Aws AI and Machine learning Service Provider

Generative AI Solutions that Generate Meaningful Results

Shaped by generative AI engagements with current customers, our services are purpose-built to help organizations embrace generative AI with confidence.

Strategy and Planning

We work with you to ideate and prioritize Generative AI use cases, and develop detailed roadmaps to maximize time-to-value while mitigating risk.

New Product Development

By leveraging NLP models to extract relevant information from multiple data sources, we help customers accelerate the development and deployment of customized Q&A systems, AI chatbots, content generators, recommendation systems, and related tools.

LLM Ops

With deep experience across the generative AI lifecycle, savvy organizations rely on NorthBay for LLM Operations including deploying, fine-tuning, managing, and optimizing large language models.

AI Insights Enablement

Using state-of-the-art algorithms tailored to your proprietary data sets, we enable you to unlock the potential of your data.

Manufacturing and Logistics

Through model compression, hardware acceleration, and related techniques, we continuously improve the speed, adaptability, scalability, and efficiency of generative AI models.

Strategy and Planning

We work with you to Ideate and prioritize Generative AI use cases, and develop detailed roadmaps to maximize time-to-value while mitigating risk.

AI Insights Enablement

Using state-of-the-art algorithms tailored to your proprietary data sets, we enable you to unlock the potential of your data.

New Product Development

As companies move from exploring and experimenting with machine learning to deploying their applications at-scale, AWS offers the ideal combination of performance and low-cost infrastructure services across the entire application development lifecycle.

Model Performance Optimization

Through model compression, hardware acceleration, and related techniques, we continuously improve the speed, adaptability, scalability, and efficiency of generative AI models.

LLM Ops

With deep experience across the generative AI lifecycle, savvy organizations rely on NorthBay for lifecycle LLM Operations including deploying, fine-tuning, managing, and optimizing large language models.

Solution Brief

How to Solve the Data Quality Conundrum

Download PDF

NorthBay Generative AI Success Stories

Experience enabling a variety of AI/ML use cases in multiple industries, including:

Optimizing Talent Acquisition

NorthBay, along with AWS ProServe, is currently working with a government entity in a Middle Eastern country to match candidate profiles with a database of job descriptions based on existing skills, propensity to learn new skills, and emotional intelligence. By helping them leverage AI toolsets to parse unstructured data from resumes/CVs and job descriptions into structured data and optimize the matching, the country will be able to optimize leadership decision-making while providing greater economic opportunities for its citizens.

Enhancing New Product Development

NorthBay is helping a large financial services organization be first to market with new product features by enabling them to create a minimum viable product (MVP) in 60 days. We are integrating the LLM and toolsets and semantic search to parse key data points from millions of documents, including summarization and contextual-dependent word extraction from multiple sources.

Enabling Proactive Fleet Troubleshooting

NorthBay is helping a heavy construction equipment asset management organization identify ways to leverage AI to mine information from a large number of unstructured machine manuals in order to summarize and recommend proactive maintenance, troubleshooting, and more.

Content Moderation

NorthBay is working with a Middle Eastern government agency that would like to enhance the country’s content moderation platform. By analyzing unstructured text published from foreign media/sources, images, and video content, the country leaders can better moderate content available to their constituents using Rekognition moderation.

Additional AI/ML Solutions on AWS

NorthBay helps organizations understand where, when, and how to leverage the power of AI/ML to enhance data-driven decision-making, increase productivity, and solve old problems in new ways.

We work side-by-side with our customers to ensure success at every stage of the AI/ML adoption journey by:

Accelerating deployment and modernizing the underlying data layer
Removing risks associated with achieving intended outcomes…on time
Gaining access to more data sources and providing flexibility to future data sets
Providing access to and enabling more personas in the stakeholder community, while increasing overall security
Supplementing and complementing analytics, data science, and AI/ML expertise
Solving today’s challenges with a budget-friendly cost model that can scale for the future

Why Choose NorthBay for AI/ML Solutions on AWS

Experience enabling a variety of AI/ML use cases in multiple industries, including:

Financial Services

Customer segmentation and product recommendation optimization
Customer lifetime value prediction
Investor digital behavior analysis and investment product research and development insights

Education

At-risk student dropout identification and intervention
Learning intervention
Learning pattern identification

Insurance

Claims automation
Forecasting and premium pricing optimization

Healthcare

IoT device data correlation and diagnosis classification
Research institutions rapid experimentation and model optimization
Staff and equipment resource forecasting and allocation

Manufacturing and Logistics

Equipment proactive maintenance, downtime avoidance, and OEE optimization

Retail

Consumer price optimization
Optimized product recommendations

Financial Services

Customer segmentation and product recommendation optimization
Customer lifetime value prediction
Investor digital behavior analysis and investment product research and development insights

Education

At-risk student dropout identification and intervention
Learning intervention
Learning pattern identification

Insurance

Claims automation
Forecasting and premium pricing optimization

Healthcare

IoT device data correlation and diagnosis classification
Research institutions rapid experimentation and model optimization
Staff and equipment resource forecasting and allocation

Manufacturing and Logistics

Equipment proactive maintenance, downtime avoidance, and OEE optimization

Retail

Consumer price optimization
Optimized product recommendations

The Power of AI/ML at Work

Experience enabling a variety of AI/ML use cases in multiple industries, including:

Injury Prevention for Athletes

Athletes at a major university will wear IoT devices, which collect and track body movements. Detailed metrics, video and other interactive tools, all connected to the cloud, will produce massive amounts of data. NorthBay will help data scientists to develop and train models that analyze and mine the data for insights that physicians and other leaders will use to predict and prevent injuries.

Expertise across AI/ML-enabling technologies

Many organizations want to immediately experiment with new models and test new ideas using their data, but are held back by the rigidity of their existing data warehouse. By leveraging NorthBay solutions such as data lake and analytics, customers can spin up an agile, scalable environment that enables experimentation and innovation with modern analytics tools.

Diagnosis Accuracy for Patients

An early-stage health tech company was using IoT sensors to collect data from patients with implanted cardiac pacemakers. The data was then transmitted to the cloud and used to diagnose health conditions such as hypertension, heart arrhythmia, atrial fibrillation, heart attacks, etc. However, the company’s existing models only delivered 50% accuracy in the analysis and classification of the data. By partnering with NorthBay to develop a new data lake that enabled them to more broadly experiment with ML and model training, they increased the accuracy of their IoT data to 95%.

Partnership across the complete AI/ML Lifecycle

NorthBay plans, builds, deploys, and even manages AWS AI/ML-based use cases for many customers. We also leverage tools such as Amazon SageMaker to automate and standardize MLOps practices, which applies DevOps practices to ML workloads in order to streamline model delivery across the machine learning development lifecycle. Because MLOps provides common machine learning algorithms that are optimized to run efficiently against extremely large data in a distributed environment, it enables use of AI/ML for true business transformation.

Injury Prevention for Athletes

Athletes at a major university wear IoT devices, which collect and track body movements. Detailed metrics, video and other interactive tools, all connected to the cloud, will produce massive amounts of data. NorthBay will develop and train models that analyze and mine the data for insights that physicians and other leaders will use to predict and prevent injuries.

Diagnosis Accuracy for Patients

An early-stage health tech company was using IoT sensors to collect data from patients with implanted cardiac pacemakers. The data was then transmitted to the cloud and used to diagnose health conditions such as hypertension, heart arrhythmia, atrial fibrillation, heart attacks, etc. However, the company’s existing models only delivered 50% accuracy in the analysis and classification of the data. By partnering with NorthBay to develop a new data lake that enabled them to more broadly experiment with ML and model training, they increased the accuracy of their IoT data to 95%.

AWS for AI/ML Use Cases

Performance

AWS provides a high-performing compute, networking, and storage infrastructure, available broadly on a pay-as-you-go basis, enabling development teams to train their models on an as-needed basis and not let infrastructure hold back their innovation.

Security and Compliance

ML applications often involve the use of data that may contain commercially sensitive, personally identifiable, or proprietary information that must be fully protected. AWS SageMaker and associated services such as Amazon Elastic Container Registry (Amazon ECR), Amazon S3, and CodeCommit all support end-to-end encryption both at rest and in transit.

Cost

As companies move from exploring and experimenting with machine learning to deploying their applications at-scale, AWS offers the ideal combination of performance and low-cost infrastructure services across the entire application development lifecycle.

Flexibility and Agility

Over 70% of companies that are building machine learning applications have stated that their teams use a mix of different ML frameworks. AWS ML infrastructure supports all of the popular deep learning frameworks such as TensorFlow and PyTorch, allowing teams to pick the right framework to match their preference and development efficiency.