Artificial Intelligence & Machine Learning
Drawing on extensive expertise across various AI technologies, including deep learning, machine learning, computer vision, reinforcement learning, and natural language processing, we develop custom, domain-specific AI models, AI Agents and provide model fine tuning solutions.
Foundational AI Model Development
Foundation models are large-scale machine learning models trained on vast amounts of diverse, unlabeled data using self-supervised learning techniques. These models serve as a versatile base for developing various AI applications across multiple domains.
We help you in your foundational model development during any of these steps/stages :
Data Gathering and Preprocessing: Collecting and cleaning vast amounts of diverse data from various sources
Architecture Design: Selecting an appropriate model architecture, often based on transformers
Model Initialization: Setting up the initial parameters of the model
Pre-training: Training the model on broad datasets using self-supervised learning techniques
Fine-tuning: Adapting the pre-trained model for specific downstream tasks
Digital Workers : AI Agents Development
AI agents are sophisticated software programs designed to automate and optimize specific tasks within enterprise processes. These intelligent systems are transforming the way organizations operate, offering unprecedented levels of efficiency and productivity. As AI technology continues to advance, we can expect AI agents to become even more sophisticated, taking on increasingly complex tasks and decision-making roles within enterprises. This evolution will likely lead to:
More natural language processing capabilities for seamless human-AI interaction
Enhanced predictive analytics for proactive problem-solving
Greater integration with IoT devices for real-time data processing and response
Collaborative AI systems that can work together on complex, multi-step processes
By strategically deploying AI agents, organizations can create a more agile, efficient, and competitive business environment, positioning themselves at the forefront of the ongoing digital transformation in the enterprise world.
Our AI deployment services seamlessly integrate cutting-edge solutions into your business operations, facilitating a smooth transition from development to real-world application. We expertly manage and optimize complex interactions between AI models, data, and infrastructure on platforms such as AWS, Azure, and Vertex AI, ensuring precise and efficient development and deployment. Our team oversees every aspect of deployment, from configuration and integration to monitoring and maintenance, guaranteeing optimal performance.
To enhance the effectiveness of AI deployment, we focus on several key areas:
Infrastructure optimization: We set up and configure robust infrastructure tailored to your AI solution, whether on-premises or cloud-based, ensuring performance, scalability, and security1.
Cloud platform integration: We excel in integrating AI solutions with leading cloud platforms, enabling leveraging of cloud power and scalability1.
Continuous Integration & Delivery (CI/CD): We implement CI/CD pipelines to automate the deployment process, enhancing development efficiency and reducing errors1.
Model packaging and optimization: We prepare AI models for deployment in efficient and compatible formats, fine-tuning performance for real-world applications1.
API development: We create robust APIs to facilitate communication between AI solutions and other systems, ensuring seamless interaction1.
Testing and validation: We conduct rigorous testing to validate accuracy and reliability, identifying and resolving potential issues before deployment1.
By addressing these critical aspects, we ensure that your AI deployment is not only successful but also delivers tangible business value.
AI Deployment Services
Our Engagements - Types of Development Partnerships, Use Cases & Case Studies
As an AI Development company specializing in AI, we have established a strong track record of successful engagements across various domains.
Development Partnerships Types
As the extended development arm for AI companies, we provide specialized expertise and resources that enable our partners to accelerate their AI product development. Our partnership model includes:
Dedicated engineering teams that seamlessly integrate with our partners' development processes
Specialized AI expertise in model fine-tuning, optimization, and deployment
Infrastructure development and management for AI model training and inference
Custom tooling development for data processing, annotation, and quality assurance
Agile development practices that adapt to evolving product requirements
We handle the complex technical implementation while our partners focus on their core product vision, creating a symbiotic relationship that speeds time-to-market and enhances product quality. Our white-label development approach ensures our partners maintain their brand identity while leveraging our technical capabilities.
Use Cases Types
AI Foundation models have a wide range of applications across various domains, leveraging their capabilities in natural language processing, computer vision, and other AI tasks. The main applications of AI foundation models today include:
Natural Language Processing
Computer Vision
Generative AI
Research and Discovery
Summarizing research papers and identifying trends2
Accelerating developments in manufacturing and biotechnology2
Healthcare
Drug discovery and personalized treatment recommendations2
Analyzing patient records and genetic data2
Industry-Specific Applications
Legal research and document analysis1
Logistics and supply chain optimization1
Robotics and automation in manufacturing6
These applications demonstrate the versatility and adaptability of foundation models across various fields, enabling advancements in AI-powered solutions and services.
Natural Language Processing
Case Study: Healthcare
A Healthcare company implemented AI, a foundation model-based solution, to improve cancer care coordination. The AI system analyzes pathology reports and medical records to identify cancer cases and streamline the diagnosis-to-treatment process. As a result:
Time from diagnosis to first treatment decreased by 6 days
Over 11,000 hours saved from manual review of pathology reports
Care teams spent 65% more time on patient care coordination
Case Study: Children's Hospital
A US Children Hospital developed several AI applications using foundation models:
POPP (Prediction of Patient Placement): A predictive model forecasting incoming admissions from the Emergency Department with over 90% accuracy.
Infectious Disease Monitoring: Combining machine learning, expert forecasts, and spatiotemporal models to predict infection hospitalization.
Fine-Tuned Language Models: Customized large language models to help nursing staff access and understand clinical policies and protocols in real-time.
Research and Discovery
Case Study: A Large Automotive Group
AI Experts at large Automotive Group, is spearheading AI initiatives for innovation and pre-development in the automotive industry. Their work focuses on:
Integrating AI solutions to enhance automotive technology
Improving user experiences through AI-powered systems
Leveraging foundation models for generative AI applications in autonomous operations and vehicle design
These case studies demonstrate the versatility and impact of foundation models across various industries, showcasing their potential to drive innovation and efficiency in real-world applications.
Case Studies Types
