Financial advisors were manually searching spreadsheets to answer client questions — each giving different answers. We built an AI agent backed by a vector database that delivers sourced, cited recommendations in seconds.
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The Knowledge GapThe Problem
Client advisors manually searched spreadsheets and internal databases to diagnose financial situations. Each rep had different knowledge, leading to inconsistent advice. New reps took months to become productive.
🔍Reps manually searching through spreadsheets and databases
↕Inconsistent advice — depends on who you ask
⏱New rep onboarding takes 3-6 months
✕No citations or sources behind recommendations
📈Cannot scale without proportional headcount
⚠Risk of outdated or incorrect financial guidance
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The Intelligent SystemSolution & Approach
We 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.
✓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 new 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 experience. Every recommendation is backed by citations that reps can verify.
<10sTime to recommendation
100%Sourced with citations
Day 1New rep productivity
0Unsourced recommendations
Tech Stack
PythonLangChainPineconeOpenAI GPT-4FastAPIReact
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