Can AI-Generated Business Intelligence Be Trusted?
Automated Business Intelligence (BI) Report Generator employing CrewAI, Groq, LiteLLM, and Python -> verifying if this type of multi-agent-based system can create reports that meet the standards of the Business Intelligence tool we use to extract the significant business insights that are in high demand.
Validate the data, calculations, insights and AI-generated narratives behind intelligent business reporting systems. Kennen Technologies is building validation tooling and methodologies to test whether multi-agent AI-generated reports are supported by the underlying enterprise data.
- From output to evidence
- Validate the complete intelligence pipeline — not just the final report
- Data, calculations, KPIs, insights, narratives and recommendations
Automated Business Intelligence (BI) Report Generator
Kennen is exploring and testing a multi-agent BI reporting architecture. This is a prototype, research initiative and proof-of-concept — not a production-ready commercial product. The system investigates how specialised AI agents can collaborate across the reporting pipeline.
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Data Analysis
Agents analyse enterprise datasets to understand structure, distributions and dimensions.
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KPI Analysis
Agents compute and evaluate key performance indicators against defined business formulas.
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Pattern Identification
Agents identify trends, correlations and anomalies within the data.
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Business Insights
Agents generate explanatory insights grounded in the analysed data.
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Risk Identification
Agents surface risks, outliers and areas requiring attention.
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Recommendations
Agents propose recommendations that logically follow from the validated evidence.
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Report Generation
Agents assemble a structured BI report with narratives, metrics and supporting context.
Technologies under exploration
The prototype employs technologies such as CrewAI, Groq, LiteLLM and Python to investigate multi-agent collaboration for BI report generation. CrewAI, Groq and LiteLLM are third-party technologies and are not Kennen-owned products.
Research status
This is a proof-of-concept and research initiative. It is not currently a production-ready commercial product. The focus is on testing whether multi-agent AI-generated reports can achieve the accuracy, consistency and business usefulness expected from enterprise BI reporting.
Related capabilities
Explore our Multi-Agent Systems Development practice and AI Agents and Agentic AI Development for the broader agentic AI engineering capabilities behind this research.
Can multi-agent AI-generated reports achieve the accuracy, consistency and business usefulness expected from enterprise Business Intelligence reporting?
The objective is not simply to generate attractive reports. The system should evaluate whether generated reports are supported by the underlying enterprise data — tracing conclusions back to evidence, validating calculations and detecting unsupported claims.
The complete intelligence validation pipeline
Validation spans the full pipeline — from source data through AI-agent analysis and report generation to human review — so enterprise decisions rest on validated intelligence, not just generated output.
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Enterprise Dataset
Source data from business systems, databases and data warehouses.
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Data Validation
Completeness, accuracy, schema integrity and anomaly checks.
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Analysis Agents
Multi-agent analysis over validated data.
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KPI & Pattern Analysis
KPI computation and trend, correlation and anomaly identification.
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Insight Generation
LLM-generated explanations and conclusions grounded in data.
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AI-Generated BI Report
Structured report with narratives, metrics and recommendations.
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Automated Validation
Hallucination detection, KPI reconciliation and consistency testing.
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Human / Business Review
Business stakeholders review validated insights before action.
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Trusted Business Insight
Enterprise decisions supported by validated, traceable intelligence.
Validation categories for AI-generated BI
Each category targets a specific stage of the intelligence pipeline — from source data through transformations, KPIs, insights and final report consistency.
Source Data Validation
Validate completeness, accuracy, duplicates, missing values, schema consistency and anomalies.
Transformation Validation
Verify joins, transformations, aggregations and intermediate calculations.
KPI Validation
Compare generated KPIs against deterministic calculations and defined business formulas.
Pattern & Trend Validation
Determine whether reported trends, correlations and anomalies are actually supported by the underlying dataset.
LLM Insight Validation
Evaluate whether generated explanations and conclusions are grounded in validated data.
Hallucination Detection
Detect unsupported facts, invented values or conclusions that cannot be traced to the source data.
Recommendation Validation
Determine whether recommendations logically follow from the validated evidence.
Report Consistency Testing
Execute equivalent datasets/questions repeatedly where appropriate and identify material inconsistencies.
Cross-System Reconciliation
Where applicable, compare AI-generated metrics with trusted source systems or existing BI outputs.
Performance Testing
Measure processing time, model latency, token usage, throughput and scalability across larger datasets.
From AI-Generated Output to Validated Business Intelligence
Generative AI can produce reports quickly, but speed alone does not make an insight trustworthy. Enterprise decisions depend on the quality of the underlying data, the correctness of calculations and the ability to trace important conclusions back to evidence.
Kennen Technologies focuses on testing the complete intelligence pipeline — from source data and AI-agent behavior to KPIs, generated insights and final business reports.
Is Your Organization Building AI on Large Enterprise Datasets?
Work with Kennen Technologies to assess how your data, models, LLM applications and autonomous agents can be systematically validated for reliability, accuracy and business usefulness.
Related: Multi-Agent Systems Development · AI Agents and Agentic AI Development · AI/LLM Testing and QA