Everything MIRA can do for your business
AI-powered analytics, verified answers, and enterprise-grade security.
MIRA AI doesn't just pull the numbers. It tells you what they mean.
Ask a question in plain English, get back a full analysis: visualisations, written conclusions, actionable insights. Like having a data analyst on call, without the wait.
Your business data, on autopilot
MIRA AI builds a living picture of your business that updates in real time. No re-uploading, no stale reports, no Sunday evening data admin.
Your tools, your data, all connected in one place
MIRA AI connects to the platforms your business already runs on, so every analysis has the full picture. No more switching between dashboards.
- Connect CRMs, databases, spreadsheets, cloud platforms and more
- New integrations added every sprint
- Your data stays in your environment, fully secure
- Works with whatever systems you already use
Connected to workspace and indexed all business data sources. Identified 4 queryable domains across the stack.
Domains Discovered
- Sales & CRM5 tables
- Finance & Accounting6 tables
- Marketing & Ads4 tables
- Operations3 tables
Matched query intent to the “Sales & CRM” domain. Inspected table schemas and detected join relationships across revenue data.
Selected Domain
Tables Found
- REVENUE_LEDGER86,400 rows
- QUARTERLY_TARGETS48 rows
Generated and executed SQL against revenue tables. Aggregated actuals vs targets by month for the current quarter.
Generated Query
SELECT r.month,
SUM(r.amount) AS revenue,
t.target
FROM REVENUE_LEDGER r
JOIN QUARTERLY_TARGETS t
ON r.period = t.period
AND r.month = t.month
WHERE r.period = 'Q1_2025'
GROUP BY r.month, t.target
ORDER BY r.month;Execution
Mapped query results to a visualization. Selected grouped bar chart to compare monthly revenue against targets.
Visualization Config
Source Query
SELECT month,
SUM(amount) AS revenue,
target
FROM REVENUE_LEDGER r
JOIN QUARTERLY_TARGETS t
ON r.period = t.period
WHERE period = 'Q1_2025'
GROUP BY month, target
ORDER BY month;In Q1 2025, total revenue reached $1.24M1Citation 1TableREVENUE_LEDGERQuerySUM(amount) WHERE period = 'Q1_2025' — 18% above last quarter2Citation 2TableREVENUE_LEDGERQuery(SUM(amount) WHERE Q1_2025) / (SUM(amount) WHERE Q4_2024) - 1. January and February both exceeded targets, while March came in $40K behind at $440K3Citation 3TablesREVENUE_LEDGERQUARTERLY_TARGETSQueriesSUM(amount) WHERE month = 'March' AND period = 'Q1_2025'target WHERE month = 'March' AND period = 'Q1_2025'. Average deal size grew 22% quarter-over-quarter4Citation 4TablesREVENUE_LEDGERDEALSQueriesAVG(deal_size) WHERE period = 'Q1_2025'AVG(deal_size) WHERE period = 'Q4_2024' — closing 3 open enterprise deals would exceed the full-quarter goal.
Every answer,
verified.
MIRA layers multiple verification systems across every response — so every figure can be traced to its source, cited from the raw data, and validated on demand.
MIRA records a full lineage at every stage of reasoning — from domain selection through query execution to visualization. Every step is timestamped and linked end-to-end, so you can always rewind, double-check any decision, and know exactly how your answer was built.
Each reasoning step is captured as a structured tool call — tables accessed, SQL generated, rows returned, visualization parameters set. Expand any step to see the complete execution trace and verify exactly where MIRA pulled its data from.
Every figure in a MIRA response carries an inline citation. Click any superscript number to instantly see the exact tables queried and the SQL behind it — no guessing, no black boxes, just full source transparency.
Hit Validate on any citation and MIRA re-runs the source query directly against your live data, compares it to the reported figure, and flags any discrepancy — giving you a one-click audit trail for every number in the response.
Scale with security.
Enterprise-grade infrastructure that keeps your data in your environment, fully audited, and always available — at any scale.
