Contact person: New CRM using AI for an Investment Bank

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Location: Oklahoma City, United States

Budget: Recommended by industry experts

Time to start: As soon as possible

Project description:
"I want to create an AI-driven lead engine CRM. so we can focus exclusively on winning more corporate-level mandates. The module must automatically score and prioritise every prospect that comes in, rank them by fit and deal potential, then trigger personalised follow-up messages without my team lifting a finger.

Here’s the flow I have in mind: the moment a new corporate record appears—whether it’s scraped from public filings, uploaded from pitch-lists or pushed in via API—the model analyses recent funding rounds, balance-sheet strength, sector activity and any public sentiment it can pick up. It assigns a confidence score we can sort by, then schedules an email or LinkedIn touchpoint that feels hand-written, complete with dynamic fields drawn from the same data set. Replies, bounces and engagement signals should loop back into the model so it keeps learning which profiles convert.

Key deliverables
• A production-ready scoring model trained on historical deal data and third-party financial feeds
* track new leads and track them so they dont get lost.
• Automated follow-up workflow for the CRM’s REST API and syncs all activity logs
• Admin dashboard where we can edit scoring weights, message templates and view real-time performance metrics
* Must generate a sales funnel and pipeline for revenue.
• Clear hand-off documentation plus a short video walkthrough explaining model retraining and maintenance
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I’m open on architecture—Python, Node, or a low-code layer—as long as it’s secure, explainable and easy to maintain behind our firewall. If you’ve previously built AI lead systems for B2B finance and can show verifiable results, let’s talk." (client-provided description)