Services
Retrieval-Augmented Generation (RAG) Infrastructure
Retrieval-Augmented Generation (RAG) Infrastructure
Architecture of scalable, accurate RAG systems augmenting probabilistic language models with deterministic, trusted enterprise knowledge bases using complex vector database engineering and semantic chunking strategies. Sub-services include vector database architecture consulting, enterprise semantic search RAG, real-time RAG pipeline development, and RAG optimization for legacy data silos.
Vector Database Architecture Consulting
Design and implementation of high-performance vector search infrastructure using Pinecone, Weaviate, or Milvus, optimizing for sub-100ms query latency and seamless integration with existing enterprise data lakes and LLM APIs.
Real-Time RAG Pipeline Engineering
Development of event-driven RAG systems that ingest and index streaming data from Kafka, Kinesis, or WebSockets, enabling live knowledge updates and sub-second response times for dynamic enterprise environments.
Multi-Modal RAG System Development
Engineering of RAG pipelines that process and retrieve across text, images, audio, and video using CLIP embeddings and cross-modal encoders, unlocking insights from unstructured multimedia archives.
RAG for Legacy Data Silos Integration
Migration and unification of fragmented enterprise knowledge from legacy databases, mainframes, and document management systems into a coherent, queryable RAG infrastructure without disrupting existing workflows.
RAG Performance Optimization Service
Specialized tuning of retrieval accuracy and latency through advanced chunking strategies, hybrid search algorithms, and query routing to reduce hallucination rates by over 40% and improve answer relevance.
Enterprise Semantic Search RAG Development
Building domain-aware search systems that understand business jargon and context, leveraging knowledge graphs and entity recognition to deliver precise, actionable answers from internal wikis and documentation.
Low-Latency RAG API Development
Creation of production-grade, scalable APIs with gRPC or GraphQL endpoints, featuring caching layers, request batching, and load balancing to serve high-volume enterprise applications with 99.9% uptime SLAs.
RAG-Enabled Chatbot Development
End-to-end development of intelligent assistants powered by accurate, source-grounded RAG, integrating with Slack, Teams, and web interfaces to automate customer support and internal help desks.
Hybrid Cloud RAG Deployment
Architecture and deployment of RAG systems across public cloud, private data centers, and edge locations, ensuring data sovereignty, cost efficiency, and resilient performance under variable load.
Open-Source Model RAG Optimization
Fine-tuning and deployment of RAG pipelines using LlamaIndex, LangChain, and open-source LLMs like Llama 3 or Mistral, reducing API costs and vendor lock-in while maintaining high accuracy standards.
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How We Work
Custom AI workflows for your Business
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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Review the use case
We understand the task, the users, and where AI can actually help.
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Pick the right approach
We define what needs search, automation, or product integration.
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Build the first useful version
We implement the part that proves the value first.
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Improve from there
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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