Question 1

What is a vector database and why do AI applications need one?

Accepted Answer

A vector database stores data as high-dimensional numerical embeddings — mathematical representations of text, images, or other content — and enables fast similarity search across millions of records. AI applications need vector databases for RAG (Retrieval-Augmented Generation), semantic search, recommendation systems, and duplicate detection. Unlike traditional databases that match exact values, vector databases find semantically similar content: a search for 'quarterly revenue forecast' retrieves documents about 'Q3 financial projections' even if the exact phrase doesn't appear. Pinecone, Weaviate, and pgvector are the most common choices depending on your scale and existing infrastructure.

Question 2

What does private AI deployment mean and when is it needed?

Accepted Answer

Private AI deployment means running AI models entirely within your own infrastructure — on-premises servers, a private cloud VPC, or an air-gapped environment — so that your data and model outputs never traverse a public API or leave your security perimeter. It is required when data sovereignty regulations prohibit cloud processing of sensitive data (common in healthcare, financial services, and defence), when your data contains trade secrets or personally identifiable information subject to GDPR or HIPAA, or when your information security policy requires that AI inference happens on infrastructure you fully control. Norvik deploys open-source models (Llama 3, Mistral) on your GPU infrastructure with the same performance characteristics as cloud-hosted proprietary models.

Question 3

How do you handle GDPR and HIPAA compliance in AI infrastructure design?

Accepted Answer

GDPR and HIPAA compliance is embedded into the infrastructure architecture from the start, not added as a retrofit. For GDPR, this means data minimisation in pipeline design, pseudonymisation of personal data before it reaches AI models, documented lawful basis for each data processing activity, and the technical means to honour right-to-erasure requests across vector databases and model training data. For HIPAA, this means Business Associate Agreements with every cloud service in the data path, encryption at rest and in transit, audit logging of all PHI access, and network segmentation that isolates AI workloads from non-compliant environments. Norvik delivers a data processing register and compliance documentation package as a standard deliverable.

Question 4

Which cloud platforms do you support for AI infrastructure?

Accepted Answer

Norvik builds AI infrastructure on AWS, Google Cloud Platform, and Microsoft Azure, as well as hybrid and on-premises configurations. AWS deployments typically use SageMaker for model serving, Bedrock for managed LLM access, and S3 and Glue for data pipelines. GCP deployments use Vertex AI, BigQuery ML, and Cloud Composer. Azure deployments use Azure OpenAI Service, Azure ML, and Azure Data Factory. We are cloud-agnostic by design — architecture decisions are driven by your existing contracts, team expertise, and regulatory requirements, not vendor partnerships.

Question 5

What is a data pipeline for AI and how is it different from a standard ETL pipeline?

Accepted Answer

An AI data pipeline extends a standard ETL pipeline with AI-specific processing steps: embedding generation, feature engineering for model training, data quality scoring, and lineage tracking at the feature level rather than just the record level. Where a standard pipeline moves data from A to B in a known format, an AI pipeline must also handle unstructured data (documents, images, audio), produce consistent embeddings as input data drifts, version features alongside model versions, and trigger retraining pipelines when upstream data distribution shifts. Norvik builds AI data pipelines using Apache Airflow for orchestration, dbt for transformation, and custom embedding layers that integrate with your vector database and model training infrastructure.

AI Data Strategy & Infrastructure for Enterprise

What's included in AI Data & Infrastructure

Data Strategy for AI

Vector Database Setup

AI Cloud Infrastructure

Data Pipelines

Private & Secure AI

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Technology-agnostic.Outcome-obsessed.

Vector DB

Pipeline

Data Warehouse

Infrastructure

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Common questions about AI Data & Infrastructure

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