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AI Consulting & Building Strategy

Our comprehensive range of offerings includes the following:

AI Consulting and Strategy Building
Data Identification & Management
Technology Selection & Recommendation:
Data Intelligence
Data Mesh Implementation:
AI Model Development & Deployment:
Change Management & AI Adoption:
AI Ethics & Compliance:
Ongoing Support & Optimization:

Our AI strategy consulting for enterprises is a comprehensive plan that outlines how an organization will leverage artificial intelligence (AI) technologies to achieve its business objectives and gain a competitive advantage. It involves identifying opportunities for AI implementation, setting priorities, selecting the right technologies, and creating a roadmap for AI adoption and integration across the organization.

We are a modern-edge technical company that specializes in providing machine learning solutions to our clients. Our team of experts leverages the latest advancements in machine learning and artificial intelligence to develop innovative solutions that deliver real value and ROI to our clients. We work closely with our clients to understand their unique business needs and develop tailored solutions that meet those needs. Our focus on modern-edge technologies enables us to stay ahead of the curve and deliver cutting-edge solutions that give our clients a competitive edge in their industries.ations, and achieve a market edge.

AI Strategy and Roadmap Development:

We enables our clients to develop a clear and actionable plan for AI adoption and implementation, based on their unique business needs and goals. Our team of experts has the skills and expertise to help our clients assess their business, technology, and data assets, identify relevant AI use cases, and develop a comprehensive roadmap for AI adoption and implementation, ensuring they are well-positioned to succeed in the AI era.

Offerings:
We assist organizations in defining their AI vision, identifying opportunities, and creating a strategic roadmap for AI adoption and integration across various business functions.
Evaluate an organization's current state of AI adoption, Gaps and capabilities which involves Business Assessment, Technology Assessment and Data Assesment including hardware, software, and cloud infrastructure, and identify opportunities for AI integration.
Identify the most relevant AI use cases for their business, based on their goals, challenges, and opportunities which drives Increasing Revenue, Optimizing the Cost and Enhancing the Customer Experience
We develop a comprehensive roadmap for AI adoption and implementation within our clients' organizations, including timelines, milestones, and resource requirements.
Provide implementation support to help our clients execute the roadmap and achieve their AI goals, including project management, training, and ongoing support.
Data Identification & Management:

Our Data Identification and Management expertise enables our clients to identify, collect, and manage relevant data sources to support their AI initiatives. Our team of experts has the skills and expertise to help our clients ensure their data is of high quality, easily accessible, and compliant with regulations and best practices for data governance.

Offerings:
Identifying the relevant data sources for the AI initiatives, including internal and external data sources, such as structured and unstructured data, and public and private data sources.
Collect the identified data sources, including data scraping, data extraction, and data integration, and prepare them for use in AI initiatives.
Ensure the data is of high quality and meets the required standards for accuracy, completeness, and consistency.
Store the collected and processed data in secure and scalable storage systems, such as cloud-based data lakes or data warehouses, to ensure it is easily accessible for analysis.
Establish data governance policies and procedures to ensure compliance with regulations and best practices for data security, privacy, and ethics.
Provide ongoing data management services, including data cleaning, data transformation, and data enrichment, to ensure the data remains relevant and useful for AI initiatives.
Implement data cataloging, metadata management, data quality management and data lineage tools.
Technology Selection & Recommendation:

We recommend technolgoies and frameworks which enables our clients to select the right technologies, strategies, algorithms and tools to support their AI initiatives, and ensure they are equipped with the right infrastructure to succeed. Our team of experts has the skills and expertise to help our clients evaluate and select the best technologies and tools for their unique needs, ensuring they have the support they need to achieve their AI goals.

Offerings:
Analyze business requirements and goals to recommend the right AI technologies and platforms.
Evaluate various AI solutions, including commercial, open-source, and custom-built options.
Assist with technology procurement, vendor selection, and contract negotiations.
Evaluate the selected technologies using a range of criteria, such as ease of use, scalability, security, performance, and cost-effectiveness.
Assessment of our clients' existing technology stack, including hardware, software, and cloud infrastructure, to identify gaps, opportunities for improvement and provide implementation support to help our clients integrate the recommended technologies and tools into their existing technology stack and ensure a smooth transition.
Data Intelligence:

As part of our AI strategy consulting services, we offer Data Intelligence, which is the process of transforming raw data into actionable insights using machine learning and artificial intelligence. Our Data Intelligence service enables our clients to make more informed business decisions, improve their operational efficiency, and gain a competitive advantage in their industries. Our team of experts has the skills and expertise to help our clients extract maximum value from their data and develop machine learning models that deliver real-world results.

Offerings:
Implement advanced analytics and machine learning models to derive actionable insights from data.
Develop data visualization and reporting tools to enable effective data-driven decision-making.
Provide training and support to help businesses become more data-driven.
Identify and analyze underutilized or untapped data sources (Grey Data) within the organization.
Implement data cleansing, enrichment, and integration processes to improve data quality.
Data Mesh Implementation:

Our AI strategy consulting offering aims to provide organizations with an innovative approach to data management and scaling their AI initiatives. We incorporate the Data Mesh concept, a decentralized data architecture, to help businesses overcome common data-related challenges and efficiently deliver AI solutions.

Offerings:
Domain-oriented data ownership: We enable organizations to establish domain-centric teams that own and manage their data products. This approach leads to better data quality, higher accountability, and improved collaboration between teams.
Self-serve data infrastructure: Our Data Mesh implementation ensures that domain teams have access to a self-serve platform for data discovery, ingestion, and processing. This empowers teams to work independently and make data-driven decisions more efficiently.
Data as a product: We promote the idea of treating data as a product, ensuring it meets the needs of its consumers. This includes adopting product management practices for data, such as defining clear SLAs, managing lifecycle, and ensuring data quality.
Federated governance: To balance autonomy and governance, we establish a federated data governance model. This model provides a set of shared principles, policies, and tools to manage data while allowing domain teams to operate independently.
Enhanced collaboration: Data Mesh promotes cross-functional collaboration, allowing domain teams to share data and insights, driving data-driven decision-making across the organization.
AI Model Development & Deployment:

Our AI strategy consulting offering focuses on providing comprehensive guidance and support for organizations looking to develop, deploy, and scale AI models effectively. We adopt a structured approach to ensure seamless AI model development and deployment while minimizing risks and maximizing ROI.

Offerings:
Design, develop, and deploy custom AI models to address specific business challenges. We guide organizations through the process of selecting and developing the most suitable AI Algorithms/models for their specific use cases. Our experts leverage cutting-edge techniques and best practices to build robust and accurate models.
We are expert in creating accurate AI models for various applications, such as predictive analytics, recommendation systems, and anomaly detection. We have deep understanding with Supervised and unsupervised learning, time-series forecasting, clustering, and classification algorithms. Our deep learning expertise enables us to develop advanced neural networks for tasks like speech recognition, natural language understanding, and image synthesis.
We also offer custom development services for clients who require bespoke machine learning solutions. Our team of experts can work with them to develop custom algorithms, data pipelines, and other components to meet their unique needs.
We utilize a variety of cloud platforms including Azure, GCP, and AWS to provide machine learning training services that meet the unique needs of our customers. Our approach allows us to leverage the strengths of each platform, enabling us to build and train high-performing machine learning models that deliver accurate results. Whether our clients need to build models for computer vision, natural language processing, or predictive analytics, we have the expertise and resources to help them achieve their goals using these powerful cloud platforms.
We assist businesses in deploying, monitoring, and managing their AI models, ensuring scalability, performance, and reliability. Implement machine learning operations (MLOps) frameworks for model management and monitoring.
We help our clients deploy their machine learning models in a variety of environments, including web services, mobile applications, and edge devices. We can also integrate these models with their existing systems, such as CRMs or ERPs.
We offer ongoing support for our clients' machine learning models, including monitoring and maintenance. We help them to monitor the performance of their models and make necessary adjustments over time to ensure that they continue to deliver accurate results.
We are good at (but not limited):
TensorFlow: Developed by Google Brain, TensorFlow is an open-source machine learning library that allows for easy model building, training, and deployment. It is highly flexible and supports a wide range of neural network architectures and machine learning models.
Keras: Keras is a high-level neural networks API that is designed for fast prototyping and ease of use. It is written in Python and can run on top of TensorFlow, Microsoft Cognitive Toolkit, or Theano. Keras is popular for its user-friendly interface and modular design.
PyTorch: Developed by Facebook's AI Research Lab, PyTorch is an open-source machine learning library based on the Torch library. It provides a flexible deep learning platform with strong GPU acceleration support and dynamic computation graph capabilities, making it a popular choice among researchers.
Scikit-learn: Scikit-learn is a widely used Python library for machine learning that provides simple and efficient tools for data mining and data analysis. It offers a variety of algorithms for classification, regression, clustering, dimensionality reduction, and model selection.
Ensemble: Ensemble algorithms are a type of machine learning technique that combine the predictions of multiple models to improve the overall accuracy and performance. The main idea behind ensemble methods is that a group of diverse models working together can make better predictions than a single model alone. This is often compared to the wisdom of the crowd, where the collective opinion of a group is more accurate than the individual opinions of its members.
XGBoost: XGBoost is short for eXtreme Gradient Boosting, is a popular machine learning algorithm that falls under the category of ensemble learning. It is an extension of the gradient boosting framework, which is designed to improve the accuracy and speed of decision-tree-based models.
LightGBM: Developed by Microsoft, LightGBM is a gradient boosting framework that uses tree-based learning algorithms. It is designed for efficiency and scalability, making it particularly useful for large datasets and resource-constrained environments.
CatBoost: Created by Yandex, CatBoost is an open-source gradient boosting library that offers high-performance, out-of-the-box support for categorical features. It is known for its robust handling of categorical data and its ability to reduce overfitting.
Theano: Theano is an open-source numerical computation library for Python that allows developers to efficiently define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays. Although it has been surpassed by TensorFlow and PyTorch, it still has a loyal following due to its performance and flexibility.
Microsoft Cognitive Toolkit (CNTK):: Developed by Microsoft, CNTK is an open-source, deep-learning library that supports a variety of neural network architectures and offers strong multi-GPU support. It has a strong focus on performance and scalability.
H20:: H2O is an open-source machine learning platform that provides a wide range of algorithms for data analysis, including deep learning, gradient boosting, and generalized linear models. It is designed for ease of use, scalability, and performance, with support for distributed computing.
Apache Spark MLlib:: Apache Spark MLlib (short for Machine Learning Library) is an open-source, scalable machine learning library built on top of the Apache Spark framework. It is designed to simplify the development of machine learning algorithms and provide efficient tools for large-scale data processing and analysis. MLlib is particularly well-suited for distributed computing environments and can handle large datasets that do not fit in the memory of a single machine.
Change Management & AI Adoption:
Offerings:
Develop change management strategies to facilitate the adoption of AI technologies across the organization.
Provide training and upskilling programs to enable employees to work effectively with AI.
Track and measure the success of AI initiatives and their impact on business performance.
AI Ethics & Compliance:
Offerings:
Develop and implement AI ethics frameworks and guidelines.
Ensure compliance with data protection and privacy regulations.
Conduct AI audits and assessments to mitigate potential risks.
We are experts in Bias detection and mitigation, fairness, accountability, transparency, and explainable AI (XAI) techniques like LIME and SHAP.
Ongoing Support & Optimization:
Offerings:
Provide ongoing support for AI initiatives and projects.
Continuously monitor and optimize AI models and applications.
Ensure alignment with business objectives and evolving market conditions.
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