Transform your legacy mainframes, databases, and document archives into a unified, queryable knowledge base without disrupting existing workflows.
Service
Architecture review before implementation
Implementation scope and rollout planning
Clear next-step recommendation
Unlock actionable intelligence from fragmented legacy systems and document silos with purpose-built RAG infrastructure.
Transform your legacy mainframes, databases, and document archives into a unified, queryable knowledge base without disrupting existing workflows.
We architect RAG systems that bridge decades of technological debt:
Oracle, IBM DB2, SAP, and proprietary mainframe systems.Our approach ensures deterministic accuracy from probabilistic models:
Stop letting data age in place. Explore our core RAG Infrastructure capabilities or learn how we ensure data sovereignty with Sovereign AI Development.
We transform your legacy data from a compliance burden into a competitive asset. Our integration service delivers measurable business results, not just technical implementation.
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 de-risk your legacy data integration project through a structured, milestone-driven methodology. Each phase delivers tangible value and a clear off-ramp, ensuring alignment and control.
| Phase & Deliverables | Discovery & Assessment | Pilot & Validation | Full Integration & Scaling |
|---|---|---|---|
Core Objective | Risk & Feasibility Analysis | Proof-of-Concept Validation | Enterprise-Wide Deployment |
Key Activities | Data Source AuditSchema Mapping AnalysisSecurity & Compliance Review | Connector Development for 1-2 SilosInitial Vector Index CreationAccuracy Benchmarking | Full Connector Suite DeploymentAutomated Pipeline OrchestrationPerformance & Security Hardening |
Primary Output | Technical Blueprint & ROI Model | Working Pilot with Measured KPIs | Production RAG System with SLA |
Timeline | 2-3 Weeks | 4-6 Weeks | 6-10 Weeks |
Team Involvement | Our ArchitectsYour SMEs | Our EngineersYour DevOps | Our Team + Your Team Knowledge Transfer |
Success Metrics Defined | Cost/Benefit Analysis, Hallucination Baseline | < 100ms Retrieval Latency> 85% Answer RelevanceSource Citation Accuracy | 99.9% Uptime SLAAutomated Data SyncFull Audit Trail |
Investment | Fixed Fee | Fixed Fee | Custom Scope-Based |
We transform fragmented, legacy data into a unified, intelligent knowledge layer. Our service delivers accurate, source-grounded AI responses by connecting your proprietary databases and document silos to modern LLMs without disrupting existing business workflows.
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.
Enabling Efficiency, Speed & Accuracy
We build AI systems for teams that need search across company data, workflow automation across tools, or AI features inside products and internal software.
Common questions from CTOs and engineering leads about integrating RAG with legacy databases, mainframes, and document systems.
Standard deployments take 2-4 weeks from kickoff to MVP. This includes data source assessment, semantic chunking strategy, and initial pipeline integration. Complex environments with 10+ disparate legacy systems (e.g., mainframes, AS/400, Lotus Notes) typically require 6-8 weeks for full production deployment. We provide a detailed project plan in the first week.
About the author
CEO & MD, Inference Systems
Prasad Kumkar is the CEO & MD of Inference Systems and writes about AI systems architecture, LLM infrastructure, model serving, evaluation, and production deployment. Over 5+ years, he has worked across computer vision models, L5 autonomous vehicle systems, and LLM research, with a focus on taking complex AI ideas into real-world engineering systems.
His work and writing cover AI systems, large language models, AI agents, multimodal systems, autonomous systems, inference optimization, RAG, evaluation, and production AI engineering.
How We Work
One-fit-all AI don't work for modern businesses. At Inferensys, we aim to understand your business & custom requirements; which we use to define most efficient agentic workflows, the data, and the tools for your business.
The first call is a practical review of your use case and the right next step.
