AdTech Engineering Expertise with the Speed of Programmatic

We automate the repetitive, high-volume workflows inside AdTech platforms that currently depend on manual intervention.

This includes building the agents and automated systems that manage internal operations: seller agents that handle inventory decisions and floor pricing, buyer agents that execute campaign pacing and bid strategy, signal agents that process data streams and trigger downstream actions, and creative agents that select or assemble ad creative based on context.

Automation can be designed with human-in-the-loop approval steps for decisions requiring oversight, or run as fully automated systems where speed is the priority. The goal is to reduce the operational overhead inside your platform so your team spends less time on routine execution and more time on strategy.

ML has been a cornerstone of AdTech for well over a decade, and the systems that drive real performance have always been model-driven.

We build and improve the decision engines at the core of programmatic infrastructure: floor pricing systems that dynamically adjust reserve prices based on real-time signals, bidding strategy models that optimize spend against performance goals, campaign pacing and budget allocation logic, audience scoring and lookalike modeling, and yield optimization systems that balance competing demand sources.

These are not new problems for us, and the maturity of our ML practice means we build systems that perform in production at auction speed, not just in notebooks.

We build generative AI tooling purpose-built for AdTech workflows, including retrieval-augmented generation (RAG) systems that ground outputs in your own data, media plan generators, automated reporting pipelines, campaign brief and creative brief generation, and operational summary tools.

RAG is particularly valuable for AdTech use cases where accuracy matters, allowing models to draw from proprietary inventory data, audience segments, historical performance, and internal documentation rather than relying on general training knowledge alone.

AI readiness is as much a data and infrastructure problem as it is a modeling one.

We help clients structure platforms and pipelines for model training and inference, while doing the foundational data work that makes it possible: organizing, cleaning, labeling, and aggregating data across fragmented sources, building ETL and real-time ingestion pipelines, and instrumenting the reporting needed to monitor data quality over time.

The companies that move fast on AI are the ones who invested in the foundation before they needed it.

We build the connectivity layer that lets your stack participate in the emerging agentic advertising ecosystem.

This includes MCP server and client implementations that expose your platform capabilities as tool-callable services, agent-to-agent (A2A) communication patterns that allow autonomous systems to coordinate across organizational boundaries, and traditional API integration work connecting your infrastructure to external AI platforms and third-party models.

AdCP is built on these protocols, making this connectivity work foundational to any agentic advertising strategy.

We build AI-powered tooling that improves engineering velocity and platform reliability, including automated root cause analysis, alert deduplication, and code assistance for internal development teams.

Beyond internal tooling, we also build external-facing tools including ad server integrations, custom reporting and analytics interfaces, developer SDKs, and platform extensions that expose capabilities to third-party systems.

Whether the audience is your engineering team, your operations team, or your clients and partners, we build the tooling layer that makes the platform more usable and more automated.