
SCIKIQ is an AI-native Data & Intelligence Platform designed to help enterprises make their data trusted, governed, connected, and ready for AI in weeks rather than years. Recognized by Forrester, NASSCOM League of 10, YourStory Tech30, Inc42, and DataIQ, SCIKIQ supports enterprises across the USA, India, UK, and UAE.
SCIKIQ brings together Data Integration, Data Quality, Data Governance, Metadata Management, Data Lineage, Semantic Intelligence, Knowledge Graphs, Conversational Analytics, Generative AI, Data Products, and AI Agents within one unified platform.
Unlike traditional data platforms that often require extensive replatforming or migration, SCIKIQ works with an enterprise’s existing technology ecosystem. Organizations can connect SAP, Salesforce, Oracle, Snowflake, Databricks, AWS, Azure, GCP, data lakes, data warehouses, and enterprise applications through 200+ pre-built connectors, without rip-and-replace.
Contextual Intelligence at the Core
SCIKIQ goes beyond connecting data by helping AI understand the business context behind it.
Its semantic intelligence layer brings together business terminology, KPI definitions, metadata, lineage, ownership, business rules, ontologies, and relationships to create a trusted context layer for enterprise analytics and AI.
Business users can ask questions in natural language, investigate KPIs, identify root causes, and generate insights without writing SQL. Data teams gain enterprise-grade capabilities for data integration, quality, governance, lineage, metadata, and control.
AI teams gain trusted, contextual enterprise data for building Generative AI applications, copilots, and intelligent AI agents.
Why Enterprises Choose SCIKIQ
AI-ready in 3–6 weeks | 200+ connectors | 99.9% availability | No-code | Multi-cloud | No vendor lock-in | No replatforming
SCIKIQ has production deployments across industries including manufacturing, retail, aviation, logistics, BFSI, healthcare, and other data-intensive enterprises.
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Okyline is an Executable Data Design (EDD) platform for declarative data validation contracts and measurable operational data quality.
Instead of maintaining disconnected specifications, validators, tests, and quality dashboards, Okyline uses a single executable contract as the operational source of truth for validation and flow quality monitoring.
The same readable contract drives multi-format validation, deterministic execution, quality measurement, data quality gate, and historical quality analytics across APIs, events, files, LLM structured outputs, and enterprise data flows.
Community Edition provides the open specification, a free Java validation runtime, a public Claude AI assistant for contract generation, and a free online studio for executable JSON validation contracts and JSON Schema transpilation.
Enterprise Edition supports direct validation of JSONL, XML, CSV, FIXED, and EDI flows, data quality gate, and operational quality dashboards, all without databases
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DataBuck is an AI-powered data validation platform that automates risk detection across dynamic, high-volume, and evolving data environments. DataBuck empowers your teams to:
✅ Enhance trust in analytics and reports, ensuring they are built on accurate and reliable data.
✅ Reduce maintenance costs by minimizing manual intervention.
✅ Scale operations 10x faster compared to traditional tools, enabling seamless adaptability in ever-changing data ecosystems.
By proactively addressing system risks and improving data accuracy, DataBuck ensures your decision-making is driven by dependable insights.
Proudly recognized in Gartner’s 2024 Market Guide for #DataObservability, DataBuck goes beyond traditional observability practices with its AI/ML innovations to deliver autonomous Data Trustability—empowering you to lead with confidence in today’s data-driven world.
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Sifflet
Automatically cover thousands of tables with ML-based anomaly detection and 50+ custom metrics. Comprehensive data and metadata monitoring. Exhaustive mapping of all dependencies between assets, from ingestion to BI. Enhanced productivity and collaboration between data engineers and data consumers. Sifflet seamlessly integrates into your data sources and preferred tools and can run on AWS, Google Cloud Platform, and Microsoft Azure. Keep an eye on the health of your data and alert the team when quality criteria aren’t met. Set up in a few clicks the fundamental coverage of all your tables. Configure the frequency of runs, their criticality, and even customized notifications at the same time. Leverage ML-based rules to detect any anomaly in your data. No need for an initial configuration. A unique model for each rule learns from historical data and from user feedback. Complement the automated rules with a library of 50+ templates that can be applied to any asset.
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