AI Business Intelligence

Executive summary

A natural-language analytics layer over the business's own data. Users ask questions in plain English; the system translates them into safe queries against a governed data lake, analyzes the results, and returns charts and narrative explanations — democratizing BI beyond the analyst team.

Business problem

Traditional BI requires analysts and dashboards that answer yesterday's questions. Leaders can't explore freely, and every new question is a ticket. Data is spread across systems with no unified, queryable view.

Architecture

AWS services

Data flow

1. 1

   Source systems are ingested into an S3 data lake and cataloged by Glue.

2. 2

   A user asks a question in plain language.

3. 3

   Bedrock interprets it; Lambda generates a safe, governed SQL query (allow-listed tables/columns).

4. 4

   Athena runs the query over the lake; results are analyzed and charted.

5. 5

   Bedrock explains the findings in narrative form, with the underlying numbers cited.

Security considerations

• Governed query layer with allow-listed schemas — no arbitrary SQL from the model.
• Row/column-level access controls via Lake Formation as needed.
• Data encrypted at rest and in transit; full query audit.
• PII masking and least-privilege access.

Cost considerations

• Athena is pay-per-query; Glue and S3 are usage-based.
• Bedrock for NL understanding + narrative is the main AI cost.
• Partitioning and columnar formats (Parquet) keep query cost low.

Scalability

• Data-lake architecture scales to large volumes cheaply.
• Athena scales queries automatically; no clusters to manage.
• New sources onboard via Glue without disrupting users.

Deployment roadmap

Future enhancements

• Proactive insights and anomaly detection.
• Scheduled natural-language reports.
• Forecasting and what-if analysis.
• Integration with the executive dashboard.