Data Orchestration and Artificial Intelligence
Accelerating Value Creation through Data Analytics, AI, and Effective Data Management
As value chains evolve, utilizing reliable data is essential. Orchestrating data effectively is crucial for implementing a successful AI strategy, ensuring seamless integration and management across various sources and systems. By embracing this strategy, your business can adapt to change, optimize decision-making, and maintain a competitive edge while fully harnessing AI's potential.
Uncover the competitive advantage by embracing a wide range of AI applications employed by industries' leaders
Boost your business with cutting-edge solutions, including intuitive chatbots like ChatGPT, image recognition software like Google Cloud Vision, and advanced predictive analytics tools like TensorFlow, driving you toward heightened success and a prominent position in your industry.
$1,5tn
Projected AI market size by 2030
97%
of mobile users are using AI-powered voice assistants
54%
of executives say that AI solutions have increased productivity in their businesses
3rd
consecutive year AI is voted as the most impactful new technology among CEOs of top companies
AI, Machine Learning, and Data Science Services at Scale
AI & ML
Deep Learning
Computer Vision
Natural Language Processing
Reinforcement Learning
AutoML
Chatbots and Conversational AI
Robotics
Automated Planning
Search and Information Retieval
MLOps
Deep Analysis
Data Annotation
Statistical Analysis
Predictive Analytics
Data Mining
Time-series Analysis
Causal Analytics
Data Visualozation and Reporing
DATA Science
Unleashing Business Potential: The Benefits of AI and Machine Learning Solutions
With successful implementation of AI/ML solutions, companies can benefit from reduced operational costs, streamlined internal processes, and enhanced sales with unique product features. AI/ML architects analyze business requirements to provide tailored recommendations for organizational improvements, resulting in full value from the AI/ML solution across the enterprise.
Business Advantages of AI/ML
effective way to identify issues that are easily missed by automated testing
Data analytics and visualization is vital for gaining insights. With the help of machine learning models, organizations can analyze massive amounts of data and predict emerging customer behavior, making proactive responses possible.
Machine Learning Analytics
By automating repetitive tests, automated testing can save time and resources
Integrating AI programs for business processes leads to significant improvements. The right AI solution reduces costs, improves customer support, and enhances consistency. Building a prototype (a so-called MVM) can prove the potential value of AI by highlighting efficiency and operational gains achievable with available data.
Artificial Intelligence Data Analytics
Evaluate software quality across the SDLC, providing critical feedback earlier and enabling higher-quality and faster deliveries.
What are you missing by not having a modern data strategy?
Unlocking Business Potential: The Importance of a Strong Data Strategy.
Unlocking Profit Potential with Data
To stay ahead of competitors, it is crucial to align your data strategy with market demands. A modern data platform enables the delivery of new features and improved customer outcomes. However, building such a solution often requires expertise from multiple vendors and complex solutions. At Architech NYC, our consultants and technicians work to create a tailored data platform that delivers real-time insights.
Streamlined Data Handling
Legacy systems are incapable of keeping up with today's data scale, which limits the potential for maximum business value. Architech NYC works with multiple vendors and platforms to modernize data warehousing, delivering a solution that meets changing business needs. We combine our consulting and technical expertise to pinpoint key business opportunities and create a progressive migration and implementation plan for your new data warehouse.
Maximizing the Value of Your Data
Advanced data visualization, data governance, and AI implementation are critical to derive value from your modern data warehouse. Architech NYC works with leading vendors such as AWS, Google, Microsoft,Tableau, and others to design and implement a custom data solution that aligns with your business needs. Our experts provide a comprehensive approach to help you get the maximum out of your data.
Data Science and AI Solutions for Industries
Transforming Business with Data Science and AI Solutions Across Industries
Fintech and Insurance
Fraud detection and financial forecasting using predictive modeling
Fast and accurate liquidity analysis for risk management
Efficient management of insurance claims and credit scoring
Customized wealth management using intelligent advisors
Tracking of Key Performance Indicators (KPIs) and visualizing performance
Detailed analysis of Profit and Loss (P&L) for benchmarking and insights.
Marketing
Personalized demand prediction and marketing
Intelligent customer segmentation
Automated real-time data aggregation
Real-time optimization and reallocation of marketing budgets
Tracking of KPIs and visualization of performance
Optimized returns on marketing investments
Accelerated reporting and retrieval of information
Product recommendation engines
Personalized shopping experiences and conversational commerce
Ecommerce
Intelligent customer service and chatbots
Personalized shopping and conversational commerce
Demand prediction and targeted advertising
Smart inventory control and management
RFM analysis and tracking of customer preferences
Recommendation engines for shoppers
Healthcare
Customized patient care and performance analysis
AI-assisted pharmaceutical research
Real-time medical data processing
Streamlined logistics, delivery, and supply operations
Automation of diagnostics and risk identification
Clinical data analysis through OD technology
Tracking of KPIs and performance visualization
Safe PHI access for authorized personnel
Education
Personalization of student experiences
Analysis of student achievement data
Captioning and accessibility of educational videos
Student success analysis and educational planning
Building a future-ready enterprise data vault
Analysis of customer lifetime value, retention, and attrition
Analysis of budget efficiency
ERP Software
Secure and regulatory-compliant enterprise data storage
AI/ML integration to empower ERP systems
Analysis of customer lifetime value, retention, and attrition
Consistent data architecture ready to scale
Digital mapping of the customer journey
Streamlined business process automation
Transportation and Logistics
Warehouse operations automated with data-driven processes
Real-time intelligent routing of supply and distribution
Predictive maintenance to ensure vehicle reliability
Intelligent fleet managemen
tReal-time traffic forecasting and updates
Automation of supply chain management
Tracking of KPIs and visualization of performance
Our process
Help identify patterns and insights that can drive informed decision-making.
The discovery stage involves identifying the problem to be solved, gathering and analyzing data, and determining the feasibility of using AI/ML.In this stage, it's important to define project goals, establish a hypothesis, and explore potential data sources and preprocessing requirements before selecting appropriate algorithms.
Discovery
In this stage, the team performs cleaning and formatting data, selecting appropriate algorithms, training and testing models, and evaluating results for refinement.
Modeling
This stage involves translating business requirements into technical specifications, selecting appropriate technologies, and creating a prototype or minimum viable product for testing.
Design and Build
The deployment stage incorporates integrating it into the existing system, testing for functionality and performance, monitoring for errors and updates, and ensuring that the solution meets the desired business objectives.
Deployment
Validation
The team executes the testing of the solution in real-world scenarios, identifies areas of improvement, and implementes necessary changes to improve performance, accuracy, and efficiency. This stage also includes retraining and tuning models for better results.