Grigoriy MisilyukMachine learning isn’t a trend—it’s a requirement. With over ten years of experience developing programmatic technologies, we state that the primary goal of ML optimization is to maximize outcomes from each specific impression automatically. It's essential since the industry suffers from decreased demand opportunities and supply shortages.
The actual ML layer of the white-label supply-side platform includes a set of tools and features for intelligent trading. These power-ups work seamlessly and in tandem, grounded on the A/B testing framework for safe scaling—that's why we call our sell-side platform “ML-driven.”
The role of machine learning at TeqBlaze
Partners that chose to launch their platforms with TeqBlaze requested ML in different ways. The reason is messing with marketing tricks.
Some SSPs on the market measure the effectiveness of optimization measures through fill rate or bid rate metrics. It’s okay, but not our top choice. We evaluate our ML layer based on financial results — this is a winning strategy because money in the account cannot lie.
So, we strive to optimize trading by growing the effective CPM (eCPM) metric.
The journey of one impression through our ML layer
To address the optimization challenge effectively, we have successfully empowered our programmatic platform technology with dynamic optimization algorithms that leverage both historical and real-time data—Traffic Shaping and WinRate Optimizer. These tools are part of a strategic concept of supply-path optimization (SPO), including the SPO toolkit for further improving traffic delivery to DSPs, and other native ML-driven features.
The goal at every stage is to increase the value ($) of each impression by more accurately aligning inventory with buyer demand, improving win rates, applying strategic prioritization, and lowering operating costs for the platform.
It’s a shift from chasing redundant scale to achieving real efficiency—from irrelevant bids to high-quality conversions—that’s the role of the machine learning layer at TeqBlaze.
Traffic Shaping Tool: intelligent request distribution
Every bid request carries weight—some more than others. The Traffic Shaping Tool utilizes machine learning to ensure that your platform doesn’t send irrelevant bid requests and that the right buyers get the proper inventory at precisely the right time.
The traffic shaping process analyzes patterns of historical and real-time data to predict which partners are most likely to engage and generate profit—and adjusts bid requests accordingly. It is also based on real CPM, not vanity metrics. You can launch the Traffic Shaping Tool by switching one toggle. We have also ensured that scaling optimization is safe—the A/B testing framework allows you to gradually, percent by percent, cover all traffic volumes.
ML continuously evaluates partner performance across various inventory types, time windows, and user profiles. It learns who values what and shapes the traffic optimally.
Reduces wasteful QPS (queries per second)
Prevents partner overload with irrelevant requests
Protects infrastructure from inefficient scale
eCPM ↑ due to smarter, buyer-specific traffic distribution
More precise delivery = fewer missed opportunities + more engaged bidders. This results in higher match rates, better buyer satisfaction, and more dollars per impression.
Traffic Shaping: a real-life example
This is a story of how, with one toggle switched, profits increased by an average of $2,700 per day, server load was reduced, and eCPM nearly doubled. The graph shows a clear improvement after the 20th—that wasn't when traffic shaping was first activated, but when we scaled it to 100% of traffic, up from just 10% tested. The optimization started working in full swing.
Here's what's essential: sometimes the RCPM increased because server load dropped while CPM remained steady. In other cases, the CPM rose while the system load remained constant. And occasionally, both improved together—an ideal scenario.
There's no single formula, but the result is consistent: more value per impression and smarter use of system resources. That's the power of intelligent traffic shaping driven by ML—it adapts to the dynamics of your traffic and infrastructure to find the most profitable outcome.
WinRate Optimizer: predicting auction success
Every buyer has a price they're willing to pay—and a specific type of inventory they actually tend to buy. The WinRate Optimizer uses machine learning to identify those winning patterns and directs inventory accordingly.
Eliminates unnecessary bid requests that don't convert
Increases delivery to high-probability, high-value paths
Cuts "auction noise" (unanswered or lost bid calls)
eCPM ↑ via sharper win targeting and reduced system computing
The tool predicts the probability of auction success for each impression-buyer pair based on bid history, deal structure, creative acceptance, and real-time bidding behavior. The optimizer focuses requests on the partners most likely to win, growing your profits and reducing server load.
SPO Toolkit: faceting the supply path
In the ad tech ecosystem, not all supply paths are equal. The SPO Toolkit equips you with a powerful analyzer and a set of manual tools that help identify, favor, and prioritize the most efficient paths. This isn’t a one-click automation but a strategic framework that enables platform owners to reduce waste and maximize value through clear, rules-based optimization.
Avoids overlong or redundant supply chains
Maximizes delivery through trusted, high-conversion routes
Lowers hidden fees and infrastructure costs
eCPM ↑ by removing supply chain friction and boosting clarity
We see the SPO toolkit not as a plug-and-play feature, but as a customizable layer for intelligent decision-making. It complements automation and gives ad platforms human-level control to shape trading logic based on transparency, profitability, and path reliability.
And for those who want full automation, tools like SmartFloor do precisely that. SmartFloor is our ML-powered engine that dynamically adjusts bid floors per impression, maximizing revenue by predicting the actual value of traffic in real time.
Together, these tools reflect the broader intelligence embedded in our SSP+ ad exchange: a blend of automation and control, where every path and price is optimized with precision.
Conclusion: the future of programmatic is intelligent
In today's competitive landscape, machine learning is a baseline expectation, especially on the SSP side, where intelligent infrastructure is a requirement for survival.
At TeqBlaze, we've built a platform where optimization is continuous, automatic, and based on your actual data. It works in the background to improve every single impression's value, adapting in real time to your traffic, your partners, and your goals. This is beneficial for publishers and platform owners, and demand partners win, too. Why? Because they receive better-matched inventory, more accurate targeting, and reduced server strain. When all sides win, your trading thrives.
Want to see how our ML layer can boost your platform’s efficiency and profits? Get in touch for a free consultation. The future of programmatic is smart bidding, adaptive logic, and measurable performance. It’s what we do at TeqBlaze—intelligently and transparently, focusing on tangible financial outcomes. Be part of it!
