MarketingMind: A Multi-Agent LLM Architecture for Federated Natural Language Querying Across Enterprise Marketing Data Systems (International Journal of Engineering & Extended Technologies Research (IJEETR), 2025)
In enterprise marketing environments, there are diverse data ecosystems that encompass customer relationship management (CRM) applications, marketing automation tools, advertising networks, and enterprise data warehouses. One major remaining challenge is the capability to communicate with such systems using natural language interfaces, which is made difficult by the schema heterogeneities, data governance requirements across systems, and the tendency of large language models (LLMs) to hallucinate. MarketingMind is proposed as a multi-agent LLM architecture to handle federated natural language queries in these systems. The framework combines a ReAct-based orchestrator [14] with six dedicated agents responsible for schema discovery, natural language to SQL (NL-to-SQL) translation, hallucination mitigation, federated query routing, CRM analytics augmentation and response synthesis. Retrieval-augmented generation (RAG) [3] is used at various points in the pipeline to anchor the generation of queries to fact-checked schema information. A validation agent that uses self-reflection mechanisms [5] obtains a hallucination rate of 7.3%, whereas the hallucination rate is 38.4% in unconstrained LLM settings. The execution accuracy of the proposed architecture in the Spider benchmark is 91.3%, which is close to the human expert baseline of 94.5%. Federated queries are distributed across six enterprise data sources secured through OAuth 2.0 authentication [2] and column-level encryption, and cover 97.4% personally identifiable information (PII). The architecture is able to handle 138.2 queries per second (QPS) with 500 concurrent users, with a mean latency of 5.4 seconds. MarketingMind highlights that multi-agent orchestration paired with domain-specific RAG grounding and adaptive hallucination mitigation is a viable route towards enterprise-level natural language business intelligence for marketing analytics.
Keywords: multi-agent systems; large language models; federated learning; text-to-SQL; marketing analytics; hallucination mitigation; enterprise data integration; natural language interfaces; CRM intelligence; ReAct framework; OAuth 2.0