The AI Banking
Copilot
Stop waiting for Monday morning reports. We built an intelligent agent that lets managers query live branch performance, loan books, and KPIs using plain English—instantly.
The Analyst Bottleneck
In most banks, data is abundant but insights are scarce. Managers rely on static PDFs or overworked BI teams.
Time Latency
Ad-hoc requests (e.g., "Why is churn up?") take 24-48 hours to process. By then, the opportunity to act is lost.
Data Silos
Performance data lives in the CRM, financial data in Core Banking. Merging them manually is painful and error-prone.
Static Views
Standard dashboards don't answer "Why." They show what happened, but prevent deep-dive analysis.
From Question to Action
We created a semantic translation layer that turns business questions into rigorous database queries.
1. Natural Language
"Compare Q3 vs Q2 loan defaults."
2. LLM Translation
Maps intent to schema & generates SQL.
3. Visualization
Results rendered as interactive charts.
The Logic: Text-to-SQL
The Copilot doesn't guess. It uses a Retriever-Augmented Generation (RAG) pipeline to understand the specific banking schema (tables, columns, relationships).
It includes a Smart Caching Layer (Redis). If a Regional Manager asks a question that was asked 5 minutes ago, the system serves the cached result instantly, reducing database load.
Enterprise Security
COUNT(c.customer_id) AS churned_count,
(COUNT(c.customer_id) * 100.0 / b.total_cust) AS churn_rate
FROM branches b
JOIN customers c ON b.id = c.branch_id
WHERE c.status = 'CLOSED'
AND c.close_date >= DATE('now', '-30 days')
GROUP BY b.branch_name
HAVING churn_rate > 5
ORDER BY churn_rate DESC;
The ROI of Autonomy
Impact measured across 250+ branches in Pilot Phase