Conversational Business Intelligence: Ask Natural Language Questions to Live Data
Founders and operators spend over 11 hours every week digging through 40 conflicting charts in Looker, Tableau, and Excel without getting definitive answers. Forion and Orbit transform business intelligence from descriptive charts into a conversational decision partner that explains why metrics changed and recommends prescriptive action plans.
Quick Answer: What is Conversational Business Intelligence?
Conversational Business Intelligence allows business leaders to query complex relational databases using natural language. Grounded in a governed Semantic Metric Layer, the system compiles questions into parameter-safe Abstract Syntax Tree (AST) SQL queries, isolates causal drivers (e.g., cohort churn, CAC spikes), and provides actionable explanations without dashboard fatigue.
Core Technical Capabilities
Governed Semantic Metric Catalog
Defines canonical business metrics (LTV, CAC, net margin, churn rate) deterministically, preventing the language model from hallucinating conflicting formulas.
Parameter-Safe AST SQL Synthesis
Translates natural language questions into optimized, index-friendly SQL queries executed against encrypted read-replicas with strict execution timeouts.
Cross-Ledger Causal Decomposition
Synthesizes data across marketing spend, sales ledgers, inventory turn rates, and customer support tickets to pinpoint true operational root causes.
Prescriptive Action Recommendations
Goes beyond passive graphs to suggest prioritized next steps: price adjustments, ad set pauses, or inventory reallocations based on financial modeling.
export interface SemanticQueryIntent {
metric: 'net_margin' | 'mrr_churn' | 'ltv_to_cac'
timeWindow: string
groupByDimension?: string
}
export function compileSemanticSql(intent: SemanticQueryIntent) {
// Deterministic translation ensuring canonical business metrics
const metricMap = {
net_margin: 'SUM(revenue - cogs - fulfillment_cost) * 100.0 / NULLIF(SUM(revenue), 0)',
mrr_churn: 'SUM(churned_mrr) * 100.0 / NULLIF(SUM(beginning_mrr), 0)',
ltv_to_cac: 'AVG(customer_ltv) / NULLIF(AVG(acquisition_cost), 0)',
}
return `
SELECT
DATE_TRUNC('month', transaction_date) AS period,
${metricMap[intent.metric]} AS calculated_metric
FROM financial_ledger
WHERE transaction_date >= NOW() - INTERVAL '${intent.timeWindow}'
GROUP BY period
ORDER BY period DESC;
`.trim()
}Stage Your CONVERSATIONAL BI in Orbit
Describe your operational goal or workflow. Orbit opens with your prompt prefilled in the composer for immediate review.
Frequently Asked Questions
- How does conversational BI avoid hallucinated SQL queries?
- Orbit enforces a governed Semantic Metric Layer. Rather than allowing an LLM to generate raw SQL strings directly, natural language is mapped to verified entity definitions and compiled into structured AST queries.
- Can this connect to PostgreSQL, Snowflake, or BigQuery?
- Yes. Orbit connects via encrypted SSL tunnels to read-replica instances of PostgreSQL, Snowflake, Google BigQuery, MySQL, and Supabase.
- How does conversational BI compare to Tableau or Looker?
- Tableau and Looker require dedicated data analysts to build static dashboards that show what happened in the past. Conversational BI allows any executive to ask why metrics changed and receive diagnostic explanations in seconds.
- Can conversational BI accidentally modify production data?
- No. Analytical queries are strictly restricted to read-replica connections with read-only database permissions. State-mutating actions require explicit confirmation gates.
Experience Continuous Software & Business AI in Orbit
Connect databases, generate multi-target codebases, and eliminate fragmented toolchains inside one continuous workspace.