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AEC Company Automates RFP Response Generation with Generative AI

In today’s hyper-competitive AEC landscape, responding to Requests for Proposals (RFPs) manually is like building a bridge one brick at a time—painstakingly slow and full of risk. For a national engineering and consulting firm on the East Coast, every delayed or missed RFP directly impacts revenue, pipeline momentum, and market positioning.

When response speed becomes a limiting factor, growth depends on how quickly teams can create, tailor, and submit high-quality proposals at scale–something that can be improved with artificial intelligence. According to McKinsey, AI could unlock between $2.6 trillion and $4.4 trillion in annual value globally by improving productivity and effectiveness across functions.

For RFP-driven organizations, that value shows up in the ability to respond faster, scale proposal capacity, and compete more effectively without increasing manual effort.

To address these challenges, this AEC firm partnered with CloudMoyo to infuse generative AI into its RFP workflow. Powered by Azure OpenAI, the new approach accelerated content creation, improved accuracy, and ensured every response reflected the company’s true capabilities and experience—backed by the right context, visuals, and data.

The results? Faster turnaround, reduced manual effort, and a stronger competitive edge in a crowded market.

Ready to see how AI transformed their RFP process from a bottleneck into a growth engine? Download the full case study.

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Cloud analytics with Compute Optimized Gen2 tier of Azure SQL Data Warehouse

Data has the power to transform a business in and out. In order to remain relevant and gain competitive advantage, an enterprise needs to have the ability to transform data into breakthrough insights. Data is growing exponentially. To control the flow of this huge chunk of data and to convert this data into meaningful insights, […]

Read more May 20, 2018
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The difference between artificial intelligence, machine learning, and deep learning

The tech world today is talking about three important terminologies: Artificial Intelligence, Machine Learning  and Deep Learning. These names often create confusions. Many think the three terms are one and the same when there are significant differences between them. They are often used interchangeably but that isn’t the case. So, what exactly is the distinction between […]

Read more April 04, 2018
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15 reasons why you should opt for a cloud data warehouse

Data holds the power to transform the business landscape, helping you to discover business insights and aids in decision-making. Yet, enterprise systems today generate large amounts of data, which is certainly not a piece of cake to manage. This new type of data comes with high volume, variety, and velocity, and is popularly known as […]

Read more March 19, 2018
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Understanding the concept of self-service BI in the cloud

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Read more February 28, 2018
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Building enterprise-class machine learning apps using Microsoft Azure

In our earlier post, we introduced the concept of Machine learning (ML) and also some types as well as applications in real world. In the second part of this series, lets peek into how to build Machine learning apps using Microsoft Azure. What is Azure Machine Learning Studio? Microsoft breaks down the use of Machine Learning (ML) […]

Read more January 30, 2018
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An ultimate, beginner’s introduction to machine learning

For many people, their first experience and introduction to Machine Learning was the ‘Recommendations’ feature on Amazon. The system was able to predict future choices to a consumer based on what previous choices they had made. Many people conflate the concepts of Artificial Intelligence and Machine Learning, whereas, AI is the ability of a machine […]

Read more January 10, 2018
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ABC of cloud data warehousing terms – A glossary

Data Warehouse, also known as enterprise data warehouse, is considered as one of the core elements of BI (Business Intelligence). Data warehouse is a system or means for reporting and data analysis and also supports the decision-making process. The process of planning, constructing, and maintaining a data warehouse system is called data warehousing. Now, to have an […]

Read more December 11, 2017
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A beginner’s guide to Microsoft’s Azure Data Warehouse

Your business data is extremely POWERFUL, only if you are able to use it properly– to generate valuable and actionable insights. However, it is also imperative to organize and analyze it well. A recent report says, less than 0.5% of the business data is actually stored and analyzed in a right way. As an impact, enterprises lose over $600 billion […]

Read more November 30, 2017
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Top 10 factors to consider while choosing a self-service BI solution

According to the report released by Gartner, the global market of Business Intelligence (BI) and analytics software will expand to reach $18.3 billion by the end of 2017 and shall continue to grow till 2020 to reach $22.8 billion.  Business users increasingly demand the access to important business data in real time. Business Intelligence (BI) is […]

Read more November 16, 2017
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5 reasons why you need a Power BI implementation partner

If you are still clouded on the idea of whether or whether not to have a Power BI implementation partner, you must read this blog. The answer is yes! Yes, you should have an implementation partner for Power BI. Power BI is a marvelous resource to any business. It is an asset that can bring […]

Read more November 13, 2017
Blog

What is the future of the cloud data warehouse?

The emergence of the data warehouse transformed the business information management landscape which was previously restricted to manual methods, complex & unwieldy spreadsheets and was generally inaccessible to the general users. Its exponential and rapid growth has made companies realizes the value of the data they generate. This gave rise to the environment of cloud […]

Read more October 25, 2017
Blog

A deep dive into the Microsoft Azure Data Lake and Data Lake Analytics

Today, large enterprise organizations are struggling with an ocean of data. From online shopping analytics to the Internet of Things (IoT) sensor data, the modern IT team is inundated with raw or semi-raw data captured from every side of the organization. These entities have begun dumping this raw data into a holding tank called the […]

Read more October 08, 2017