A fintech company had reps manually searching through client financial data and relying on personal knowledge to diagnose issues — slow, inconsistent, and unreliable at scale. I built an AI agent with retrieval-augmented generation that gives every rep instant, sourced recommendations.
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The Knowledge GapThe Problem
Client advisors were manually searching spreadsheets and internal databases to diagnose client financial situations. Each rep had different knowledge, leading to inconsistent advice. Onboarding new reps took months before they could provide reliable guidance.
🔍Reps manually searching through spreadsheets and databases
↕Inconsistent advice — depends on who you ask
⏱New rep onboarding takes months
✕No citations or sources behind recommendations
📈Can't scale without proportional headcount
⚠Risk of outdated or incorrect financial guidance
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The Intelligent SystemSolution & Approach
I built an AI agent where a rep enters the problem description and selects the relevant company. The system queries a vector database of financial records to return specific, sourced recommendations — citing exact invoices, clients, and actions. Every output is traceable and actionable, not generic. The architecture was designed to scale into direct client-facing dashboards.
✓Vector database ensures every recommendation is sourced and verifiable
✓Cites specific invoices, clients, and actions — not generic advice
✓New reps get the same quality output as veterans on day one
✓Architecture designed to scale into client-facing self-service
✓RAG approach means knowledge updates automatically with data
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The Intelligence LayerQuery
How should we handle client X's invoice?
Based on the current policy, client X qualifies for a 15-day net payment term. The outstanding invoice should be flagged for follow-up per the escalation workflow.
Invoice #4521Policy Doc v3Client History
92% confidence
Outcome & Results
Reps now get sourced financial recommendations in seconds instead of hours. Advice quality is consistent regardless of individual rep experience. The vector database enables trust through citations — reps can verify every recommendation against the underlying data.
<10sTime to recommendation
100%Sourced with citations
Day 1New rep productivity
0Unsourced recommendations
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