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  • Solution development
  • AI & ML

Building Performance-Oriented Mobile DSP with Innovative User Behavior Prediction Mechanism

Download the case study
mobile dsp

Client background

Dataseat, part of Verve Group, offers brands a demand-supply platform to manage their high-performance mobile advertising campaigns. The company provides advertisers with all the necessary tools to ensure effective control and transparency in media buying.

In December of 2019, Dataseat secured its seed funding and raised $2.3m from Play Ventures,, and SAATCHiNVEST.

Business Challenge

Dataseat was looking to quickly get a
share of the performance ad market.

  • Dataseat had 3rd party data on user behavior.
  • The idea was to build a DSP (media buying system) to enable ad targeting at users who have demonstrated interest in specific game genres.
  • Dataseat didn’t want to use white-label DSPs, due to the lack of features, flexibility, impact on capitalization, aggressively growing prices, and IP issues.
  • From-scratch development wasn’t a good fit for Dataseat because of time limitations.
  • Existing AI libraries were poorly integrable into AdTech solutions due to their real-time nature.

Xenoss Solution

Extremely fast start and process adaptation for quick delivery

Building the team of experienced engineers from top AdTech firms

We didn’t have much time for team integration into the domain. We needed a team that could start bringing value from day one. Xenoss leveraged its network inside the AdTech engineering community to hunt down the top engineers from the leading AdTech companies.

Fast start ensured by Xenoss Acceleration Team

Xenoss employed its in-house Acceleration Team. We initiated early-stage work streams while our Recruiting department was looking for permanent developers. Once the permanent devs were found, the Acceleration Team onboarded and trained them. They smoothly integrated the new team into the project as their replacement.

Job parallelization

Xenoss designed a development plan that would entail simultaneous feature development. This way we managed to ship the early versions very fast, so the business was able to swiftly start course calibration.

Streamlining processes during the early stages

We decided to take advantage of our highly-senior team and simplify engineering processes in order to ensure high development velocity. The established process was formalized after the MVP was shipped.

Technology boost

Using Ready-to-use AdTech components to speed up development

About 60 percent of the solution was built from Xenoss Framework, a library of ready-to-use blocks, enabling construction of the solution quickly.

Adopting AI toolset for AdTech to leverage high-performance computing

We compiled the AI models into directly executable code using a sophisticated approach. This enabled the adaptation of the state-of-the art AI toolset to be used in the high-performance environment.



3.5 months

The MVP version was delivered 14 weeks after the project kickoff. It’s one of the best results on the market for the complex DSP projects.


7 engineers

We managed to keep a very small team, due to team seniority, work parallelization, and the use of ready-made components.


4 SSPs

In just 3 months after the start of the development, the 4 major SSPs were successfully integrated.


400k QPS

400k queries per second handled by 8 c5.2x large servers.



Optimally designed solution architecture ensured record low expenses per one QPS. The monthly cost for the whole system is below $20k.



Due to the smart adaptation of the existing AI toolset, the real-time prediction mechanism was included in the MVP, ensuring above-market conversion rate.

Sounds interesting?

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