Service
Break free from vendor lock-in and unpredictable API costs with optimized open-source RAG infrastructure.
Closed-source RAG services create two critical business risks:
Open-source RAG optimization replaces variable costs with predictable infrastructure, reducing total cost of ownership by 60-80%.
We architect and deploy high-performance RAG using LlamaIndex, LangChain, and open-source LLMs like Llama 3 or Mistral. This delivers:
This approach is foundational for secure, scalable enterprise AI. Learn more about our broader Retrieval-Augmented Generation (RAG) Infrastructure capabilities.
Beyond cost, open-source RAG enables deep technical control critical for compliance and performance:
Our engineering focus delivers concrete business value by reducing costs, accelerating deployment, and ensuring long-term control over your AI infrastructure.
Deploy RAG pipelines with open-source LLMs like Llama 3 and Mistral, cutting reliance on expensive, proprietary APIs. We architect for cost predictability and long-term infrastructure sovereignty.
Leverage our battle-tested frameworks and pre-built components for LlamaIndex and LangChain. We deliver optimized, scalable pipelines, not proof-of-concepts, accelerating your time-to-value.
Fine-tune retrieval and generation components on your proprietary data. We implement advanced chunking, re-ranking, and hybrid search to reduce hallucination rates and improve answer relevance by over 40%.
Keep sensitive enterprise data within your controlled environment. Our open-source RAG deployments are architected for air-gapped networks and can be designed to comply with GDPR, HIPAA, and the EU AI Act.
We build for high-volume, low-latency demands. Our pipelines feature efficient vector indexing, caching strategies, and load balancing to maintain sub-second response times under enterprise load.
Receive clean, documented, and modular codebases. We ensure your team can easily extend, maintain, and swap components (models, retrievers) as the open-source ecosystem evolves, protecting your investment.
A clear breakdown of our phased approach to optimizing your open-source RAG pipeline, from initial assessment to production deployment and ongoing support.
| Phase & Key Deliverables | Starter (4-6 Weeks) | Professional (8-10 Weeks) | Enterprise (12+ Weeks) |
|---|---|---|---|
Initial RAG Architecture Audit & Gap Analysis | |||
Custom Chunking & Embedding Strategy Design | |||
Vector Database Selection & Schema Optimization (Pinecone/Weaviate/Milvus) | Basic Configuration | Advanced Tuning & Hybrid Search | Multi-Cluster, Geo-Distributed Architecture |
Open-Source LLM Fine-Tuning (Llama 3, Mistral) | Lightweight Adapter | Full Parameter Fine-Tuning | Multi-Model Ensemble & A/B Testing Framework |
Retrieval Pipeline Optimization (LlamaIndex/LangChain) | Core Pipeline | Advanced Query Routing & Reranking | Agentic, Self-Correcting Retrieval Logic |
Performance Benchmarking & Hallucination Reduction | Basic Accuracy Tests | Comprehensive Latency & Accuracy Benchmarks (>40% Reduction Target) | Continuous Monitoring Dashboard & Automated Drift Detection |
Production API Development (FastAPI/gRPC) & Deployment | Single-Endpoint API | Scalable API with Caching & Load Balancing | Multi-Region Deployment with 99.9% Uptime SLA |
Security & Compliance Review | Basic Data Handling Audit | Full Security Penetration Testing | Integration with Enterprise AI Governance Frameworks |
Knowledge Transfer & Developer Training | Documentation & Handoff | 2 Workshops & Technical Documentation | Dedicated Engineering Support & Quarterly Reviews |
Ongoing Support & Maintenance | 30 Days Post-Launch | 6-Month Optional SLA | 12-Month Dedicated Engineer & Proactive Optimization |
We specialize in fine-tuning and deploying high-performance RAG pipelines using LlamaIndex, LangChain, and open-source LLMs like Llama 3 and Mistral. Our approach delivers enterprise-grade accuracy while cutting API costs and preventing vendor dependency.
We architect and optimize end-to-end RAG workflows using industry-standard frameworks. This includes custom query engines, advanced retrieval strategies, and agent orchestration to ensure high accuracy and maintainability. Learn more about our approach to RAG Performance Optimization.
Deploy and fine-tune models like Llama 3, Mistral, and Phi-3 for your specific domain. We reduce reliance on expensive, closed APIs by optimizing open-source models for your knowledge base, ensuring cost control and data privacy. Explore our work with Domain-Specific Language Models.
Go beyond basic vector search. We implement hybrid retrieval (dense + sparse), hierarchical chunking, and query expansion to dramatically improve answer relevance. Our strategies connect disparate data, similar to our RAG for Legacy Data Silos Integration service.
We build scalable, monitored RAG APIs ready for enterprise traffic. This includes containerization with Docker, orchestration with Kubernetes, CI/CD pipelines, and comprehensive logging/alerting to guarantee 99.9% uptime SLAs in production.
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.
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Give teams answers from docs, tickets, runbooks, and product data with sources and permissions.
Useful when people spend too long searching or get different answers from different systems.
Use AI to route work, draft outputs, trigger actions, and keep approvals and logs in place.
Useful when repetitive work moves across multiple tools and teams.
Build assistants, guided actions, or decision support into the software your team or customers already use.
Useful when AI needs to be part of the product, not a separate tool.
Answers to common questions about optimizing and deploying cost-effective, high-accuracy RAG systems using open-source frameworks and models.
A standard deployment takes 2-4 weeks from initial architecture review to production-ready API. This includes 1 week for assessment and design, 1-2 weeks for core pipeline development with LlamaIndex or LangChain, and 1 week for integration, testing, and documentation. Complex integrations with legacy systems or custom model fine-tuning can extend this to 6-8 weeks. We provide a detailed project plan with weekly milestones at kickoff.
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.
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We understand the task, the users, and where AI can actually help.
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We define what needs search, automation, or product integration.
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We implement the part that proves the value first.
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We add the checks and visibility needed to keep it useful.
Read moreThe first call is a practical review of your use case and the right next step.
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