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Helping AI buyers find their next step

Audience research, an LLM self-assessment, and connected content helped explain a broader AI offering.

Client
Turing
My role
Content strategist
Work
Research-led content program

The starting point

The challenge

Turing was known for some of its solutions but wanted to tell a broader story as it rebranded for an evolving AI market. The content needed to speak to buyers with different needs and levels of readiness.

The decisions

My approach

The strategy used audience and market research to understand the questions behind buying decisions, including pain points, unmet needs, and emerging priorities.

An LLM self-assessment gave businesses a way to identify where model improvements or new applications could support their goals.

The research informed white papers and a wider collection of blogs, videos, and infographics. Derivative assets carried the same ideas into email, social, ads, and landing pages.

From plan to published

The execution

The program connected research-based resources with a self-assessment and follow-up content for different stages of LLM adoption. Smaller visual and written assets helped the larger ideas travel across channels.

Explore the work

The strategy in action

Selected pieces from the program. My role was content strategist; individual publication credits are noted below.

White paper

Maximizing Your LLM ROI

A guide to the role of strategy, evaluation, and training in LLM performance.

View cover + one sample page Collapse preview : Maximizing Your LLM ROI
Maximizing Your LLM ROI — cover, part 1
Cover
Maximizing Your LLM ROI — selected sample page, part 1
Selected sample page

Selected excerpt · cover + one sample page

Video

Elevate your LLM

A short video connects LLM evaluation and improvement to the assessment.

What followed

The outcome

The work produced a connected set of educational and campaign assets, with an assessment that helps visitors identify a relevant next step.