Audience intelligence teams can spend significant time turning a campaign brief into the precise variables, exclusions and Boolean logic needed for analysis. TelmarHelixa is attempting to shorten that process with Stanley, an AI-powered Query Builder within its Discover platform that converts natural-language audience objectives into executable audience strategies in under 30 seconds.

TelmarHelixa Brings Generative AI to Audience Query Building

The hardest part of audience analysis is not always interpreting the final numbers. Often, it is deciding exactly which audience should be measured in the first place.

That challenge becomes more complicated as research platforms accumulate increasingly detailed taxonomies, demographic variables, behavioral signals and points of interest. Analysts may know what a client wants to understand but still have to translate that objective into the precise combinations of variables and exclusions required to build a usable audience.

TelmarHelixa’s new Stanley feature is designed to automate that translation.

Built into the company’s Discover platform, Stanley uses AI to interpret a natural-language audience brief and turn it into five different query strategies. The company says the system can analyze more than 200,000 catalogue items and their underlying taxonomy in less than 30 seconds before identifying relevant variables, relationships and exclusions.

The result is intended to be more than an AI-generated audience description. Stanley produces executable Boolean logic, estimated audience sizes and explanations for why particular variables were included or excluded.

That distinction matters for advertising and media teams. AI-generated recommendations are only useful when analysts can understand how the audience was constructed and decide whether it accurately reflects the original brief.

From Audience Brief to Executable Logic

Traditional audience definition often requires analysts to navigate a data provider’s taxonomy manually.

A marketer might begin with a relatively straightforward objective—such as identifying consumers interested in a particular category—but the analytical path can quickly become complicated. The relevant audience may be distributed across demographic characteristics, interests, behaviors, lifestyle indicators and other catalogue attributes.

The analyst then has to determine which combinations best represent the brief without making the resulting audience either too broad or unnecessarily restrictive.

Stanley is designed to compress that workflow.

Rather than returning one interpretation, it generates five audience strategies, ranging from broad definitions to more tightly focused alternatives. Each strategy contains Boolean query logic and an explanation of the analytical reasoning behind its construction.

For audience analysts, the value may therefore lie less in eliminating human judgment than in moving that judgment further up the workflow.

Instead of manually finding every possible variable, analysts can compare several machine-generated routes and determine which one best represents the business question.

Why Multiple Audience Definitions Matter

Audience targeting is rarely a binary exercise.

There can be several defensible ways to define the same consumer group, each producing a different audience size and potentially different downstream insights. A broad audience may offer scale but sacrifice specificity. A highly focused audience can provide greater precision while reducing reach.

Stanley’s five-strategy approach makes those trade-offs more explicit.

That could be particularly useful for agencies working across different campaign objectives. A planning team might want one definition optimized for scale, another focused on behavioral characteristics and a third designed around a narrower set of purchase-related signals.

The ability to see these options before committing to one audience definition can also reduce a less visible problem: analytical path dependency.

If an analyst misses an important variable at the beginning of the process, the mistake can propagate through audience sizing, campaign planning, measurement and strategic recommendations. By analyzing the taxonomy and relationships behind the catalogue, TelmarHelixa says Stanley is intended to surface connections that manual navigation might miss.

AI Is Becoming a Layer Between Briefs and Data

Stanley fits into a wider evolution in advertising technology: the movement of AI from content generation toward data interaction and workflow automation.

Platforms from Google, Amazon, Adobe, Salesforce and other major technology providers are increasingly using natural-language interfaces to make complex data and marketing systems more accessible. But audience intelligence presents a particular challenge because the system has to understand both the user’s intent and the structure of the underlying data.

That makes taxonomy-aware AI more significant than a generic chatbot interface.

An effective audience query builder needs to understand relationships between variables, recognize when exclusions are necessary and translate a strategic objective into logic that a research platform can actually execute.

The distinction is important as advertisers contend with increasingly fragmented consumer behavior across search, social platforms, retail media, streaming services and other digital channels.

The Enterprise Case: More Capacity Without More Analysts

TelmarHelixa is also positioning Stanley as a way for insight teams to increase analytical capacity without expanding headcount.

That proposition is likely to resonate with agencies and media organizations managing large volumes of client briefs. Query construction is highly dependent on specialist knowledge, but parts of the process are repetitive enough to be candidates for automation.

The more useful enterprise model may therefore be AI-assisted analysis rather than AI-replaced analysis.

Stanley does not remove the analyst from the workflow. Instead, the analyst receives multiple possible definitions, audience sizes and the reasoning behind each option, then evaluates those outputs against the client’s objectives.

That approach preserves an important element of advertising analytics: accountability.

When audience definitions influence targeting, media planning or campaign measurement, teams need to know why a particular audience was selected. An opaque AI recommendation may be fast, but it is difficult to defend. By exposing the variables, exclusions and Boolean logic behind its suggestions, Stanley is designed to keep the analytical process inspectable.

What It Means for Agencies, Advertisers and Media Owners

The immediate beneficiaries are likely to be teams that repeatedly translate campaign objectives into detailed audience definitions.

Agencies could use Stanley to explore more strategic routes from the same brief. Advertisers could use alternative audience definitions to assess the trade-off between scale and precision before campaign decisions are made. Media owners could potentially use the technology to translate advertiser objectives into audience opportunities more quickly.

The broader implication is that AI may increasingly sit between the language of marketing strategy and the technical structure of advertising data.

That is a meaningful change.

For years, the barrier between a marketer’s brief and an executable query has been partly a human expertise problem. As AI becomes better at understanding taxonomies and translating intent into structured logic, that barrier could become less about query construction and more about deciding which strategic interpretation is actually worth pursuing.

Stanley does not eliminate the complexity of audience intelligence. It attempts to make that complexity easier to navigate—while leaving the final strategic decision with the people responsible for the outcome.

Market Landscape

Audience targeting is becoming more complicated as advertisers work across fragmented channels, privacy-sensitive data environments and increasingly specialized media ecosystems.

The broader market is also moving toward AI-assisted decision-making. Gartner forecasts that by 2028, 90% of marketing analytics workflows will be augmented by generative AI, illustrating how quickly natural-language interfaces and AI assistance are becoming part of analytical operations.

The challenge is that audience analytics requires more than generating text. Systems need to interpret taxonomies, understand relationships between data points and produce outputs that can be executed and audited.

That makes tools such as Stanley part of a wider shift toward AI-native audience intelligence. The competitive question will increasingly be whether platforms can accelerate analysis while maintaining transparency, data governance and human oversight.

For enterprise advertising teams, the practical benefit is not necessarily replacing audience strategists. It is reducing the amount of low-value query construction and data navigation they have to perform before strategic analysis can begin.

Top Insights

• TelmarHelixa’s Stanley converts natural-language audience objectives into five executable strategies, helping agencies and advertisers explore targeting options faster.
• The AI analyzes more than 200,000 catalogue items and taxonomy relationships, potentially surfacing variables and exclusions manual audience research could miss.
• Multiple audience definitions expose trade-offs between reach and precision, giving analysts more evidence before selecting targeting strategies for campaigns.
• Executable Boolean logic and transparent reasoning keep specialists involved, positioning Stanley as an AI-assisted workflow rather than a replacement for audience expertise.
• For lean insight teams, automated query construction could increase analytical capacity while allowing specialists to focus on interpretation, strategy and client recommendations.

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