Purpose-built infrastructure for high-performance advertising operations.
A modular platform designed for reliability, speed, and seamless integration with advertising APIs.
AdPilot is built as a set of loosely coupled services, each responsible for a specific domain of advertising operations. This architecture allows us to deploy, scale, and update individual components independently without disrupting the entire system.
At the core, an API integration layer handles all communication with advertising platforms. Campaign management, reporting, optimization, and alerting services consume data from this layer and expose functionality through a unified internal interface.
The platform processes campaign data in near real-time, with performance metrics refreshed at configurable intervals ranging from 15 minutes to 24 hours depending on the use case.
We choose proven, well-supported technologies that let us move fast without sacrificing reliability.
Our backend services are built primarily in Python, leveraging its rich ecosystem of data science and API client libraries.
Direct integrations with advertising platform APIs for campaign management, reporting, and optimization.
Data pipeline and analytics infrastructure for processing campaign metrics and generating performance insights.
Cloud-native infrastructure designed for reliability, security, and automated operations.
Security-first approach to credential management, data encryption, and access control across all systems.
Comprehensive monitoring and alerting to maintain platform health and detect issues before they impact operations.
The Google Ads API is one of our most critical platform integrations. We use it to programmatically manage campaigns across Google Search, Display Network, YouTube, and Performance Max.
Our integration leverages the official Google Ads API Python client library and follows Google's recommended patterns for authentication, error handling, rate limiting, and data retrieval.
Integration highlights:
Data-driven algorithms that continuously learn from campaign performance to improve budget allocation and bidding decisions.
Models trained on historical campaign data predict which campaigns will deliver the best returns, enabling proactive budget shifts before performance degrades.
Statistical models monitor campaign metrics in real-time, detecting unusual patterns in spend, conversion rates, or CPAs and triggering alerts before issues escalate.
Time-series forecasting models project future spend and conversion volumes, helping the team plan budgets and set realistic performance targets.