From raw data to insights. Automatically.
Buda's AI agent for data analysis handles data cleaning, multi-source aggregation, ad-hoc queries, anomaly detection, scheduled reporting, and executive briefs — with persistent context across your schema and data sources.
Data analysts spend most of their time preparing data, not analysing it.
Analysts join tables, validate output, and write summaries manually.
Revenue spikes, webhook gaps, and churn clusters show up in reports after the action window closes.
An AI agent that knows your data so your analysts don't have to repeat themselves.
AI agents that run the whole data workflow
Query, clean, aggregate, detect, and report agents share the same data workspace — so a stakeholder question can trigger a pipeline from raw data to reviewed insight.
See capabilities
Data context that persists across every run
Schema map, data dictionary, known anomaly patterns, and prior query results stay in the workspace. The agent does not re-learn your data every time.
Start with context
Analyst reviews every output before it reaches a stakeholder
Every analysis, report, and anomaly flag lands in the review queue. The analyst verifies data, interpretation, and framing before sharing.
See workflowsSix AI capabilities for the full data analysis workflow.
Buda gives data teams persistent agents for querying, cleaning, aggregation, anomaly detection, scheduled reporting, and executive insights.
Natural language data querying
Ask data questions in plain English. The agent identifies relevant tables, writes and runs the query, cleans the result, and returns a structured answer.
Automated data cleaning
Detects and resolves nulls, duplicates, inconsistent formats, and mismatched keys, logging every transformation for analyst review.
Multi-source data aggregation
Joins data from CRM, payment systems, analytics, and internal tools into a unified dataset with reconciliation logs.
Anomaly and outlier detection
Watches for revenue spikes, churn clusters, data gaps, and statistical outliers, then routes alerts with context to the right analyst.
Scheduled data reporting
Pulls data on a schedule, writes structured reports with segment breakdowns and deltas, and queues drafts for review.
Executive insight generation
Surfaces the biggest opportunity, the most significant risk, and the clearest action from any dataset in a leadership-ready format.
Automate your first data workflow in 30 minutes.
Pick one recurring workflow, connect the context, run the first output, and review before scheduling.
Start with the data workflow that costs your team the most time per week.
Choose one repeated data workflow with clear inputs, a trusted reviewer, and a reviewable output.
Ad-hoc analysis on demand
Stakeholder asks a business question. Agent identifies tables, runs the query, cleans the join, and returns a structured insight with the data behind it.
Start this workflowAutomated weekly data report
Every Monday, the agent pulls data from connected sources, writes the segment summary with deltas, and queues the draft for analyst review.
Automate reportsReal-time anomaly monitoring
Agent watches connected data sources for spikes, gaps, and outliers, then flags issues with context before they surface in the weekly report.
Monitor anomaliesExecutive data brief
Agent surfaces the week's top opportunity, biggest risk, and clearest action across connected sources, formatted for leadership and reviewed by the analyst.
Create executive briefsPilot an AI data analysis agent in four steps.
Start with one recurring workflow, connect source context, run on a real dataset, then schedule and expand after review.
Pick your most time-consuming data workflow
Start with ad-hoc analysis, weekly reporting, anomaly monitoring, or executive briefs.
Connect data sources and load your schema
Add source context, schema maps, data dictionaries, prior query results, and known anomaly patterns.
Run the agent on one real dataset
Let the agent query, clean, aggregate, detect, or report, then review every transformation and interpretation.
Schedule reports and expand
Once trusted, schedule the workflow and expand to more sources, stakeholders, and report types.
Give your data team an AI agent that handles the preparation work.
Run data cleaning, ad-hoc queries, aggregation, anomaly detection, scheduled reports, and executive briefs in one persistent Buda workspace.