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Initial
Requirements

Our client, a leading engineering firm, approached us with a critical challenge: accurately predicting tender prices to improve their competitiveness in the fiercely competitive construction industry. They needed a solution that could analyze competitors' bids and market data to provide precise price predictions, allowing them to make informed decisions during the bidding process.

Obstacles We
Overcame

During the development of this AI-powered solution, we encountered several challenges:

1. Data Quality: Obtaining high-quality historical bid data from competitors proved challenging. We implemented data cleansing and validation processes to mitigate inaccuracies.

2. Real-time Data Integration: Integrating real-time market data required seamless API integration and robust error-handling mechanisms to ensure uninterrupted service.

3. Model Complexity: Developing a predictive model that could handle diverse projects with varying complexities was demanding. We overcame this by refining our feature selection and engineering processes.

4. Scalability: As our client's project portfolio grew, the system needed to scale accordingly. We implemented cloud-based infrastructure and monitoring systems to handle increased workloads.

To meet our client's requirements, we developed a bespoke AI-based solution using Python and Scikit-Learn. Here's what our solution achieved:

Competitor Bid Analysis 1

Our system was designed to collect and analyze historical bidding data from government bid results within the engineering sector. It extracted relevant information, such as project details, cost breakdowns, and winning bid amounts.

Market Data Integration 2

We integrated real-time market data, including economic indicators, material prices, and labor costs. This enriched our prediction model with up-to-date information, ensuring accuracy.

Machine Learning Model 3

Using Scikit-Learn, we built a robust machine learning model capable of processing large volumes of data to predict tender prices. The model leveraged various regression techniques, feature engineering, and data preprocessing to optimize accuracy.

Accuracy Enhancement 4

We fine-tuned the model continuously, employing techniques like cross-validation and hyperparameter tuning to achieve the highest prediction accuracy possible.

User-Friendly Interface 5

To make the solution accessible to non-technical staff within the engineering firm, we developed a user-friendly interface. This allowed users to input project details and receive instant price predictions, along with comprehensive data visualizations.

The technology
used

Python

Scikit-Learn

Final Product

Our AI-powered solution for predicting tender prices has revolutionized our client's bidding process. By leveraging Python and Scikit-Learn, we provided an accurate, data-driven approach to tender pricing. The final product offers the following benefits:

  • Increased Competitiveness: The client can now make informed decisions, ensuring their bids are competitive while maintaining profitability.
  • Time and Cost Savings: Manual price estimation processes have been replaced with automated predictions, reducing both time and resource requirements.
  • Data-Driven Insights: The system provides valuable insights into market trends, helping the client adapt to changing conditions.
  • Scalability: With a scalable architecture, the solution accommodates the client's growing project portfolio.

Our AI-based solution has empowered the engineering firm to consistently secure profitable projects and maintain a competitive edge in the market, demonstrating the transformative power of data-driven decision-making in the construction industry.

Our clients simply love
our work

"Even though they work remotely, communication is almost in real-time."

Uli Ebensperger

Founder, Ziggma

"Great quality deliverable with respect to timeline and business scope. Great Team to work with in general. Definitely can recommend to anyone who is looking for Hi-Fi UX Mockups. Thank you!"

Ben Koussa

Founder

"From the very first call they were the only agency that has shown real interest and trying to understand the reason behind why I ask for what I ask for. They were customer oriented all the time and delivered on demand, while proposing improvements wherever possible. Very great cooperation! They have prepared my SaaS documents they will most likely be the partner to develop it in the end!"

Marco Koehler

Co-Founder

"If I sent them an email, I would get an immediate response and not have to wait a week."

Alexander Taubenkorb

CEO, Wopio

"They don’t treat you like an everyday client. You feel like you’re really important."

Hajj Womack

CEO, TeachersInTouch

"I am so happy I took a risk and hired them."

Jeffery N. Tejcek

Communication Director, Virtual EMDR

"Know high of the highest level and maximum availability. Highly recommended."

Daniele Nardin

Co-Founder

"The quality of work was great, but they went way past schedule (could be due to Corona, who knows). I will be working with them again, though. I would recommend them for any project."

Justin Butler

CEO

"Highly recommended and will use their services again."

Eli Fratkin

CEO, Heart2Heart

"We were impressed by their commitment to the project as they delivered the best possible result."

Sam O'Brien

Lead Project Manager, World Sport Action

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