About Spire

Spire is an unique domain intelligence technology based AI platform that generates contextual meaning out of unstructured text. The platform, that has evolved over 12 years, has been deployed with game changing outcomes in the space of Talent and is now creating waves in other areas like underwriting, fraud prevention, legal, sentiment, etc.

©2007-2019 Spire Innovations, Inc.

Spire AI – The Artificial Domain Intelligence Platform

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RESPONSIBLE AI
FOR TALENT TRANSFORMATION

Spire has designed & created unique
ethical and unbiased artificial intelligence engine to power the world of talent

TOTAL TALENT TRANSFORMATION SOLUTIONS

The Spire TalentSHIP® solution suite is powered by Domain AI & spans across Total Talent landscape

Talent Supply Chain
Solutions

 

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Talent Acquisition

  • Sourcing Scale Up & Demand-Supply Cross-pollination
  • Automated Talent Pool Creation & Sourcing Assistant
  • Robotic Global Job Aggregator & Unified Portal

Talent Fitment & Deployment

  • Total Integrated Talent Supply Chain Management
  • Fitment Diagnostics, Internal First & Mobility

Talent On-demand

  • GigForce™ Flexible Qualified Talent VPN
  • Subcon & Vendor SLA Management System

Upskilling & Reskilling

  • Skill Gap Identification & Gap Aggregator
  • Demand-based Skilling Recommendation Engine
  • Preceding & Succeeding Skill Path Analyzer

Skill Framework

  • Self-evolving Live Skill Framework
  • Role Composition and Career Architecture

Strategic Talent Analytics

  • Integrated Talent SCM Build/Buy Intelligence
  • Market Intelligence for Role & Skill Evolution
  • Demand-based Competitor Intelligence

Growth & Strategy
Solutions

spirobot

The Spire TalentSHIP® Platform

Powered by Ethical & Unbiased Domain AI Engine for Talent

Most AI focused talent systems use semantic NLP and Machine Learning technologies that learn from bias-prone data generated from historical talent operations & decisions and are therefore ethically irresponsible

Spire’s unique Domain Intelligence based AI approach is built over a decade with iterative research and strong Responsible AI principles that are ethics focused and use unbiased technology application frameworks that are particularly important to process unstructured data related to Talent

Spire AlgoRator™ is a unique ‘multi-stage fishing net’ concept based matching and ranking engine that has demolished for ever the constraint of dealing simultaneously with niche vs. generic demands and high vs. low quantity or quality of supply to adapt at run-time to needs of dynamic business context

SpiroBot™ Domain AI is language agnostic and supports cross-language contextual search and match across 104 global languages (because of non-dependence on semantic NLP)

Spire’s multi-system integration and multi-stream data unification capabilities are powered with strong enterprise service bus interface & data mediation layer that enables API or SFTP based integration models

Spire solutions are deployed in a virtual private cloud based environment and are hosted on AWS or Google cloud infrastructure with ability to host and process data based on location requirements governed by GDPR or major country specific data regulations

Spire data management, processing and deployment systems are built with privacy regulations & EEOC compliance at the core of its design with auditability built at various stages of data handling & processing

SPIRE DOMAIN AI ENGINE CAPABILITIES

104 Global Languages
600 Billion Domain Associations
5 Billion Matches Per Day

SUCCESSFUL TALENT ORGANISATIONS USE SPIRE AI

TO OVERCOME TECHNOLOGY LIMITATIONS OF EXISTING TALENT SYSTEMS

Limitation 1: Identifying Richness & Relevance of a Profile vis-à-vis Demand Ethically

Talent demand (jobs, roles) and supply profiles are largely unstructured by design and hence cannot be interpreted by traditional talent solutions as well as semantic NLP and machine learning based AI

Spire Solution: Spire Richness Index™

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 & ML based calculation approaches, Spire uses domain graph based calculations for breadth of domain coverage, presence, frequency & recency of skill experience and skill relationship index to derive Richness Index.

Limitation 2: Quality of Job Descriptions & Employee/Candidate Supply Data

A typical core concern of talent organisations is the availability of good quality job or role descriptions as well as sufficient and/or recent data in employee & candidate profiles

Spire Solution: Data Quality based Configuration of Search & Match Algorithms

The one-of-its-kind search & match algorithm configuration capability of Spire solves for disparity in the quality of demand & supply data across business functions and multiple talent systems in an organisation. Spire appreciates clients’ global data quality landscape contextually tied to their business processes and provides for tuning of multiple config parameters ensuring best outcome for each process of the client.

Limitation 3: A Constant Dichotomy for Selection based on Quality vs. 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 Solution: Spire AlgoRator™

Spire AlgoRator™ is a unique ‘multi-stage fishing net’ concept based matching and ranking engine that 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.

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GAME-CHANGING SPIRE SOLUTION CONSTRUCTS

TO OVERCOME OPERATIONAL LIMITATIONS OF EXISTING TALENT SYSTEMS

Limitation 1: High Quality Talent Lost In Oblivion

Talent organisations are stuck to straight line processing of limited supply pool due to archaic search & match technologies of existing systems, leading to high quality talent pool being lost in oblivion in their dead databases

Spire Solution: Cross-pollination with iSourcing™ & Mining

Spire iSourcing™ & Mining are unique search & match algorithmic approaches that categorises supply data into multiple active supply streams and cross-pollinates all live candidates, dormant candidates or existing employees with all demands using a rules based processing logic based on specific use case of talent operations. This ensures instant OFCCP, GDPR & EEOC compliant visibility of highly qualified profiles stuck in process-pipeline or dead databases.

Limitation 2: Unavailability of Reverse Map of All Demands Against Each Profile

All existing talent systems are demand/requisition centred and hence do not provide visibility of multiplicity of profile fitment leading to loss of high skill-worth flexible talent pool during talent identification exercises

Spire Solution: Bi-directional Demand-Supply Mapping

Spire match algorithms are designed to provide bi-directional mapping of demand and supply as a base outcome. This unique approach enables talent organisations to always be aware of the full job-match potential of each profile and provides them with an opportunity to discuss multiple demand-fitment options with each candidate / employee as well as with hiring managers thereby ensuring maximum effectiveness of Talent in their new roles.

Limitation 3: No Visibility for Skill Matrix, Capability, Capacity & Gaps Driven Planning

Due to the inability of skill fitment & gap driven processing of demands and supply in existing talent systems, talent organisations are limited in designing or experimenting with multiple talent management & deployment scenarios

Spire Solution: Skill Factorial™ & Gap Analysis

Skill Factorial™ is the Spire invention for enabling talent managers with unprecedented skill matrix based visibility of talent inventory, capability mix, capacity clusters and above all the skill gaps in an organisation. This model provides for analysis of talent clusters by geography, business units, experience level, employment type, etc. with individualised gap identification as well as open demand based or transformation strategy focused gap aggregation.