App Orchid introduced its Conversational Analytics Agent for Easy Answers, which lets users and agents ask questions in natural language and instantly receive insights and analytics.
“Most AI tools can answer questions. Very few can get high-accuracy answers consistently,” says Ravi Bommakanti, CTO of App Orchid. “Our Conversational Analytics Agent doesn’t just retrieve data, it interprets intent, applies context, and uses machine learning models to produce answers that are explainable, traceable, and actionable.”
Key takeaways:
· The new Easy Answers Agent leverages the core App Orchid semantic knowledge graph capabilities to understand both the meaning and the context of natural language questions. The Agent queries structured and unstructured enterprise data, presenting accurate, traceable, and explainable answers in the right visual format, whether that be a summary, graph, chart, or table.
· The Agent’s machine learning (ML)–driven insights uncover patterns, trends, and other important information from your enterprise data.
· By merging conversational intelligence with continuous large language model (LLM)-driven semantic enrichment, the new Easy Answers Agent learns from every interaction to deliver smarter, explainable, and more trustworthy analytics.
· The Conversational Analytics Agent for Easy Answers is a dialogue-driven interface that understands enterprise context, validates terminology, and translates natural language into Semantic SQL that “speaks” an organization’s domain language.
· The heart of the conversational Agent is App Orchid’s Semantic Knowledge Graph, which maps enterprise data using a graph ontology and enriches it with context, relationships, and metadata. Starting with this release, ontology discovery is enhanced with continuous LLM-driven semantic enrichment. Using AI-based discovery and auto-generated metadata, the platform continuously adds new business concepts, synonyms, and relationships to the ontology. It also builds “memory,” retaining context and prior questions to streamline future interactions and shift recurring work to agents.
· The system automatically creates the optimal visualizations (maps, charts, tables) based on user questions and data characteristics, promoting self-service analytics and supporting both dynamic answers and building permanent dashboards for consistent reporting needs.
· Expanded analytical insights now cover time-series, correlation, causation, regression, and other statistical methods, each powered by multiple machine learning models. With agentic discovery, the system can recommend the most relevant analyses and guide users toward deeper exploration automatically.
