
High-Performance Data Engineering. Total Sovereignty.
TIMi delivers the power of a complete cloud data stack—on-premises, fully sovereign, and ridiculously fast.
We reject artificial vendor lock-in and hidden costs. Instead, we offer absolute peace of mind through engineering excellence, giving your team the freedom to experiment, innovate, and solve complex AI, analytics, and automation challenges in record time.
Why Top Enterprises Choose TIMi?
Enterprise Integration & *No-Code* ETL/Data preparation: Automate complex workflows and seamlessly link your entire stack: SAP, Salesforce, SharePoint, S3, Azure Storage, PowerBI, Tableau, etc.
Unmatched Infrastructure Efficiency: Our competitors such as Databricks, Dataiku, and MS Fabric all rely on Spark—and that makes them inherently inefficient since a single €2k TIMi server outperforms a 267-node Spark cluster. TIMi process billions of rows in seconds and manage petabyte-scale data lakes at a fraction of the cost.
Proven AI Leadership: Harness pioneering machine learning from the creators of the first Auto-ML engine (est. 2007).
Whether deployed on-premises or via our EU-Hosted Sovereign Cloud, TIMi empowers leaders in Banking, Telecoms, Manufacturing, Retail, Defense and Government.
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SCIKIQ is one of the most innovative AI-native Data & Intelligence platforms for enterprises, built to make enterprise data AI-ready in weeks, not years.
Recognized by Forrester among leading AI-augmented data platforms, NASSCOM League of 10, YourStory Tech30, Inc42 and DataIQ, SCIKIQ is trusted by leading global enterprises across the USA, India, and UAE.
SCIKIQ brings Data Integration, Data Quality, Data Governance, Metadata Management, Data Lineage, Semantic Intelligence, Knowledge Graphs, Conversational Analytics, Generative AI, Data Products and AI Agents together in one unified platform. Unlike traditional data platforms that require enterprises to move or rebuild their technology stack, SCIKIQ works with what you already have. Connect SAP, Salesforce, Oracle, Snowflake, Databricks, AWS, Azure, GCP, data lakes, warehouses and enterprise applications through 200+ pre-built connectors, with no rip-and-replace.
What makes SCIKIQ different is Contextual Intelligence.
SCIKIQ doesn't just connect data; it helps AI understand its business meaning. Its semantic layer combines business terms, KPI definitions, metadata, lineage, ownership, rules, ontologies and relationships to create a trusted foundation for enterprise AI. Business users can talk to their data in natural language, investigate KPIs, discover root causes and generate insights without SQL. Data teams gain enterprise-grade governance, quality, lineage and control. AI teams get trusted, contextual data for building GenAI applications and intelligent AI agents.
Why enterprises choose SCIKIQ
AI-ready in 3–6 weeks | 167+ connectors | 99.9% availability | Multi-cloud | No-code | No vendor lock-in | No replatforming
Proven production deployments across Manufacturing retail, airlines, logistics, BFSI
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JMP Statistical Software
JMP is a data analysis tool compatible with both Mac and Windows that merges robust statistical capabilities with engaging interactive visualizations.
The software simplifies the process of importing and analyzing data through its user-friendly drag-and-drop interface, interconnected graphs, an extensive library of advanced analytic features, a scripting language, and various sharing options, enabling users to explore their datasets more efficiently and effectively.
Initially created in the 1980s to leverage the potential of graphical user interfaces for personal computing, JMP continues to evolve by incorporating innovative statistical techniques and specialized analysis methods from diverse industries with each new version released. Furthermore, John Sall, the founder of the organization, remains actively involved as the Chief Architect, ensuring the software stays at the forefront of analytical technology.
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Microsoft Cognitive Toolkit
The Microsoft Cognitive Toolkit (CNTK) is an open-source framework designed for high-performance distributed deep learning applications. It represents neural networks through a sequence of computational operations organized in a directed graph structure. Users can effortlessly implement and integrate various popular model architectures, including feed-forward deep neural networks (DNNs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs/LSTMs). CNTK employs stochastic gradient descent (SGD) along with error backpropagation learning, enabling automatic differentiation and parallel processing across multiple GPUs and servers. It can be utilized as a library within Python, C#, or C++ applications, or operated as an independent machine-learning tool utilizing its own model description language, BrainScript. Additionally, CNTK's model evaluation capabilities can be accessed from Java applications, broadening its usability. The toolkit is compatible with 64-bit Linux as well as 64-bit Windows operating systems. For installation, users have the option of downloading pre-compiled binary packages or building the toolkit from source code available on GitHub, which provides flexibility depending on user preferences and technical expertise. This versatility makes CNTK a powerful tool for developers looking to harness deep learning in their projects.
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