CRM Email Assistant
A full-stack banking CRM workspace that helps employees prepare accurate customer email drafts from structured case context. The assistant recommends draft settings, generates editable subjects and bodies, explains which CRM facts were used or omitted, and keeps every message behind an employee approval workflow before copy or CRM logging. It brings AI-assisted communication into daily relationship-management, leasing, support, and collections workflows without weakening compliance control.
React
TypeScript
Node.js
OpenAI
Docker
About the project
The Product: A CRM Email Assistant for bank employees who prepare repetitive but sensitive customer communications. The application is designed for leasing, support, collections, and relationship-management workflows where teams need to reference CRM facts quickly, maintain a professional tone, and keep every outbound message under employee review.
What it Does: The product turns CRM case data into a structured draft workflow. Employees can review the case queue, inspect customer profile details, product and contract information, safe facts, internal-only context, communication history, and audit events before asking the assistant to suggest scenario settings and generate an editable email draft.
How it Works: Built with React, TypeScript, Vite, and an Express API, the system loads CRM records server-side and exposes endpoints for cases, settings recommendation, case summary, and draft generation. Prompt builders use an AI-safe projection of the CRM case: selected values can be direct, masked with placeholders, or blocked entirely depending on the field policy.
The Advantage: The assistant produces structured, review-ready output instead of an opaque chat response. Every draft includes a subject, body, used facts, omitted facts, warnings, next CRM action, provider status, and confidence score. Copy and CRM logging remain locked until the employee confirms review, while deterministic fallback templates preserve a reliable workflow when model calls, parsing, or validation need graceful recovery.
Safety Model: The implementation separates employee-visible CRM context from AI-visible and customer-facing content. Customer identity, contract references, amounts, and due dates can be masked before provider calls and hydrated locally only after validation, while internal-only risk notes, escalation strategy, fraud review, collection pressure, and policy-sensitive context stay blocked from customer-facing drafts.
Enterprise Fit: The architecture supports a clean CRM adapter boundary, permission-aware field mapping, role-based visibility, compliance-approved wording libraries, email-client handoff, CRM writeback, draft quality analytics, and multilingual communication. This makes the assistant suitable for controlled banking environments where speed, traceability, and communication quality have to work together.
Learn tech information for this project
About the project
The Product: A CRM Email Assistant for bank employees who prepare repetitive but sensitive customer communications. The application is designed for leasing, support, collections, and relationship-management workflows where teams need to reference CRM facts quickly, maintain a professional tone, and keep every outbound message under employee review.
What it Does: The product turns CRM case data into a structured draft workflow. Employees can review the case queue, inspect customer profile details, product and contract information, safe facts, internal-only context, communication history, and audit events before asking the assistant to suggest scenario settings and generate an editable email draft.
How it Works: Built with React, TypeScript, Vite, and an Express API, the system loads CRM records server-side and exposes endpoints for cases, settings recommendation, case summary, and draft generation. Prompt builders use an AI-safe projection of the CRM case: selected values can be direct, masked with placeholders, or blocked entirely depending on the field policy.
The Advantage: The assistant produces structured, review-ready output instead of an opaque chat response. Every draft includes a subject, body, used facts, omitted facts, warnings, next CRM action, provider status, and confidence score. Copy and CRM logging remain locked until the employee confirms review, while deterministic fallback templates preserve a reliable workflow when model calls, parsing, or validation need graceful recovery.
Safety Model: The implementation separates employee-visible CRM context from AI-visible and customer-facing content. Customer identity, contract references, amounts, and due dates can be masked before provider calls and hydrated locally only after validation, while internal-only risk notes, escalation strategy, fraud review, collection pressure, and policy-sensitive context stay blocked from customer-facing drafts.
Enterprise Fit: The architecture supports a clean CRM adapter boundary, permission-aware field mapping, role-based visibility, compliance-approved wording libraries, email-client handoff, CRM writeback, draft quality analytics, and multilingual communication. This makes the assistant suitable for controlled banking environments where speed, traceability, and communication quality have to work together.
Learn tech information for this project
CRM Email Assistant
AI-assisted bank CRM email drafting with masked CRM data, editable drafts, human review gates, and auditable CRM logging.
ReactTypeScriptNode.jsOpenAIDockerCRM Email Assistant
AI-assisted bank CRM email drafting with masked CRM data, editable drafts, human review gates, and auditable CRM logging.
ReactTypeScriptNode.jsOpenAIDocker
Features
Safe CRM Context
The workspace is organized around a practical banking CRM review flow. Employees choose a customer case from the queue, inspect the customer profile, leasing contract, current case, safe CRM facts, internal-only context, communication history, and audit trail before generating any customer-facing text.
The product model keeps safety distinctions explicit. Facts approved for customer communication are shown separately from internal-only notes, and the backend policy classifies fields as direct, masked, or blocked before prompt construction. This creates a credible CRM integration pattern for AI-assisted drafting without exposing raw sensitive customer values to a model provider.
AI Draft Workflow
The assistant requires an explicit Suggest step before generation. It recommends the scenario, tone, and employee instruction for the selected CRM case, then lets the employee edit those settings before requesting a draft. This creates a visible checkpoint between case review and customer-facing content generation.
Draft output is structured for operational review instead of chat-style ambiguity. The response includes subject, body, used facts, omitted facts, provider status, confidence, review warnings, and the next CRM action. If live AI generation is unavailable or validation rejects the response, deterministic fallback templates keep the workflow predictable and uninterrupted.
Human Review Gate
The assistant supports a controlled bank employee workflow rather than one-click automation. Generated drafts are editable, but copy and CRM logging remain disabled until the employee confirms that the customer-facing message has been reviewed and CRM facts have been verified.
Each important action can be recorded in the audit trail: draft generated, draft edited, employee review confirmed, draft copied, and communication logged. This traceability pattern helps teams preserve operational accountability while improving response speed and consistency across customer communication workflows.
Features
Safe CRM Context
The workspace is organized around a practical banking CRM review flow. Employees choose a customer case from the queue, inspect the customer profile, leasing contract, current case, safe CRM facts, internal-only context, communication history, and audit trail before generating any customer-facing text.
The product model keeps safety distinctions explicit. Facts approved for customer communication are shown separately from internal-only notes, and the backend policy classifies fields as direct, masked, or blocked before prompt construction. This creates a credible CRM integration pattern for AI-assisted drafting without exposing raw sensitive customer values to a model provider.
AI Draft Workflow
The assistant requires an explicit Suggest step before generation. It recommends the scenario, tone, and employee instruction for the selected CRM case, then lets the employee edit those settings before requesting a draft. This creates a visible checkpoint between case review and customer-facing content generation.
Draft output is structured for operational review instead of chat-style ambiguity. The response includes subject, body, used facts, omitted facts, provider status, confidence, review warnings, and the next CRM action. If live AI generation is unavailable or validation rejects the response, deterministic fallback templates keep the workflow predictable and uninterrupted.
Human Review Gate
The assistant supports a controlled bank employee workflow rather than one-click automation. Generated drafts are editable, but copy and CRM logging remain disabled until the employee confirms that the customer-facing message has been reviewed and CRM facts have been verified.
Each important action can be recorded in the audit trail: draft generated, draft edited, employee review confirmed, draft copied, and communication logged. This traceability pattern helps teams preserve operational accountability while improving response speed and consistency across customer communication workflows.
Contact Us
Anastasia Timoshenko
Regional Account Manager
700+
In-house developers
27+
Years in industry
300+
Clients world wide
27+
Years in industry
5.0 Ratings