Takeoff Software for Construction Estimating

Proven to increase productivity by as much as 15 times

  • Higher bid output
  • improved win rates
  • decreased margins of error
  • affordable
  • easy to use

Affordable, Easy to Use Takeoff Software

eTakeoff is a leading provider of construction quantity takeoff software, including technology that tightly integrates the takeoff and estimating process. At eTakeoff, we listen to estimators like you to understand the unique and changing challenges you face each day.

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The task of takeoff is the most time consuming and error-prone part of construction cost estimating. Our eTakeoff Dimension software quickens the pace of accurate takeoffs with sophisticated assemblies, a patented symbol search for auto counting, innovative work breakdown capabilities, cutting edge database technology, and more. eTakeoff Dimension is so geared to simplify how estimators work, even Dodge Data & Analytics selected our Basic version for use in its plan rooms.

Takeoff Software for Surface Pro

100% compatible with Surface Pro 3 & 4

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Case Study

eTakeoff Bridge cuts estimating
time by an additional 50%

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The construction industry’s top estimating integration platform.

eTakeoff Bridge helps you build a solid base by efficiently transferring precise takeoff information from Dimension and into the most popular estimating software used by contractors—Sage Estimating.

There’s no time-consuming setup. As you use Bridge with Sage Estimating, you can easily map relationships between takeoff measurements and the items and assemblies in your construction estimating database. Once Bridge “learns” a relationship, it will recall it whenever the measurement, assembly or item is used again.

Learn more about eTakeoff Bridge

Award Winning Software

eTakeoff Dimension is an Industry Award Winner from Software Advice.

“One of the best designed
construction takeoff UIs
on the market.”

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Bridging the gap between BIM and estimating

Ask us about our upcoming integration with building information models.

Building Information Models