Banking & Financial Services
AI-Driven Sales Acceleration
4 autonomous agents accelerate every stage of the sales pipeline. 28% improvement in close rates.
Agentic AI Workflow
4 agents run in parallel to score leads, analyze deals, generate proposals, and forecast pipeline
The Challenge
Relationship managers were spending more time on admin than on selling
A commercial bank's sales team of 200 relationship managers was struggling with 23% close rates on their commercial lending pipeline. RMs spent 60% of their time on non-selling activities: researching prospects, updating CRM, preparing proposals, and writing call summaries.
Pipeline prioritization was based on deal size alone, ignoring buying signals. Proposal quality varied wildly across the team, and new RMs took 9 months to reach average productivity. The bank estimated it was leaving $45M in annual revenue on the table from poorly qualified leads and missed follow-ups.
The bank needed a system that could prioritize the right deals, prepare RMs for every conversation, and generate consistent, high-quality proposals.
The Solution
Agents that score, research, coach, and propose so RMs can focus on relationships
Vijan.AI deployed 4 autonomous agents to support the sales team. The Pipeline Scorer ranks every deal using CRM signals, engagement data, and firmographic attributes. The Research agent enriches accounts with financial filings, news, competitor activity, and org charts from public data. The Coach agent provides real-time call guidance using conversation intelligence, surfacing objection handlers and next-best-actions during live calls. The Proposal agent drafts personalized pitch decks and term sheets using LLM, tailored to each prospect's industry and needs.
Autonomous Agents
How each agent reasons, decides, and acts
Step 1 · Scoring
Lead Scoring Agent
Predictive Lead Scoring
Automatically scores and prioritizes leads using multi-factor models that predict conversion likelihood.
Input
Lead data, firmographics, engagement history, CRM data
Output
Lead scores with priority ranking and fit analysis
- Calls lead scoring model to compute conversion probability based on historical data
- Calls fit analysis tool to match lead profile against ideal customer persona
- Autonomous decision: route hot leads to sales, nurture medium leads, disqualify poor fits
- Routes high-priority leads to Deal Analytics agent for deeper assessment
Step 2 · Health Check
Deal Analytics Agent
Real-Time Deal Health Monitoring
Continuously assesses deal health, identifies risks, and provides coaching recommendations to accelerate closures.
Input
Deal stage, activity log, competitor intel, buying signals
Output
Deal health score with risk flags and next-best actions
- Calls deal health tool to assess engagement level, stakeholder involvement, timeline
- Calls risk assessment tool to flag stalled deals, missing decision-makers, competitive threats
- Autonomous decision: recommend specific actions (executive briefing, proof-of-concept, discount)
- Routes deal insights to sales team dashboard and Proposal Generator
Step 3 · Proposal
Proposal Generator
Intelligent Proposal Generation
Generates customized sales proposals with automated pricing, contract terms, and ROI calculations.
Input
Deal requirements, product catalog, pricing rules, customer data
Output
Complete proposal document with pricing and terms
- Calls template engine to generate proposal using deal-specific content and branding
- Calls pricing calculator to compute optimal pricing, discounts, and payment terms
- Autonomous decision: apply tiered discounting based on deal size and strategic value
- Routes finalized proposal to sales rep for review and customer delivery
Step 4 · Forecast
Pipeline Forecaster
AI-Powered Pipeline Forecasting
Predicts quarterly revenue outcomes using deal-level data and historical conversion patterns.
Input
Pipeline data, deal stages, historical win rates, seasonality
Output
Revenue forecast with confidence intervals and scenario analysis
- Calls forecasting model to predict close probability per deal stage
- Calls confidence scoring tool to quantify forecast accuracy and variance
- Autonomous decision: flag pipeline gaps and recommend actions to hit revenue targets
- Routes forecast to sales leadership dashboard for planning and resource allocation
Results
Measurable impact within 90 days of deployment
More Deals Closed
Close rate improved from 23% to 29.4% through better pipeline prioritization and real-time coaching.
Faster Ramp-Up
New RM time-to-productivity reduced from 9 months to 3 months with AI coaching and automated research.
More Selling Time
RM time spent on active selling increased from 40% to 65% by automating research, CRM updates, and proposals.
Revenue Increase
Incremental annual revenue from improved close rates and faster deal cycles.
Implementation
From pilot to production in 12 weeks
Agent Design & Tool Integration
Defined agent capabilities, connected ML model, rules engine, graph DB, and chargeback API tools. Configured orchestrator routing logic.
Shadow Mode & Autonomous Tuning
Agents ran in shadow mode on 10% of transactions. Tuned decision thresholds, tool call parameters, and feedback loop retraining frequency.
Full Autonomous Deployment
Production rollout across all channels. Agents operating fully autonomously with human-in-the-loop for critical escalations only.
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