SAAS designed and developed on AWS aimed at HR TA team
Challenges Addressed
Key Features
Potential Benefits
Multiple formats of CV’s & JD’s
Low accuracy (<50%) in extraction, leading to lost opportunities and inefficiency in Talent Acquisition
Long build times (>6 mo) for new algorithms to be integrated into the processing engines
Ability to extract skills of importance from job descriptions
Ability to extract over 30 important candidate attributes from their CV’s
API’s in AWS to run as SaaS for bulk processing
LLM’s and ML power the core engine
Skills extracted from JD’s improved from 40% to >90%
Attributes of candidates extracted from CV’s with 92+% accuracy
Contextualized search over the repositories
Ability to automate matching using AI.
CASE STUDY 4
AI for voice insights (Contact Centre)
MVP designed and developed on AWS aimed at SMB CX heads
Challenges Addressed
Key Features
Potential Benefits
High cost of call centre operations due to the inability to bring in best practices
Wasted voice data due to very few realistic solutions leveraging AI and Data
High price of existing Voice solutions makes it out of the reach of SMB’s
Provide a curated Data Platform and Voice Intelligence as a Service (SaaS) to BPO/KPO’s.
Provide actionable insights from agents’ conversations with prospects and customers
Enhance the customer engagement for B2C companies and reduce cost of operations
Innovative drives down the cost of solution to about £200/mo (1/5th reduction over competition)
Ability to ingest historic voice data and provide insights in real time
Configurable dashboards for use by agents and operations leaders
Ability to create AI driven alerts to enhance CX
Data driven agent performance management
CASE STUDY 5
AI for stock trading (investment mgmt)
MVP designed and developed on AWS aimed at traders
Challenges Addressed
Key Features
Potential Benefits
Day traders often use multiple tools and databases to help with buying/selling stocks and commodities
There is little control on quality of data sets enabling trading and AI driven insights is out of reach
Feature overload of simple BI tools distracts traders
Support day traders with Data and AI driven price predictions
Leverage real time data from Exchanges
Enable traders to set alerts and rules
Buy/sell signals based on risk profiles
Trading cockpit driven by real time data
1.5-2x better returns over legacy methods for over 50 stocks in NASDAQ
Ability to ingest any data sets including open source
Custom visualization for traders to set their rules
Ability to Integrate with any trading platform
CASE STUDY 6
ENOPT – energy optimiser (energy)
MVP designed and developed for Battery Energy Storage Systems
Challenges Addressed
Key Features
Potential Benefits
Current planning systems publish static heuristic schedules
Do not take into account external data sets
Are not customized at the level of BESS
Create Control logic software and algorithms for BESS
Account for the economics of operation and Peak shaving
Consider data from site ops, energy market, weather forecasts and battery modelling.
BESS Charge/ Discharge schedule is the main output for Operations
Optimize the daily revenue from energy arbitrage
Solves hard optimization problem in near real time and informs the BESS and central controls
Potential to deploy globally
CASE STUDY 7
AI based digital twin (defense)
Conceptual model, platform design and development
Client
Challenges
Business Outcomes
A strategic public sector client responsible for security
Need to build diagnostic, predictive and prescriptive analytics to support asset maintenance and operations
Several home-grown systems support operations currently lying as islands of automation
Data capture not done in an integrated manner
Intelligence is provided in a delayed manner as there isn’t a provision for a data lake for operations
Data governance and data lake implementation for asset data enables real time intelligence and alerts and resulted in over 30% improvement in operational readiness
Deployed analytics and AI solutions to support predictive maintenance with over 80% enhancement in asset visibility
CASE STUDY 8
AI based video intelligence (defence)
AI model, solution design and development on cloud
Client
Challenges
Business Outcomes
A strategic public sector client responsible for security
There is a need to build a data and intelligence platform to support search, analysis and execution based on video and image data ingested.
Video and image data is received and ingested at very high volumes and archived
Intelligence operatives spend a lot of time discovering data
No tools exist to seamlessly support advanced indexing, analytics and real time intelligence
Improved overall productivity of intelligence operatives by over 60%.
Secure containeraised way of video and image data ingestion and analytics
Auto Indexed and searchable metadata catalogue
AI/ML based solutions and alerts for various key stakeholders
