Artificial Intelligence / Machine Learning

Modern AI technologies are the result of an unprecedented convergence of algorithms, computing, data availability, connectivity, and large-scale collaboration. AI/ML has already established itself as an essential building block for modern products and technology platforms.

Our approach is rooted in the domain of computing and design-focused thinking. While we have built AI algorithms, solutions, and models, we also believe AI DevOps and related tool development are equally important for modern engineering programs.

From the formulation of data analytics problems based on business needs to the deployment and monitoring of AI-based solutions, we have worked across the AI field and have:

Built AI models from scratch defining custom model architectures
Enabled AI research by crafting domain-specific tools for data curation
Created specialized development tools for highly efficient workflows
Created multiple cloud-based AI analytics platforms leveraging various AI backends

Our Approach

01

Tracking the rapidly evolving medical device and healthcare landscape and strive to leverage technology across multiple domains

02

Striving for radical innovation, help our customers create options by transforming ideas into functional prototypes

03

Enable medical device innovators to create affordable medical devices through frugal engineering to specifically address the needs of emerging markets

04

Build relevant domain expertise complimented with wide technology experience within our team to develop integrated solutions to our customer

Our Offerings

Customer Speak

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AI DevOps

Enable R&D with cloud-based AI DevOps systems
Model training session configuration, tracking, and re-use
Trained model management, Custom tools – data pre-processing, labelling.
Integrated workflows for data sources, compute, GPU infrastructure, development env, code management
AI solution Hosting on AI Marketplaces

Connected Platforms for Cloud based AI

Cloud platform for data aggregation and analysis from multiple devices in field
Predictive analytics of critical product components
Devices and systems utilization related Insights for operations optimization
Proactive service operations planning and optimization
NLP based training data generation

Deep Learning

AI based segmentation, Object detection and classification
Sensor data analytics using ML AI based Product Validation
Edge based AI model deployment on embedded processors
Transfer learning of AI models

Infrastructure Analytics

Support Diagnostic or Root cause Analytics for infrastructure
Asset Health Analytics involving trend analysis, Live status, performance, health, utlization
Topology visualization
Infrastructure assessment analytics
Cloud data analytics integration
Big Data analytics

Virtual Assistants

Development of chatbots providing Digital Customer Experience
NLP Engine for identifying intents
Defining dialog flows
BDD framework for automation of chatbots
NLP Engine integrations for virtual assistants
Integration with ML-Based Cloud document search services

DL, ML Based Video Analytics

Object, humans, and context-based scene detection
ML, DL algorithms for specific indoor and outdoor surveillance, monitoring needs
AI based automation using different sensors like depth camera, color, monochrome camera
Aviation ground operations efficiency estimation with video analytics

GenAI Based Test Automation

Multi-agent architecture
Targets any web application
Supports building of test framework using LLMs
Supports test case automation at complex abstractions
Cost optimized to not require continued usage of LLM APIs

AI Based Log Analysis

Rule engine based fault detection
Automated Fingerprint analysis of fault conditions
Anomaly detection algorithms based on transformers

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Case Studies

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