RAG for Legacy Data Silos Integration

Break down silos between mainframes, legacy databases, and document systems. We deliver a single, queryable interface that surfaces insights from decades of institutional knowledge without disrupting existing workflows.

Every AI-generated insight is traceable back to its source document and version. We build provenance tracking into the RAG pipeline, ensuring compliance with internal governance and external regulations like GDPR and SOX.

Move from manual document searches to instant, AI-powered answers. Our optimized retrieval pipelines provide sub-second responses, cutting the time employees spend hunting for information and accelerating decision cycles.

We architect with open-source frameworks like LlamaIndex and deploy on your infrastructure. You maintain full control over your data and models, avoiding costly per-query API fees and ensuring long-term architectural flexibility. Learn more about our approach to Open-Source Model RAG Optimization.

Deploy a system built for enterprise load. We implement caching, load balancing, and monitoring to ensure 99.9% uptime SLAs, whether serving 100 queries a day or 10,000 queries per hour across global teams.

Dramatically reduce AI hallucinations. By grounding responses in your proprietary data with advanced semantic chunking and hybrid search, we ensure answers are relevant, accurate, and actionable for your specific business context. This is a core component of our Enterprise Semantic Search RAG Development.

We build secure, high-fidelity data pipelines that connect directly to your legacy mainframes (IBM z/OS, AS/400), on-premise databases (Oracle, SQL Server), and document management systems (SharePoint, FileNet). This ensures zero data loss and maintains referential integrity during the migration to a vectorized knowledge base.

Our proprietary algorithms intelligently parse and chunk complex legacy documents—including scanned PDFs, COBOL copybooks, and EDI transactions—preserving hierarchical relationships and business logic. This context-aware chunking is critical for high retrieval accuracy in RAG systems.

We architect a centralized vector database (using Pinecone, Weaviate, or Milvus) that semantically links entities across all your legacy silos. This creates a single source of truth, enabling cross-database queries that were previously impossible, such as linking customer records from a mainframe to support tickets in a legacy CRM.

We deploy advanced hybrid search (vector + keyword + metadata) and query routing to ensure answers are strictly grounded in your legacy data. Our systems include source citation and confidence scoring, dramatically reducing AI hallucination rates for mission-critical business intelligence.

Our pipelines support real-time or batch incremental updates from source systems, ensuring your RAG knowledge base is always current. This event-driven architecture, often using Kafka or Change Data Capture (CDC), allows AI agents to act on the latest transactional data.

We enforce existing row-level and column-level security policies from your legacy systems within the new RAG infrastructure. All data is encrypted in transit and at rest, with audit trails for every query, ensuring compliance with SOC 2, HIPAA, and GDPR standards.