Inigra Software House

Tag: CRM

  • Automated CRM platform for real-time payment monitoring and debt collection

    Automated CRM platform for real-time payment monitoring and debt collection

    We built a custom CRM platform for a real estate development company to automate payment monitoring and client communication. The system integrates directly with the bank via secure API to track incoming payments in real-time, identifies overdue invoices, and initiates automated reminders and escalation procedures. An internal debt collection engine streamlines follow-ups, significantly reducing manual effort and improving payment discipline.

    Key Features:

    Real-time bank account integration (PSD2-compliant API)

    Automatic detection of delayed or missing payments

    Automated email/SMS notifications to clients

    Configurable debt collection workflows and timelines

    Payment tracking dashboard and reporting module

    Role-based access control and audit logging

    Tech Stack:

    Frontend: React.js, Tailwind CSS

    Backend: Node.js (Express), REST API

    Database: PostgreSQL

    Integrations: Bank API (Open Banking), Twilio (SMS), SendGrid (email)

    Security: OAuth 2.0, HTTPS, data encryption at rest & in transit

    Infrastructure: Docker, Nginx, CI/CD pipelines, AWS

    Client: www.sovo.dev

  • Design and rollout of a scalable transaction data storage and processing platform

    Design and rollout of a scalable transaction data storage and processing platform

    A robust platform designed to efficiently store, process, and analyze high volumes of transaction data in real time. Built for scalability, resilience, and data integrity, it supports business-critical operations such as reporting, analytics, and system integrations.

    Key Features

    1. Scalable Data Ingestion
    Handles high-frequency transactional input from multiple systems, ensuring real-time data capture without performance degradation.

    2. Reliable Storage Layer
    Structured storage with schema-based validation using relational and time-series databases, optimized for fast querying and historical tracking.

    3. Stream Processing and ETL Pipelines
    Data is cleaned, transformed, and enriched on the fly using distributed stream processing frameworks (e.g., Apache Kafka, Apache Flink).

    4. Secure and Auditable
    Implements encryption at rest and in transit, role-based access control, and full audit logs for regulatory compliance.

    5. Integration-Ready
    Exposes APIs and data exports to integrate with BI tools, finance systems, and machine learning platforms.

    Tech Stack

    • Data Ingestion: Apache Kafka, REST APIs

    • Processing: Apache Flink, dbt, Python

    • Storage: PostgreSQL, ClickHouse or BigQuery for analytics

    • Infrastructure: Kubernetes, Terraform, AWS/GCP

    • Security: OAuth2, encrypted S3 backups, audit trail logging

    This platform enables businesses to make data-driven decisions with confidence while ensuring scalability and compliance.