How Eagle Eyed Digital Merges SERP and LLM Insights into One Workflow

As clicks and impressions declined, clients grew concerned about the effect of their SEO efforts. The shift toward AI-generated answers raised new questions about brand visibility outside SERPs. Thus, to keep delivering valuable insights to clients, Eagle Eyed Digital needed a reliable way to measure how often and in what context clients’ brands appeared in LLM answers.

The Solution: Integrating AccuLLM

Having relied on AccuRanker’s speed, accuracy, and granular SERP insights for nearly a decade, Eagle Eyed Digital’s team immediately recognised AccuLLM’s potential to fill the measurement gap.

By combining keyword rank tracking with LLM data, they created a hybrid model that integrates both SERP and AI visibility. The new process employs AccuRanker’s keyword tracking and SERP analysis tools as the foundation for every step. Here is how it looks:

1. Keyword research and intent mapping

Eagle Eyed Digital starts the process by conducting keyword research based on search volume and intent, separating commercial and informational queries. This helps the team answer the key question their clients ask: “How do we know people are actually asking these questions?”

The keyword data provides insight into market demand, showing what users are searching for and the intent behind those queries. After mapping the data, they review the results with their clients to ensure alignment between audience demand and the client’s business focus.

2. SERP-level insights

When the clients have approved the keywords, Eagle Eyed Digital tracks them in AccuRanker. They use AccuRanker’s SERP snapshots to analyse each term’s SERP, including People Also Ask questions, top-ranking landing pages, supporting content on the high-performing sites, and the overall SERP intent.

The SERP analysis helps them identify content trends, common angles, and opportunities where their clients can appear in both traditional search results and AI-generated responses.

3. LLM question modelling

Using the insights from the SERP-level analysis, Eagle Eyed Digital uses ChatGPT, Gemini and other LLMs to identify the natural-language prompts behind the keywords. They sense-check the prompts against AccuLLM’s prompt suggestions to ensure that the prompts reflect real search behaviour.

4. Prompt categorisation

They group the prompts into topic clusters based on related intents, mirroring keyword categorisation, and then track them in AccuLLM’s dashboard. This categorisation makes it easier to track visibility, monitor trends, and align content planning around the most commercially relevant topics.

The agency shares the prompts and clusters with clients to confirm they match the types of questions their customers are actually asking. This collaborative process ensures that both SEO and LLM optimisation efforts are grounded in actual search demand.

5. Upload and reporting setup in AccuRanker

AccuRanker’s import workflow enables the agency to quickly upload the prompts and align them with existing keyword groups. Thus, they can easily label prompts by theme, funnel stage, or brand mention and configure reporting to include SERP and LLM visibility.

6. LLM tracking and trend analysis

AccuLLM allows Eagle Eyed Digital to monitor brand mentions, content sentiment, and visibility trends across LLMs and prompts. Combined with traditional SERP data, the agency now has a complete view of organic visibility across search engines and AI interfaces.

This new process, built around AccuRanker’s platform, enables Eagle Eyed Digital’s team to quickly scale LLM tracking across multiple clients without additional engineering, ensuring that every dataset follows the same tagging and reporting structure.

The Results: SERP and AI-Based Reporting Frameworks

Integrating keyword and LLM insights has transformed how Eagle Eyed Digital reports and strategises for its clients. AccuRanker’s keyword and LLM data allow the team to configure its reporting framework to visualise AI vs. SERP visibility side by side. Thus, they can now:

• Demonstrate visibility where traffic metrics fall short, showing brand presence in AI-generated answers even when clicks decline.
• Diagnose performance changes faster by separating actual ranking movements from SERP layout changes or inclusion in AI answers.
• Optimise for AI visibility by identifying content formats, structures, and phrasing that increase LLM inclusion and credibility.
• Educate clients on the future of search, show them where their brand appears within LLMs, and help them adapt early.

According to the co-founders, every client they have onboarded since introducing this model has been genuinely impressed by the data and the clarity it brings. They no longer just say, “You’re ranking well.” They can now also show where and how LLMs surface a client’s brand with a clear view of the brand’s sentiment, rank, and top-cited landing pages.

Clients have expressed that this approach makes them feel more confident about visibility. Even as informational traffic shifts, they can clearly see that leads and sales remain consistent, which proves that visibility in AI results supports business outcomes.