AI Software Suite - Rachael Hall Creative I Content Strategist I Copywriter

Case Study: SmartTensor Software Suite

Client: Los Alamos National Laboratory
Role: SEO/UX Strategist · Content Architect · Technical Copywriter

Tools: SEMrush, Google Analytics, Google Console, Site Improve, Figma CRM

The Client

Los Alamos National Laboratory is a global leader in advanced AI research supporting U.S. energy innovation and national security. In 2022, the team received an R&D Award for the SmartTensor software suite—a breakthrough platform that applies neural networks to discover, analyze, and organize complex data.

Project Background

When the SmartTensor software suite launched, its capabilities were largely understood only by researchers closest to the work. I worked from limited inputs to identify the software’s core capabilities and translate them into a clear, structured page – discoverable for stakeholders beyond the immediate research team.

About the Software Suite

These tools are available as open-source downloads on GitHub, allowing developers to integrate them into custom, branded software solutions that transform raw data into actionable insights.
Developers can leverage the code to build robust software and technical stacks under bespoke brands.
While AI tools are now commonplace in many workplaces, this research was pioneering in advancing AI-driven data analysis and applications of large language models (LLMs).

Rachael Hall Creative Senior Copywriter (2)

Top 3 Challenges
1. No Clear Inputs or SME Brief
When I inherited this project, there were no defined lists of software capabilities, keywords, or messaging. I independently researched to learn “what the tools did” by digging through technical documentation, research papers, and fragmented supporting materials. I then translate that understanding into clear, searchable language.

2. Extreme Content Fragmentation
The original content was sparse, jargon-heavy, and scattered across notes, journals, and multimedia fragments. It was challenging to synthesize incomplete inputs into a cohesive narrative that worked for both technical and non-technical audiences.

3. CMS Constraints During Early Development
I was building this case study while the new CMS was still in development, which meant I had access to a limited set of components. To the best of my ability, I designed a usable, scannable page architecture using basic tools—without the benefit of reusable templates, advanced layouts, or rich media components.

Approach

I began by identifying the software’s unique features and benefits, starting from first principles rather than a predefined list.
Once the capabilities were clear, I used SEMrush to conduct competitive and keyword research aligned to AI and machine learning terminology, ensuring the language reflected how both researchers and searchers would discover the work.

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Keyword Strategy

I designed the keyword strategy to reflect real search behavior across internal, external, and developer audiences. Each term was selected to reinforce discoverability while accurately representing how the software is used and evaluated in practice.

Optimized Copy

I focused on optimizing the brand name, SmartTensors, since both internal teams and external stakeholders consistently search for the software by name across the internal network and the public web.

Because the individual tools are available for download and reuse via GitHub, I also incorporated broader, descriptive terms such as data analysis software and available on GitHub to support discovery by developers and technical evaluators exploring implementation options.

To reflect real-world use cases, I aligned headlines and section titles with terms like big data analysis and enterprise data analysis, ensuring the language spoke to both technical capability and business context.

Finally, I reinforced visibility and accuracy by integrating core feature-level terminology throughout the page—including Natural Language Processing (NLP), Computer Vision, Large Language Models, tensors, neural networks, and anomaly detection. This approach preserved scientific credibility while aligning the content with how users and search systems understand and surface advanced AI capabilities.

Highlight Award Winning Software Features

There was no clear feature list or product narrative, so I had to analyze research materials and technical documentation to identify the capabilities that would resonate with end users. Once those features were defined, I structured and presented them in a way that made complex, award-winning research understandable, scannable, and discoverable.

UX Strategy

Lead with user benefits
Isolate meaningful features
Use visuals to support scanning
Reinforce meaning with metadata
Align language with search intent

Balancing UX and SEO

I used accordion components to separate and highlight key features and keyword topics, allowing users to scan complex information without being overwhelmed by long-form copy. This approach improved readability while keeping advanced technical detail accessible when needed.

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Outcome: Generative AI Search Visibility

After the page was published, the content began appearing in generative AI search results. When searching “What is a neural network LANL?”, the AI-generated response surfaced and cited information directly from this page.

Although the case study is not exclusively focused on neural networks, adding LANL clarified intent and demonstrated how structured content helps AI systems retrieve the correct context. The generated response mirrors language and definitions used in the accordion sections above—showing that clearly structured topics and feature descriptions can influence how AI search tools interpret, synthesize, and cite content.

Outcome
- Produced a fully structured, discoverable case study explaining what SmartTensor does, how it works, and why it matters
- SmartTensor analyzes decades of unstructured data using tensor-based AI across siloed datasets—delivering insights in hours instead of months
- Keywords and technical terms were optimized for search while maintaining credibility (e.g., NLP, Computer Vision, Tensors, Neural Networks, Explainable AI, Data Filtering & Compression)
- Improved internal and external discoverability, created a reusable content foundation, and preserved critical research insights for future marketing

Key Takeaways

UX and SEO are translation layers between complex systems and real-world users. By combining research, strategy, and content architecture, I turned incomplete, high-friction material into a durable, high-performing, AI-friendly asset.