We design and implement robust data pipelines that gather, process, and prepare your data for GenAI. From collection to storage and preparation, our solutions ensure your data is ready for everything from RAG to fine-tuning LLMs.
Data engineering is the backbone of AI success, involving the collection, transformation, storage, and preparation of data for AI models. At Zangoh, we specialize in building pipelines that ensure data is clean, secure, and ready for various AI applications such as training, RAG, and real-time analytics.
Streamline data collection, processing, and storage for seamless AI integration.
We automate data collection from enterprise systems, ensuring the data is contextualized, clean, and ready for AI processing.
Use optimized vector databases and scalable architecture for fast and efficient retrieval of AI embeddings.
Implement data encryption, access controls, and versioning to ensure data security and integrity throughout the pipeline.
Zangoh’s data engineering process focuses on building the infrastructure for secure and efficient data collection, transformation, and storage. We handle the full data lifecycle, ensuring that your data is properly cleaned, contextualized, and stored for seamless integration with your AI models
Design the Data Pipeline: We identify data sources and design the integration and flow, tailoring the pipeline to your business needs.
Data Collection: We implement automated scripts to collect contextualized data from enterprise systems, reducing hallucinations and ensuring AI relevance to your specific use case.
Data Transformation: Noisy data is cleaned, missing data is handled using imputation techniques, and unstructured data is converted to structured formats for efficient processing.
Data Storage: Using scalable vector databases, we store embeddings optimized for fast retrieval, ensuring the system performs well under large-scale demands.
Data Preparation: Tokenization, normalization, and comprehensive versioning processes ensure that your data is ready for training or real-time applications.
Live Data Pipelines: For RAG applications, we set up live pipelines and data connectors, ensuring that your AI models have access to up-to-date, accurate data in real time.
What is data engineering, and why is it essential for AI?
Data engineering involves designing systems to collect, store, and process data efficiently. It's critical for AI because well-prepared data ensures that AI models function at their best, delivering accurate and relevant results.
How does Zangoh ensure secure data pipelines?
Zangoh uses enterprise-grade encryption, access controls, and compliance measures such as GDPR and HIPAA to ensure your data is securely processed and stored.
What types of data do you collect for AI training?
We collect contextualized data from enterprise systems using automated scripts. This helps reduce AI hallucinations and ensures outcomes are relevant to the specific business at hand.
How scalable are your data engineering solutions?
Our data pipelines are built for scalability, using vector databases and scalable architectures that ensure fast and efficient data retrieval as your AI models grow in complexity.
Can you handle live data for real-time AI applications?
Yes, we specialize in setting up live data pipelines and connectors for RAG applications, ensuring your AI models have real-time access to the latest data
What tools and technologies do you use for data engineering?
We leverage industry-leading tools like Apache Kafka, Spark, Hadoop, and cloud-native solutions to build robust and scalable data pipelines.
How do you ensure data compliance with regulations?
We implement data encryption, access control, and versioning to ensure compliance with data protection regulations such as GDPR and HIPAA.
What industries benefit from Zangoh’s data engineering services?
Industries such as finance, healthcare, retail, and insurance benefit from secure, scalable data pipelines that enable better data-driven decisions and AI implementations.
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