Services
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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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