Build a Cross-Engine Citation Audit with LUMOS Multi-Agent Orchestration

Why Single-Engine GEO Optimization Fails in a Multi-Engine World Most enterprise GEO strategies treat generative engines as interchangeable. Optimize for ChatGPT, and you expect the same citation behavior from Perplexity and Gemini. That assumption costs you visibility. Each engine has distinct retrieval algorithms, source preferences, and citation formatting. ChatGPT often prioritizes conversational authority from high-DR publications. Perplexity favors real-time, structured sources like Wikipedia and official documentation. Gemini leans on Google’s index, weighting freshness and entity relationships differently. Manual auditing across three engines is slow and error-prone. Content teams can’t query every keyword daily per engine. The result: you miss where your brand is cited, misattribute gaps to content quality when it’s really an engine-specific bias, and waste budget on generic fix

es that don’t move the needle on every platform. You need an automated, systematic way to compare citation inclusion, source attribution, and answer completeness across ChatGPT, Perplexity, and Gemini simultaneously. Introducing LUMOS Multi-Agent Orchestration for GEO Audits LUMOS is a multi-agent orchestration framework designed to coordinate specialized AI agents that each perform a distinct subtask. For cross-engine citation auditing, you deploy three query agents—one per engine—that execute identical keyword prompts in parallel. A fourth analysis agent collects the responses, parses citation metadata, and produces a structured comparison table. A fifth dashboard agent ingests that data into a persistent tracker. This architecture eliminates manual round-trips and ensures apples-to-apples comparisons. You control the exact prompt sent to each engine, so differences in output reflect e

ngine behavior, not query variation. The framework is technology-agnostic: you can implement LUMOS using Python + LangChain or a dedicated no-code agent builder, as long as it supports parallel API calls and JSON output parsing. Step 1: Define Your Agent Roles and Keyword Targets Start by defining three query agents: one for ChatGPT (using GPT-4o or GPT-4 API), one for Perplexity (using the Perplexity API or a proxy that returns answer snippets with citations), and one for Gemini (using the Gemini API, specifying the model, e.g., ). Each agent receives the same and parameters (temperature=0.0, max tokens=500) to minimize randomness. Select a set of 10–20 seed keywords relevant to your brand, ideally ones with high commercial intent (e.g., "best enterprise data extraction tool" for a B2B SaaS). Include both branded and unbranded terms. Create a spreadsheet mapping each keyword to the agen

t outputs you expect. Example agent role definitions: - Agent ChatGPT : queries chat completions endpoint, extracts the main answer body, identifies any inline citations (URLs or source names). - Agent Perplexity : queries Perplexity’s answer endpoint, extracts the synthesized answer and the list of cited sources (typically displayed after the answer). - Agent Gemini : queries Gemini’s generateContent endpoint, extracts the text and any grounding metadata (if available). Define a standard output schema for each agent: . Step 2: Build the Query-and-Collect Pipeline Use LUMOS’s orchestration layer to dispatch agents concurrently. The orchestrator iterates over your keyword list, creates a prompt for each keyword, and sends it to all three agents simultaneously. Collect the responses as they complete (asynchronous handling). Store each response in a central database or data lake. A simple a

pproach: use a SQLite database with a table containing columns: , , , , , , , . Implement retry logic with exponential backoff for rate limits. Monitor for empty responses or errors and log them separately. The pipeline should run on a schedule—weekly is sufficient for most enterprises. Step 3: Compare Citation Inclusion, Source Attribution, and Answer Completeness Once you have collected results for a batch, the analysis agent (Agent Analyzer) reads the database and produces a comparative report. For each keyword, it calculates: - Citation presence : Does each engine cite any source? How many? - Brand inclusion : Is your brand name mentioned in the answer? In the citations? - Source attribution quality : Do the cited sources link back to your domain? Are they authoritative (.gov, .edu, industry recognized)? - Answer completeness : Does the answer fully address the query intent? Use a si

mple rubric: missing key points, partially covered, fully covered. Create a matrix table like this: Keyword ChatGPT citation count ChatGPT brand mentioned Perplexity citation count Perplexity brand mentioned Gemini citation count Gemini brand mentioned --------- ------------------------ ------------