If you want to stand out in a competitive market AND drive better outcomes, now’s the time to explore the power of AI.
Whether you’re starting fresh or scaling up, our experienced team brings you robust strategy, capabilities, industry experience, and partnerships to deliver innovative, cost-effective AI solutions that provide value. Why spend time and resources building an AI team from scratch or working with partners who don’t support you every step of the way?
Whatever expertise you’re looking for, we can provide it. Get ahead of competitors and take steps to rapidly operationalize AI today.
See below to discover the range of capabilities we can help with:
Other AI analyzes data, but Generative AI (GenAI) generates new content like text, images, code, audio, and music by finding patterns in large quantities of data it’s been trained on. GenAI has many applications, including the following:
Content creation and marketing: Writes blog posts, articles, emails, and social media posts. Designs ads and images
Customer support/engagement: Answers questions and complaints via chatbots or virtual assistants; automates language translation; translates speech-to-text and text-to-speech
Software development: Writes code; detects bugs; automates programming tasks, processes, and testing to speed up repetitive IT and operations tasks
Design: Creates graphics, fashion design, and architectural plans
Product design and innovation: Creates prototypes and mockups; performs healthcare research/design; aids in drug discovery; and creates reports and summaries for R&D
Entertainment: Produces original videos, music, or animation
Machine learning (ML) teaches computers to learn from data, analyzing analyzes data and making decisions and predictions without being explicitly programmed for a specific task. ML models identify patterns and improve accuracy and performance over time.
ML is ideal for automating repetitive, data-heavy tasks; reducing human error; making predictions; personalizing experiences; and helping you make data-informed decisions.
Customer experience and insights: Analyzes customer behavior to suggest personalized recommendations for products (like on Amazon, Netflix, and Spotify)
Finance and business: Analyzes market trends and makes predictions; optimizes investments; detects fraud and manages risk in real time; provides credit scoring; and automates reports to summarize financial and operational data
Marketing and sales optimization: Segments customers, creates targeted ads, predicts which customers are most likely to convert, and optimizes ad in real time
Supply chain and operations optimization: Provides demand forecasting for inventory, optimizes logistics, and improves quality control
Healthcare: Assists in diagnosing diseases, predicts patient outcomes, personalizes treatments, forecasts patient risks, and aids in drug discovery research
Predictive maintenance: Anticipates equipment failures to reduce downtime and maintenance costs
Natural Language Processing (NLP) makes the interaction between humans and computers more natural and efficient. It teaches machines to understand, interpret and generate human language that doesn’t sound robotic. NLP is used in many kinds of business applications:
Customer support: Uses chatbots to respond to customer queries 24/7 with instant, accurate responses that seem as if written by humans
Sentiment analysis: Analyzes social media posts, reviews, and feedback to gauge public sentiment
Text processing: Helps filter spam to identify and block junk email
Language translation and transcription: Translates other languages, like Google Translate does. Uses voice recognition to convert audio into text, as used in speech-to-text applications
Content creation and summarization: Enables GenAI to draft emails, reports, and creative content. Summarizes large articles into summaries and key points.
Deep learning, a subset of ML, is inspired by aspects of human brain function and uses neural networks (interconnected nodes that transmit and process information) to transmit and process data and make complex decisions. Unlike traditional ML, deep learning handles vast amounts of unstructured data, including images, speech, and text. Neural networks help AI learn and improve, making them key to modern AI systems.
Image and video analysis: Facial recognition, medical imaging, and quality control inspection of products in manufacturing for defects
Speech and language processing: Powers voice assistants (such as Siri and Alexa), automated transcription (YouTube captions), and AI chat models (ChatGPT, Gemini, and Copilot)
Autonomous systems and robotics: Enables self-driving cars to understand their environment, objects, and people and make real-time decisions. Powers delivery drones and robotics automation in warehouses
Fraud detection and cybersecurity: Helps detect anomalies, such as unusual bank transactions. Detects malware. Predicts and prevents cyberattacks
Predictive analytics and business intelligence: Used as a foundation to forecast trends and outcomes in finance, marketing, and healthcare
Computer vision (CV) enables machines to see, analyze, and understand visual data, such as images and video. It mimics human vision, but at a much larger scale and speed, using deep learning and neural networks to recognize objects, patterns, and actions. Then CV makes decisions based on the visual data.
Some uses for computer vision include:
Retail and e-commerce: Enables self-checkout systems, analyzes shopper behavior, and monitors inventory in stores. Facilitates virtual try-on for ecommerce
Manufacturing and quality control: Detects defects in products on assembly lines and monitors process automation
Healthcare: Assists in medical imaging by detecting tumors, fractures, abnormalities, or diseases in X-rays, MRIs, or CT scans. Monitors hospital patients by tracking movements and vitals
Security and surveillance: Provides facial recognition in airports, banks, and public spaces; detects unauthorized access; and finds anomalies (such as suspicious behavior) in security footage
Autonomous vehicles and transportation: Detects obstacles, pedestrians, and signs, and adjusts accordingly in real time. Analyzes congestion and optimizes traffic flow
Agriculture and farming: Uses drones to monitor crops, detect plant diseases and soil conditions. Tracks livestock health and behavior
We’ve got experience helping top organizations in a variety of industries, including healthcare, life sciences, financial services, telecom, and manufacturing.
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Microsoft Fabric and Co-Pilot AI is an end-to-end analytics and data platform that unifies various technologies, including Azure Data Factory, Azure Synapse Analytics, and Power BI.
Databricks combines generative AI with the unification benefits of a lake house architecture. This allows it to power a Data Intelligence Engine that understands the unique semantics of your data.
Salesforce Einstein is an AI platform integrated into Salesforce’s suite of business solutions. It offers features like lead conversion prediction, chatbots for customer service, and personalized product recommendations.
Google Cloud’s AI Platform simplifies the end-to-end machine learning workflow, allowing developers to build, train, and deploy models efficiently.
AWS provides a wide range of AI services, including machine learning, natural language processing, and computer vision, which can be easily integrated into applications.
Our data-driven approach to AI, along with our experience, expertise, partnerships, and proven methodology for AI adoption will drive innovation and continued transformation.
AI Development
AI Proof-of-Concepts
AI Discovery & Education
AI Centers of Excellence
AI Discovery and Strategy
AI Service Disciplines focused on outcomes
AI Evolution Framework
AI is Transformative but Misrepresented.
AI isn’t just tech, but a new business function
A classic data foundation is necessary for AI
Microsoft Fabric
Databricks
Salesforce
Google AI
AWS
Contact us to learn how our tailored solutions can drive your organization's success in a data-driven world.
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