Ask questions in plain language and let Lumenore Ask Me bring together governed business context, specialized analytical agents, advanced analytics, and external intelligence to help you understand what changed, why it changed, and what to consider next.
No SQL. No report request. No need to know which analytical method to run first.
Conversational analytics lets people explore business data by asking questions in natural language instead of writing queries or navigating a predefined report path. A modern conversational analytics experience should do more than translate text into a chart: it should understand business context, use the right analytical capability, preserve relevant conversational context, and return an answer users can evaluate and explore further.
Lumenore Ask Me combines natural-language querying with governed business context, specialized AI agents, advanced analytics, and flexible visual and narrative responses. The result is a faster path from a business question to deeper understanding—without requiring every follow-up to become a new report request.
Use familiar questions instead of SQL or dashboard-building steps.
Refine the question, compare segments, change the view, or investigate the next layer.
Receive charts, narratives, root-cause insights, trends, forecasts, and other supported analyses.
Dashboards are ideal for monitoring the metrics you already know matter: revenue, margin, churn, OEE, utilization, SLA, pipeline, inventory, and other repeatable KPIs. The challenge begins when a number moves and the next question was not designed into the dashboard.
The two experiences work together: monitor with dashboards; investigate with Ask Me when the business question changes.
Start with a business question in plain language.
Use schema definitions, business context, metadata, and access rules to interpret the question correctly.
Route the request to the relevant analytical capability—such as NLQ, root-cause analysis, visualization, or data science.
Return the result in the format that best fits the question, including narrative insights, charts, tables, or a combination.
Surface the key drivers, implications, and what the user may want to examine next.
When the analysis needs wider context, MCP-enabled Ask Me can also bring approved external market intelligence and connected services into the experience.
Ask Me uses specialized analytical agents behind a single conversational experience. The Master Agent interprets intent and routes the request to the capability best suited to the question, so business users do not need to choose an analytical technique before they begin.
Coordinates the request by interpreting intent and routing it to the appropriate specialized analytical capability.
Interprets natural-language questions and returns relevant, governed data results and visual responses.
Helps investigate the contributing dimensions and drivers behind changes in important metrics.
Selects or refines visual representations so the answer is easier to interpret.
Applies supported statistical and machine-learning techniques for trends, anomalies, forecasts, classifications, and related analysis.
You ask the business question. Ask Me guides the analytical path behind the scenes.
A performance issue may appear in a metric, but the explanation can live in a report, a customer comment, a document, or the market outside your organization. Ask Me is designed to expand the evidence available to the conversation instead of forcing users to analyze each source in isolation.
Analyze governed business data across connected databases, cloud platforms, and analytical schemas.
Use RAG-based analysis for documents and text-based business content such as PDFs, reports, and supporting files.
Agentic RAG extends the analytical experience to supported multimodal content such as documents, images, audio, and video.
Bring approved market trends and contextual external information into Ask Me when an internal metric needs a wider business explanation.
Receive narratives, charts, tables, or combined responses based on the question and the available evidence.
Enterprise data was usually designed for databases and dashboards—not for AI. Column names can be ambiguous, business terminology can vary by team, and the same metric can mean different things in different contexts. Lumenore helps organizations assess and improve the metadata and business context AI needs to interpret data more reliably.
The AI Readiness Score provides a 0–100 view of how prepared a schema is for AI-driven analysis and highlights the areas that can be improved.
Teams can review the score, identify missing context, and use AI-assisted improvements to strengthen readiness before scaling conversational analytics across the organization.
Ask Me is designed to operate on governed analytical context rather than treating enterprise data like an open-ended public chatbot. Business definitions, access rules, schema controls, and supporting context help keep the experience aligned with how the organization actually uses its data.
Use centrally managed definitions and semantic context so important terms are interpreted consistently.
Apply platform access controls so users analyze the information they are authorized to use.
Use schema prompts and controlled field visibility to reduce ambiguity and keep AI focused on relevant data.
Where supported, surface the schemas, documents, links, and source context used to support the response.
Use feedback and Train Ask Me capabilities to refine responses and improve contextual performance over time.
The goal is not to make AI sound confident. It is to give users enough context to understand, validate, and continue the analysis with confidence.
Ask Me Minibot brings conversational exploration directly into the dashboard experience. Users can monitor a KPI, ask a follow-up question in context, and continue investigating trends or anomalies without leaving the interface they are already using.
Investigate pipeline gaps, regional performance, operational variance, and cross-functional questions.
Example: “Which regions are driving the pipeline shortfall?”
Explore variance, margin drivers, cost centers, forecasts, and planning questions.
Example: “Why is gross margin below plan this month?”
Investigate territory performance, deal risk, rep conversion, and quota trends.
Example: “Which reps have falling conversion despite strong lead volume?”
Analyze campaign performance, channels, segments, and conversion quality.
Example: “Which campaign produced the highest qualified-lead conversion?”
Connect service KPIs with complaints, feedback, and other unstructured information.
Example: “What is driving the rise in escalations?”
Investigate stockouts, supplier delays, OEE variance, quality issues, demand, and operational exceptions.
Example: “Which factors contributed most to the increase in downtime?”
Explore utilization, readmission drivers, payer mix, service-line performance, and cohort-level variance.
Example: “Which cohort is showing higher utilization than expected?”
Define business context, descriptions, synonyms, schema prompts, units, and other metadata that help AI understand enterprise data.
Support deeper diagnostic and predictive analysis with trends, correlations, anomaly detection, forecasting, and other analytical techniques.
Monitor repeatable KPIs visually, then use Ask Me when the question becomes exploratory.
Connect, prepare, and govern analytics-ready data across enterprise sources; Ask Me can also work with supported external platforms and context.
Bring analytics and conversational experiences into the applications and workflows where users already work.
Operationalize insights through broader Lumenore decision workflows and low-code applications.
Explore business data in natural language, investigate root causes, forecast outcomes, and bring governed business context into the analytical conversation with Lumenore Ask Me.
Conversational analytics lets users explore business data by asking questions in natural language and receiving contextual analytical answers. It can reduce reliance on SQL and fixed report paths by allowing users to ask follow-up questions, compare segments, investigate root causes, and request supported predictive or visual analysis through a conversation.
Natural language query (NLQ) is the ability to translate a user’s question into a data query. Conversational analytics is broader: it adds contextual follow-up, analytical interpretation, visual or narrative responses, and—depending on the platform—specialized agents, advanced analytics, document analysis, and external context.
Dashboards are best for repeatable monitoring of known KPIs. Ask Me is best for changing questions: why a metric moved, which segment drove the change, how the result compares, or what to investigate next. Lumenore supports both experiences and also brings Ask Me into dashboards through Minibot.
No. Business users can ask questions in natural language. Ask Me handles the supported analytical execution behind the interface, while technical teams can still govern the underlying data, schemas, definitions, and access.
Yes. Ask Me supports contextual conversations so users can refine a result, compare another segment, change the visualization, or continue the investigation within the supported conversation context.
Ask Me includes specialized analytical capabilities for root-cause investigation and data-science-driven analysis such as trends, anomalies, forecasts, classifications, and other supported techniques.
Yes. Lumenore supports RAG-based analysis of unstructured content such as PDFs, documents, and reports. The platform also describes Agentic RAG capabilities for supported multimodal content including documents, images, audio, and video.
Model Context Protocol (MCP) is used to extend the conversational experience beyond a single internal data source. In Lumenore Ask Me, MCP supports approved external market intelligence, flexible responses, and connections to internal services or external tools where supported. This helps add wider context to analytical questions.
The AI Readiness Score is a 0–100 assessment of how well a schema is prepared for AI-driven analysis. It evaluates metadata and contextual elements such as business context, descriptions, schema prompts, synonyms, and units of measure, and helps teams identify where to improve AI readiness.
Lumenore combines governed analytical context, access controls, schema guidance, AI Readiness, and source visibility where supported. These capabilities help the AI interpret business data more consistently and give users more context for evaluating the result.