#84: Quo Vadis Green Shoot AdTech Interviews
With Pat LaCroix, EVP, Strategic Partnerships, Seekr
Welcome back to Quo Vadis Green Shoot AdTech Interviews.
In this series, we step into the shoes of “AdTech Equalizers” — the green shoot companies changing how the advertising job gets done. We delve into their worldview and unveil new strategies, tools, and thinking that make advertisers (and investors) better off.
Today we are speaking with Pat LaCroix, EVP of Strategic Partnerships from Seekr. Seekr is an artificial intelligence company building trustworthy large language models (LLMs) that identify, score, and generate reliable content at scale.
Q1: What problem does Seekr AI solve?
A: Pat
Seekr develops large language models and foundation models that evaluate and generate reliable content at scale. Our models and tools are optimized for a wide variety of enterprise use cases, with an immediate focus on helping to save quality journalism
For advertisers and publishers, SeekrAlign uses patented contextual AI to help brands and publishers reach broad, brand-safe podcast and online audiences through accurate, unbiased, and transparent content evaluation.
Q2: Why is the SeekrAlign product important in today’s news environment?
A: Pat
Unfortunately, public trust in online news continues to erode due to factors such as misinformation, propaganda, and personal political leanings. Legitimate news outlets see engagement decrease and thus suffer a financial impact from the growing skepticism in news. This is compounded by the fact that advertisers, concerned with brand safety, decrease their spending on news as they don’t want to get caught in the crosshairs. The result? Publishers invest less in quality journalism, people are less informed and advertising dollars end up flowing to low or no-impact media
Q3: How does SeekrAlign work and compare to other in-market brand suitability solutions?
A: Pat
Nearly all competing brand suitability and contextual tools use keyword-based systems. Most of the tools misrepresent or over-penalize content due to the simple discussion of certain topics. For example, we recently reviewed a sample of 20,000 podcasts and found that even though these words were used as a personal attack 3% or less of the time, 97% of occurrences where words like “shoot”, “debate”, or “gay” are present in episodes, keyword-based tools inaccurately apply a risk metric when none is present. You can read more here.
The unintended consequence of this reality is that more ad dollars flow to lower-quality media. While well-intentioned, today’s brand suitability offerings are black boxes that restrict reach for advertisers and revenue for publishers. When you rely on keyword-based systems, you regularly produce false positives and negatives, because you are not evaluating the context of how the language was used. To evaluate brand suitability, it is not just the letters of the words, but also the context and spirit of the conversation that really matters.
With SeekrAlign, advertisers can improve their performance and increase reach while upholding their brand values. The Seekr Civility Score™ is a patented feature that tracks personal attacks, including those that may not register as hate speech under GARM but are still hostile in nature.
The Civility Score ranges from a value of 100 to 0 and is categorized into four labels: High, Medium, Low, and No Civility. Unlike a linear algorithm that follows a strict set of rules regardless of circumstance, this scoring system factors in context, including nuanced conversations that can often occur within podcasts. With traditional brand safety tools, legitimate and responsible journalistic-based programs could be blocked due to topics discussed—war, sensitive social issues, harmful acts—even though their conversations are civil, respectful, and informative.
Q4: How does AI power your solutions?
A: Pat
AI enables us to review entire podcast transcripts on an episode level. To date, we’ve analyzed over 40,000 podcasts for brand suitability. Importantly, where alternative solutions hide their data behind APIs and unmarked transcripts—leaving you wondering how much you are being wrongly penalized—Seekr offers the opportunity to review our scores and flag inaccuracies to improve the product over time.
However, context and quantity curb AI’s ability to solve the brand suitability issue alone. That’s why Seekr applies principles and standards to our LLMs with humans involved in the process. When companies forge ahead without a human-in-the-loop approach, they often do so by sacrificing the checks and balances needed to understand the context and subtleties of naturally occurring human language – making it harder to distinguish right from wrong.
Q5: You recently announced a collaboration with Intel. Tell us about it and how it will help you with publishers and advertisers.
A: Pat
In April, we announced a partnership with Intel. The collaboration allows Seekr to leverage the Intel® Developer Cloud to build, train, and deploy advanced LLMs in a cost-effective way. Within that announcement, we also launched SeekrFlow, an end-to-end LLM development toolset that allows developers to train and build LLMs using scalable and composable workflows.
For advertisers and publishers, our custom LLMs are piped into the user-friendly Seekr Align platform, which provides fast and accurate evaluations of digital content. When leveraged together, Seekr’s AI technology enables advertisers to find brand-safe environments to advertise in podcasts and digital content that focuses on news and politics. Our solution is dedicated to increasing reach and revenue for advertisers and publishers.
Resource Link: If you’d like to learn more about Seekrs partnership with Intel Developer Cloud, you can check it out here.
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Disclaimer: This post, and any other post from Quo Vadis, should not be considered investment advice. This content is for informational purposes only. You should not construe this information, or any other material from Quo Vadis, as investment, financial, or any other form of advice.