Data Semantics is an AI-first digital transformation partner helping enterprises turn data into action—inside the workflows where finance and operations teams run the business.
Built on a decade of excellence in data engineering and analytics, our future is driven by Autonomous AI Agents, Intelligent Automation and Modern Data Assessments.
We don’t just implement technology—we build intellectual property to solve complex business challenges. We make your data trusted, automated and active through four core capabilities:
1. Migration Assurance for ERP/CRM Upgrades & M&A (Data Prep 360) Most programs validate “loaded.” We validate “true.” Evidence-based sign-off with 100% record-level validation and reconciliation—so Day-1 reporting ties back with audit-ready confidence. 2. Intelligent Finance Automation: AP, AR & Reconciliation (Serina.ai) We modernize finance ops end-to-end—automating Procure-to-Pay and Order-to-Cash, reducing manual effort in invoicing, collections, cash application, and exception handling. We also automate bank/vendor/intercompany reconciliations—backed by consistent validations and audit-ready traceability. 3. Modern Data Platforms & Analytics (Fabric / Databricks / Snowflake + AI Experiences) We build lakehouse and analytics ecosystems on Microsoft Fabric, Databricks, and Snowflake—enabling governed reporting, automated MIS/board packs, chat-with-data, and proactive anomaly signals so leaders act early without Excel dependency. 4. Contact Center Intelligence & Autonomous Engagement (Zebo) Zebo is our autonomous contact center AI for sales, collections, and customer service—paired with conversation intelligence for QA, agent performance, compliance monitoring, and dispute/collections insights, while keeping governance and control in place.
With a 400+ strong workforce across USA, UAE and a deep GCC footprint—we serve 200+ enterprises across Finance, Real Estate, Logistics to Manufacturing, with enterprise-grade security and governance.
Rating Reviews
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Pros: I really appreciated the exposure to various challenging data projects. You get to work with cutting-edge tools and techniques, which is fantastic for skill development. My colleagues were intelligent and supportive, making it a pleasant place to collaborate. The hybrid work model offered good flexibility, allowing for a decent work-life balance.
Cons: The main drawback was the often-unclear project scope and shifting priorities, which could lead to some frustration and overtime. Internal documentation and standard operating procedures could also be more robust. Sometimes, getting timely approvals or resources felt like a bottleneck, impacting project timelines.
Advice to Management: Focus on refining project scoping and management practices. Investing in clearer communication tools and processes would significantly boost team efficiency and employee satisfaction.
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How do data semantics in category management help set realistic goals for a mid-sized retail company?
Understanding data semantics clarifies product relationships and hierarchies, which really helped my team at our retail company set achievable sales targets and marketing strategies.
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