Deep-domain AI

To fully realize the potential of AI for enterprises, solutions should embrace the domain. When combined with domain ontologies, processes & architectures, AI revolutionizes domains such as Life Sciences, Financial Services and Industrials.

We focused on the following domains and mapped core business problems in each domain to the latest research and state of art in AI. The solutions and accelerators which emerged are listed below and can be further customized to the specific needs of your enterprise.

We address opportunities across the value chain from drug discovery to manufacturing:

  • Shorten discovery cycle times from years to months using in silico methods such as de-novo drug design, virtual screening, ADME-Tox predictions, and neo-epitope calling

  • Reduce material and energy costs through synthesis planning and retro-synthesis analyses, yield prediction models, simulations on digital twins and real-time process optimization

  • Pre-fill regulatory submissions such as Product Safety Update Report (PSUR) and Chemistry and Manufacturing Controls (CMC) from data and documents available in your data and document stores

  • Identify high-risk data points through cross-trial analysis and cross-site analysis of data to facilitate more targeted site visits by Clinical Research Associates (CRA)

We apply AI to address core concerns common to all segments of financial services:

  • Automate KYC tasks such as ID verification, document and form extraction, validation & processing

  • Measure and monitor opportunities, business and regulatory risks in agent-customer conversations while handling huge call volumes

  • Leverage Robotic Process Automation (RPA) for efficient routing of service requests, smart pre-filling of regulatory submissions

  • Reduce false positives and accurately detect genuine fraud cases using latest advances in anomaly detection

We apply AI advances to model complex mining processes & accelerate exploration studies:

  • Minimize risk, cost and time involved in exploration, using AI based techniques for geological data collection, curation, modeling and lead generation for minerals

  • Build AI models that support experts to prioritize targets, lower costs per targets explored, scale exploration to larger areas

  • Create digital twins of mining processes for better estimates of throughput, recovery and prediction of unscheduled downtimes

  • Leverage AI based utility solutions for knowledge digitization and quick information retrieval from legacy data

AI Expertise

Recent advances in AI such as low-shot learning, explainable AI, knowledge graphs and multi-modal AI have addressed challenges that were posing a bottleneck for adoption of AI in enterprises.

We apply such latest technological AI advances, which are already deployed in the consumer space, to address specific and context driven needs for the enterprises areas. In addition to working with usual technologies from industry leaders such as Google, Amazon and Facebook, we also implement several recent advances in research, in each of the following AI domains.

  • Combine signals from fingerprint and iris scans for enhanced security

  • Combine inputs from satellite images and assay data to identify leads for mineral extraction

  • Combine inputs from email, chat, social media, call centre to gauge customer sentiment in real-time

  • Improve productivity and automate repetitive tasks such as Regulatory filing, Talent Management, Contract review and analysis using latest NLP techniques that consider the context of a word and sentence.

  • Build efficient information retrieval systems by lowering the barriers to automated data extraction from legacy enterprise documents such as land surveys, experiments, engineering drawings, satellite images and maps etc.

  • Gauge customer sentiment in real-time using a multi-modal approach that considers inputs from multiple channels such as email, chat, social media etc.

  • Codify knowledge and subject matter expertise into knowledge graphs that embed domain rich context

  • Convert enterprise data into formats that applications can leverage to accomplish complex tasks such as automated timeline generation

  • Uncover hidden patterns and automate construction of ontologies from multiple sources

  • Simplify user experience through automated liveness detection and iris scanning

  • Analyze satellite images to extract relevant geological features for a machine learning model

  • Enable faster claims processing while minimizing fraud by comparing images of damaged vs. registered assets

  • Apply audio and speech processing to manage regulatory and quality risks without escalated costs

  • Optimize effort in call centres by adapting AI techniques to handle specific industry and enterprise interactions

  • Derive insights for Business Process Reengineering (BPR) by analyzing customer-agent conversations

Enterprise AI

Managing exploding public data in conjunction with growing enterprise data and transforming it into formats consumable by AI models with agility is a challenge.

Aganitha’s AI Data Manager provides a robust data pipelining solution to enable seamless AI adoption by enterprises.
Building an infrastructure that provides flexibility to absorb the changing mix of processes, automation and human interactions is essential for enterprises to navigate the continuously evolving AI technology.

Aganitha’s AI Solution workbench provides a comprehensive set of building blocks to help enterprises build such a flexible infrastructure.