Cloud Data Integration
Robust pipelines and secure ingest to major cloud platforms (AWS, GCP, Azure).
Empowering small to mid-size businesses with AI automation that saves time and increases efficiency. From contract analysis to patient intake to receipt processing—we automate your repetitive work.
Transform your business with intelligent automation. Our AI solutions eliminate repetitive manual work, reduce errors by 90%, and save your team 10+ hours per week—so you can focus on what matters.
AI-powered contract analysis that extracts key terms, identifies risks, and summarizes obligations in seconds.
Automated patient intake that structures symptoms, assesses urgency, and suggests ICD codes—reducing front desk time by 60%.
Eliminate manual data entry. AI extracts all receipt data, categorizes expenses, and generates bookkeeping entries automatically.
Robust pipelines and secure ingest to major cloud platforms (AWS, GCP, Azure).
Streaming analytics and low-latency processing to unlock real-time insights.
Deploy and serve models at scale; monitoring and CI/CD for production ML.
Policy, lineage, and secure controls to ensure compliance and trust in your data.
Tailored architectures, containerized tooling, and automation to match business needs.
Founded in 2022 in the heart of Bronx, NYC, DIDAG (Diversity and Inclusion Data Analytics Group) has evolved from a data science consulting startup to a leader in AI-powered business automation. We now help small to mid-size businesses across legal, medical, and accounting sectors eliminate repetitive work through intelligent automation—while maintaining our commitment to enterprise-grade cloud data solutions. Our dual expertise in AI automation and cloud infrastructure means we can solve problems from simple workflow automation to complex data platform migrations.
A 12-attorney Baltimore law firm was spending 20+ hours per week on manual contract review. DIDAG deployed our Contract Intelligence Tool, reducing review time by 93% and enabling the firm to take on 40% more clients without hiring additional staff.
Tech & approach: GPT-4 powered document analysis, secure cloud deployment, user authentication with role-based access, automated extraction of key terms, risk identification, and obligation tracking. Integrated with existing document management system.
Outcome: 93% reduction in contract review time (30 min → 2 min per contract), $78,000 annual savings in billable hours, 40% increase in client capacity, and 99.1% accuracy rate on term extraction.
For a leading e-commerce platform, DIDAG developed a real-time analytics engine that enabled instant customer behavior insights, leading to a 20% increase in sales conversions through personalized marketing strategies.
Tech & approach: Stream processing with Kafka + Spark Streaming, event-driven enrichment, and a centralized analytics layer on Databricks. We instrumented event schemas and built feature stores to support personalization models.
Outcome: 20% increase in conversions through targeted offers, 35% improvement in analytics query latency, and an operational feature store enabling repeatable ML workflows.
We assisted a major healthcare provider in migrating their data operations to a secure cloud platform, reducing operational costs by 30% while enhancing data compliance and patient data security.
Tech & approach: Phased migration to encrypted cloud storage and managed analytics services, data anonymization in-flight, and validation using automated ETL tests. We used infrastructure as code (Terraform) and monitoring with Prometheus and Grafana.
Outcome: 30% reduction in operational costs, stronger auditability and compliance controls, and improved data availability for analytics and research.
DIDAG implemented machine learning models for a New York-based financial institution to detect fraudulent transactions in real-time, decreasing fraud incidents by 25% within the first six months of deployment.
Tech & approach: Feature engineering and model training on Databricks, real-time scoring via Kafka and containerized model servers, and robust model monitoring and alerting. We used explainability tooling to support compliance and reduce false positives.
Outcome: 25% reduction in fraud incidents, faster investigation times, and a production-ready ML monitoring stack for ongoing model governance.
DIDAG migrated a complex legacy data estate to a cloud-native platform, implemented NLP pipelines to extract structured insights from unstructured case records and communications, and deployed AI agents to automate triage and task routing. The combined solution reduced manual processing time by 40% and improved service matching while preserving data privacy and compliance.
Tech & approach: Cloud migration to managed data platforms, orchestration with Airflow, real-time ingestion with Kafka, ETL and analytics on Databricks. NLP pipelines used Hugging Face Transformers for entity extraction and semantic search; light-weight AI agents handled automated routing and workflow triggers.
Outcome: 40% reduction in manual processing time, improved accuracy of automated triage, and an auditable, privacy-preserving pipeline ready for scaled ML operations.
If you have any questions or need further information, please do not hesitate to contact us.