Where SoftsensorX began — retrieval-augmented generation, NLP and conversational AI. RAG-Fusion and agentic retrieval, semantic search, and streaming chat that answers from your documents and data, not the model's memory — every response traceable to its source.
A raw LLM invents plausible answers with no grounding — unacceptable over legal, financial, clinical or brand-critical content.
Decades of documents, talks and articles that no one can actually query conversationally.
Answers with no citation can't be trusted or audited — users need to see exactly where each one came from.
Advanced retrieval, grounded generation and conversational AI — engineered for production.
Grounded retrieval-augmented generation with multi-query RAG-Fusion, hybrid semantic + keyword search, metadata filtering and re-ranking for precise, source-linked answers.
Multi-step agent pipelines that plan, retrieve, compare-and-contrast and synthesize detailed answers across large corpora — reasoning, not just lookup.
WebSocket streaming assistants with chat memory and multi-tenant namespaces — real-time conversational access to your knowledge base.
Vector search on Pinecone with LlamaIndex and sentence-transformers — chunking, embedding and indexing strategies tuned to your content.
Prompt engineering, fine-tuning, guardrails and evaluation harnesses — provider-agnostic across OpenAI, GLM, DeepSeek and open models, cost-observed.
Chat over PDFs, images and mixed content with page-level grounding — pairs with Document AI and Computer Vision.
A conversational assistant answering questions grounded in a global foundation's vast library of talks and teachings — semantic retrieval, streaming chat, source-linked answers.
Read the case study →Grounded RAG with OCR answering questions over 2,000+ page scanned bond documents — zero hallucination, PDF-level traceability.
Read the case study →Vector search and AI chunking turned poor keyword search into contextual discovery with 90%+ match accuracy.
Read the case study →Company research across public and private sources — prospect research cut from 3–4 hours to minutes with 45+ data points.
Read the case study →GPT-powered file search, summaries and metadata tagging — search cut by 98.9%, manual sorting reduced by 90%.
Read the case study →GPT-4o counselling, document processing and video-interview scoring across the admissions funnel.
Read the case study →RAG grounds an LLM's answers in your own documents and data rather than its training memory — so responses are current, verifiable and traceable to a source. It's the reliable way to put LLMs over enterprise knowledge without hallucination.
RAG-Fusion multi-query retrieval, agentic RAG pipelines, hybrid semantic + keyword search with metadata filtering and re-ranking, streaming conversational chat with memory, and guardrails — built with LangChain, LlamaIndex, Pinecone, sentence-transformers and OpenAI or open models.
Answers are generated strictly from retrieved, source-linked context, with re-ranking, confidence signals, guardrails and evaluation — every response traces back to the passage it came from.
Yes — we build conversational assistants and semantic search over document, web and multimodal corpora, with multi-tenant namespaces and streaming responses, deployed on your cloud.
Tell us the corpus and the questions you need answered — we'll bring the engineers who've built grounded RAG since SoftsensorX started.