AdTech Lab

Applied ML for programmatic advertising.

Machine learning is how programmatic advertising achieves efficiency at scale — in floor pricing, supply paths, pacing, fraud, and agentic media buying.

How we work Read the research

$100M+

Ad spend optimized

35%

Average ROAS improvement

<5ms

Model inference latency

4 wks

Avg. time to production

Practice Areas

Production systems.

Each practice area applies machine learning to a structural inefficiency in the auction — at the latency, throughput, and accountability that live programmatic demands.

Core Optimization

Production-grade ML systems for the foundational problems of programmatic advertising — pricing, routing, scoring, pacing, and fraud.

Floor Price Optimization

Dynamic price floors that maximize publisher yield in real time using gradient-boosted models trained on auction outcomes.

Supply Path Optimization

Intelligent routing that cuts intermediary hops, reduces fees, and boosts win rates through learned bidder quality scores.

Traffic Ranking

ML-driven impression scoring to prioritize high-value traffic at scale, before it reaches the auction.

Budget Pacing

Adaptive spend algorithms calibrated against temporal bidder behavior patterns to hit campaign goals precisely.

Fraud Prevention

Multi-signal detection models that identify invalid traffic patterns before a single dollar is wasted.

Floor Price Optimization

Dynamic price floors that maximize publisher yield in real time using gradient-boosted models trained on auction outcomes.

Supply Path Optimization

Intelligent routing that cuts intermediary hops, reduces fees, and boosts win rates through learned bidder quality scores.

Traffic Ranking

ML-driven impression scoring to prioritize high-value traffic at scale, before it reaches the auction.

Budget Pacing

Adaptive spend algorithms calibrated against temporal bidder behavior patterns to hit campaign goals precisely.

Fraud Prevention

Multi-signal detection models that identify invalid traffic patterns before a single dollar is wasted.

Floor Price Optimization

Dynamic price floors that maximize publisher yield in real time using gradient-boosted models trained on auction outcomes.

Supply Path Optimization

Intelligent routing that cuts intermediary hops, reduces fees, and boosts win rates through learned bidder quality scores.

Traffic Ranking

ML-driven impression scoring to prioritize high-value traffic at scale, before it reaches the auction.

Budget Pacing

Adaptive spend algorithms calibrated against temporal bidder behavior patterns to hit campaign goals precisely.

Fraud Prevention

Multi-signal detection models that identify invalid traffic patterns before a single dollar is wasted.

The Agentic Lab

Next-generation autonomous systems that reason, negotiate, and optimize across the full advertising lifecycle.

Agentic Media Buying

Autonomous agents that negotiate, bid, and optimize campaigns end-to-end — reducing human overhead while improving outcomes.

LLM2RTB

Large language models fine-tuned for high-speed OpenRTB request generation, bridging natural language intent and programmatic execution.

Agentic Media Buying

Autonomous agents that negotiate, bid, and optimize campaigns end-to-end — reducing human overhead while improving outcomes.

LLM2RTB

Large language models fine-tuned for high-speed OpenRTB request generation, bridging natural language intent and programmatic execution.

Agentic Media Buying

Autonomous agents that negotiate, bid, and optimize campaigns end-to-end — reducing human overhead while improving outcomes.

LLM2RTB

Large language models fine-tuned for high-speed OpenRTB request generation, bridging natural language intent and programmatic execution.

Cloud ML Infrastructure

The platform layer that makes ad tech ML possible at scale — from training pipelines to sub-millisecond serving.

MLOps for Ad Tech

Production pipelines purpose-built for bidding latency constraints, with automated retraining and drift detection.

Cost Reduction

Right-sized inference, smart caching, and workload scheduling that typically cut cloud ML spend by 30-50%.

Low-Latency Serving

Sub-10ms model inference at the edge for real-time decisioning, with graceful degradation under load.

MLOps for Ad Tech

Production pipelines purpose-built for bidding latency constraints, with automated retraining and drift detection.

Cost Reduction

Right-sized inference, smart caching, and workload scheduling that typically cut cloud ML spend by 30-50%.

Low-Latency Serving

Sub-10ms model inference at the edge for real-time decisioning, with graceful degradation under load.

MLOps for Ad Tech

Production pipelines purpose-built for bidding latency constraints, with automated retraining and drift detection.

Cost Reduction

Right-sized inference, smart caching, and workload scheduling that typically cut cloud ML spend by 30-50%.

Low-Latency Serving

Sub-10ms model inference at the edge for real-time decisioning, with graceful degradation under load.

Engagements

Three engagement formats.

Each defines scope, timeline, and deliverables. Selection is matched to the maturity of the system being built.

Rapid-Impact Intervention

SWAT Team

A high-impact, multidisciplinary strike force that hits the ground running. We diagnose performance bottlenecks, architect the solution, and deliver measurable lift within weeks.

End-to-end diagnostic and solution delivery

Cross-functional teams: ML engineers, infra, and ad tech strategists

Measurable KPI improvement with defined timelines

Typical timeline: 4-8 weeksGet in touch

Applied Research Partnership

The ML Lab

A dedicated research partnership where our scientists and engineers co-develop proprietary models alongside your team — from initial hypothesis through production deployment.

Joint model development and full knowledge transfer

Custom algorithms built on your data and objectives

Structured engagement from discovery to production scale

Typical timeline: 3-6 monthsGet in touch

Risk-Free Experimentation

Simulator

A controlled experimentation environment purpose-built for programmatic advertising. Test strategies against realistic auction dynamics before a single dollar touches live traffic.

Realistic auction simulation with historical data replay

A/B scenario testing for pricing, pacing, and bidding logic

Quantified impact forecasting before production rollout

Typical timeline: 2-4 weeksGet in touch

From the Lab