# AI Orchestration platforms that track brand citations across ChatGPT, Perplexity and multiple LLMs

<p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true">Core AI orchestration platforms aren't built to track brand citations across ChatGPT, Perplexity, and multiple LLMs out of the box. What orchestration platforms can actually offer here is the scheduling and multi-step structure to query several LLMs on a recurring basis and assemble the results yourself, rather than a pre-made dashboard. If your team has actually built a multi-LLM citation-tracking pipeline on one of these, how are you handling the differences in each LLM's API/output format, and has the maintenance stayed manageable as you've added more models to track?</p><p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true">Looking across G2's <a class="a a--md" elv="true" href="https://www.g2.com/categories/ai-orchestration"><strong>AI Orchestration</strong></a> category, the platforms best suited to building a recurring, multi-LLM query pipeline are LangChain, Prefect, and Apache Airflow.</p><p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true">Here's a closer look at four worth comparing:</p><ol>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/langchain/reviews"><strong>LangChain</strong></a>: A developer-centered framework that queries ChatGPT, Perplexity, Gemini, and Claude with the same prompt set and compares outputs.</li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/apache-airflow/reviews"><strong>Apache Airflow</strong></a> (4.4/5, 120+ reviews): Reviewers praise its scheduling and dependency management for exactly this kind of recurring, multi-step job, which queries each LLM, stores results, and makes alerts on changes.</li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/astro-by-astronomer/reviews"><strong>Astro by Astronomer</strong></a> (4.5/5, 120+ reviews): Reviewers write that it's useful if you want a multi-LLM tracking workflow running without a team owning the infrastructure.</li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/workato/reviews"><strong>Workato</strong></a> (4.7/5, 20+ reviews): This software uses APIs on a schedule and sends results to a dashboard or creates an alert.</li>
</ol><p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true"></p>

##### Post Metadata
- Posted at: about 2 months ago
- Author title: Research Analyst
- Net upvotes: 2


## Comments
### Comment 1

&lt;p&gt;The biggest challenge seems to be normalizing the results, since each model handles citations, sources, and formatting differently. I’d use a consistent prompt set and store the model, response, cited domain, citation position, and date in a standard schema. Tracking prompt and model-version changes would also be important so shifts in citation share are not mistaken for genuine brand-performance changes.&lt;/p&gt;

##### Comment Metadata
- Posted at: about 1 month ago
- Author title: Team Lead





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