Database, Data Engineering, Cloud & AI consulting since 2007
AI & Data
Turn your organization's data into trustworthy AI capabilities - with secure data platforms, retrieval-augmented generation and the infrastructure to run them reliably.
Generative AI models are powerful, but on their own they know nothing about your customers, products, contracts or procedures. The value comes from connecting models to accurate, current and properly secured business data - and that is where most AI initiatives succeed or stall.
AVRIN brings nearly two decades of database and data engineering experience to AI projects. We help you identify practical use cases, prepare and govern the data, choose between cloud AI services and self-hosted models, and build solutions that respect existing security and compliance controls.
We favor small, measurable pilots that prove value quickly and then scale on a platform your team can operate - rather than large experiments that never reach production.
Assistants, document summarization, knowledge search, classification and data extraction using Azure OpenAI, Amazon Bedrock, Google Vertex AI or open models - integrated into your applications and workflows.
RAG retrieves relevant passages from your documents and databases and passes them to the model, so answers are grounded in your data, cite sources and respect user permissions.
Embeddings stored and searched in SQL Server 2025's native vector type, PostgreSQL with pgvector, Azure AI Search, Amazon OpenSearch or dedicated vector stores - chosen for your scale and existing platform.
Clean, well-modeled and governed data in lakehouses and warehouses - Databricks, Snowflake, Microsoft Fabric or SQL Server - with catalogs, lineage and access controls that AI solutions can rely on.
Pipelines that extract, clean, chunk and embed content, keep vectors in sync with source changes, and feed features to machine learning models - monitored like any production workload.
Right-sized GPU and CPU compute, storage and networking in the cloud or on-premises, model hosting and serving, observability, and cost management for inference and training workloads.
How RAG works
Documents and records are cleaned, split into chunks and converted to embeddings.
Embeddings are stored in a vector index with metadata and access permissions.
Each question is embedded and matched to the most relevant chunks the user is allowed to see.
The model answers using the retrieved context and cites its sources.
SQL Server 2025 introduces a native VECTOR data type and VECTOR_DISTANCE function, with DiskANN-based vector indexes available in preview. Teams can keep embeddings next to relational data and reuse existing security, backup and high availability.
Read: SQL Server 2025 vector search and RAG
Prioritize use cases by value, feasibility and risk.
Assess data quality, access and governance gaps.
Build a focused solution and measure answer quality.
Harden, monitor and roll out on a supportable platform.
Talk to AVRIN about AI-ready data platforms, RAG pilots and secure generative AI integration.
