Engineering firms can lose a project before it even starts. The crucial element: time. 

Bidding can be a brutal process, in that it is the bread and butter of the business and yet a lot of times qualified engineering firms miss out on projects because they take too long. Traditional bidding methods often rely on manual processes that are slow, error-prone, and difficult to scale.

‍TL;DR

• AI tools now perform quantity takeoffs faster and with accuracy as high as 97%, dramatically reducing human error and accelerating bid preparation
• Machine learning models, deep learning, and hybrid approaches, typically achieve cost estimation accuracy between 85–90%, significantly outperforming conventional methods
• AI systems also leverage thousands of past projects to establish robust cost benchmarks and dynamically adjust for variables like location, material costs, and project scale
• With faster takeoffs, sharper accuracy, and smarter cost predictions, AI-driven bidding puts firms ahead of the curve winning more contracts while minimising risk.

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Challenges in the traditional bidding process

• Inaccurate Cost Estimation: Manual estimation methods are prone to errors, leading to inaccurate bids. Estimators have to assess specs line by line and match them with the RFP scope and many times, costs balloon in the absence of clarity. For example, failure to account for changing material costs and market conditions can lead to underpricing. 
• Incomplete or Ambiguous Bids: Missing details or unclear scope descriptions can result in misunderstandings and disputes. This not only leads to revenue loss, but also drains motivation of the team members. 
• Lack of Historical Data Analysis: Not leveraging past bid data can result in missed opportunities for improvement. 
• Inefficient Document Management: Construction documents are massive, often hundreds of pages long and manual handling of bid documents can lead to errors and inefficiencies. Files may be scattered across various shared drives and more often than not, if documents cannot be traced, the bid gets dropped. 

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AI-driven tools add speed, with precision

• Intelligent Pricing and Contract Analysis: AI tools can assess past pricing, market trends, and contract terms to recommend competitive and profitable strategies. A standout feature of regression models is their ability to account for changing market conditions. 
• Bid Error Detection Systems: AI-driven error detection tools review entire bid packages to identify missing information, pricing mistakes, and calculation errors. They also verify scope descriptions, material quantities, and labour estimates to ensure comprehensive and accurate bids.
• Faster estimation speed: By automating material measurement through an analysis of blueprints, AI-driven tools can accelerate the estimation process by 80% compared to manual methods. These tools are trained algorithms built on thousands of past projects to calculate:
  
  • Exact quantities
  • Material classifications
  • Labour and installation estimates

• Improved Bid Success Rates: With estimation becoming more data-backed, highly precise and super fast, the AI-assisted estimation and targeted bidding can boost win rates by up to 20% through faster, more accurate proposals.

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With all these benefits, let’s also look at who gets impacted during this process. In a French bureau d’études techniques (BET), the bidding process (appel d’offre) is a team effort, but some roles are more directly involved than others. Bidding is a high impact activity and it directly affects the revenue of the engineering firm. 

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Firms that embrace AI, not for hype, but for operational precision, will deliver projects more reliably, efficiently, and intelligently.

At Temelion, we think in workflows. A workflow is simply how tasks move from one stage to the next without gaps or miscommunication. When it’s tight, errors drop and projects run smoother. When it’s loose, things slip. Our workflows helps engineering firms with better, smarter, faster bidding. Explore more here.

Temelion is an AI-copilot supporting design firms in eliminating documentation bottlenecks and elevating engineering execution.  

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References:

1. Altersquare
2. MDPI
3. Altersquare

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