S I M C O E . A I


And the lake is intelligent

As resumes are added to the lake, they are actively analyzed and ranked within the lake. The lake’s facade presents the results which are already intelligently-graded.

Example of lakes!

Marco, Elissa, Ryan, Nicole, and Ashley launched their startup and their technology team has been growing fast. They need to hire another five .net developers and one Cloud Architect. They drafted two types of job descriptions: 

  • One job description to hire .net developers
  • Another one to hire the Cloud Architect.

They posted these job descriptions on their website and received 5,000 resumes for their .net developer position and 2,000 resumes for the Cloud Architect position. 

They created two lakes: one for .net developers and another one for the architect role. After uploading the resumes to the right lake, they ran a search against the developers’ lake and got a shortlist of .net developers for further qualification.
It turns out that their startup actually needs two cloud architects instead of one. One architect with experience in Microsoft Azure and the other one with experience in AWS. So, they prepare two more specific architect role job descriptions and run two separate searches against the Cloud Architect lake.
As it’s seen, one lake i.e. Cloud Architect’s is used to be looked up for two (and even more) job descriptions.

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