FinTech is a technology market intelligence platform that delivers data-driven insights and analysis to businesses, investors, and professionals worldwide. We provided FinTech with access to comprehensive data on startups, venture capital groups, private equity firms, M&A, and emerging industries across the globe.

25+

Team Members

115,000

Hours in Labor Saved

$1,300,000+

In Cost Savings

Revenue: $100M per year
Employees: 400+
The Team:
  • 25 Data Mining Technicians
  • 5 Quality Control Mangers
  • 2 Training and Deployment Leads
Results:
  • Outperformed accuracy goals at a rate exceeding 99%
  • Surpassed speed and processing targets
  • Saved over 115,000 hours in labor 
  • Reduced labor costs by $1,325,500

The Challenge

FinTech uses large scale algorithm-based scraping to collect massive amounts of data. Machine Learning tools can help sort and organize this trove of data. However, the client also needed human support to help process and maintain the accuracy of its extensive data and information on medium- and large-market companies across various domains.

The Solution

TechSpeed trained a dedicated team of 25 highly specialized technicians to directly Review, Validate and Enhance the client’s database profiles. Utilizing a cross-trained rotating team model, TechSpeed provided seamless 24/7 support, responding to surges in volume while simultaneously leading additional special projects for the client.

Value Add


  • Assembled, trained, and deployed our team in less than 7 days


  • Produced comprehensive training manuals to ensure that all team members have a clear understanding of the company’s processes and procedures


  • Implemented a Quality Assurance system to monitor accuracy and performance metrics and ensure that the highest standards are being met


  • Developed a suite of reports and dashboards to provide real-time visibility into processing speed, accuracy, and training progress


  • Conducted bi-weekly leadership calls with clients to discuss processing analysis, provide updates, and make recommendations for process improvements based on data insights