Bridge the gap between raw data structures and high-performance LLM / RAG architectures. Eliminate bottlenecks, optimize queries, and scale with confidence.
Great AI models fail on poor data foundations. We optimize your relational and vector databases for maximum LLM performance.
Deep analysis powered by state-of-the-art LLMs, reviewed and fine-tuned by experienced database architects.
Prepare schemas for pgvector, Pinecone, Qdrant, and hybrid search pipelines to boost context recall.
Analyze DDL schemas and query logs without uploading sensitive customer records. Zero privacy compromise.
From free instant scores to fully automated digital reports and high-ticket architectural reviews.
Automated score (0-100) assessing schema readiness, vector potential, and bottleneck risks.
Blueprint templates, DDL best practices, and RAG optimization guides for modern DBs.
Upload slow queries or execution logs. Receive rewritten queries & indexing recommendations.
Step-by-step roadmap for migrating SQL data into pgvector, Pinecone, Qdrant, or Milvus.
Comprehensive 15+ page report analyzing schema, indexing, security, and LLM readiness.
Generate 10,000+ GDPR-compliant synthetic rows based on your DDL for secure AI testing.
60-minute strategic deep dive with a senior Database & AI Architect to solve complex blockers.
Automated monthly health audits tracking schema drift, slow queries, and vector efficiency.
Paste your anonymized SQL DDL schema or table structure below. Our AI engine will evaluate your database readiness for RAG, LLM integrations, and index efficiency in real-time.
Have questions regarding an audit or need a custom enterprise solution? Send us a message directly to info@aidatacheckup.com.