not trigger an alert if it now has the availability for something I was looking for?"
The game-changing research and invention of Spire in the space of enterprise search brought a new gift to life for the next generation users - the Auto-Inventoried Search Analytics. The search engine automatically inventorises the entire demand & supply data based on the domain skill context of our clients thereby prompting users to playfully navigate and analyse millions of profiles in the available supply and zero-in to the select few that match the demand context in their minds.
In today's world, there is a constant dichotomy for selection decisions based on quality versus quantity. Whether selecting talent for niche roles or high volume generic roles, quality of talent selection is the paramount focus of any talent organisation – existing talent systems are limited in handling this dichotomy.
Spire AlgoRator™ is a unique ‘multi-stage fishing net’ concept based matching and ranking engine that has demolished for ever the constraint of dealing with such dichotomy and ensures high quality talent selection for niche roles as well as helps talent organisations achieve desired balance between quality & quantity for high volume generic roles. It allows talent organisations to adapt at run-time to the dynamic needs of various business functions based on their growth, stabilization or optimization focused strategies.
An important need for the users today is the ethical identification of the richness and relevance of the profile vis-à-vis demands. Talent solutions that use semantic NLP and machine learning based AI blatantly violate the ethical standards of processing the largely unstructured talent demand and supply data.
The Spire Richness Index™ is a bi-directional measure of relevance between demand & supply. This score represents the depth and richness of the ‘domain content’ in a profile vis-à-vis the ‘context of domain requirement’ in the job or role descriptions. Instead of using historical data-driven semantic NLP or machine learning based calculation approaches, Spire uses artificial domain intelligence based calculations for breadth of domain coverage, presence, frequency & recency of skill experience and skill relationship index to derive Richness Index.
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Imagine the complexity of global organisations in processing, while their demands are represented in English or some times in the language generally spoken at their global headquarters, the global supply content might be available in completely different languages.
SpiroBot™ Domain AI is language agnostic and supports cross-language contextual search and match processing across 104 global languages. Our Domain AI is independent of the limitations of semantic NLP and therefore can process a demand in English with supply in French, or a demand in French with supply in German, Arabic or Japanese, etc.
Talent organisations are stuck with straight line processing of limited supply pool due to archaic search & match technologies of their systems, whether legacy or modern, leading to high quality talent pool being lost in oblivion as they quickly become dormant in these systems
Spire iSourcing™ & Mining are unique search & match algorithmic approaches that categorises supply data into multiple active supply streams and cross-pollinates all existing employees, mobility eligible employees, live job applicants or dormant candidate profiles with all demands using a rules based processing logic based on specific talent operations use cases of our clients.
This ensures instant CCPA, OFCCP, GDPR & EEOC compliant visibility of qualified profiles which were otherwise unidentified by current systems and stuck in process-pipeline or become dormant in dead databases