Our AI Knowledge Discovery & Retrieval Platform enables organizations to search and retrieve insights from millions of heterogeneous documents with unmatched accuracy and speed.
Using advanced semantic search, multimodal retrieval, and RAG (Retrieval-Augmented Generation), it delivers precise, citation-backed answers across text, tables, and figures. The platform supports both a web dashboard and APIs, making knowledge discovery accessible, secure, and scalable for research-intensive environments.
Keyword-based search was inadequate for large-scale repositories.
Content existed in varied formats (text, tables, figures) requiring multimodal AI.
Tens of millions of documents demanded low-latency, scalable architecture.
Enterprises required security, monitoring, and seamless integration.
We built a comprehensive AI-powered retrieval system that ingests and indexes massive content repositories, applies semantic embeddings, and delivers citation-grounded answers. It combines OCR, layout parsing, and advanced ANN vector search with modular APIs – ensuring accuracy, scalability, and enterprise-grade compliance.
Vector DBs: Milvus / Chroma
Indexing Methods: HNSW, PQ, hierarchical partitioning
AI Models: OCR + LayoutLMv3, Transformers for semantic embeddings
Backend: Python APIs, REST services
Infrastructure: Cloud-native, scalable, role-based IAM
Frontend: Web-based UI with monitoring & dashboards
Delivers context-aware, citation-backed answers, ensuring trust and reliability in research and enterprise settings.
Handles text, tables, and figures seamlessly, making it valuable for diverse research and knowledge management scenarios.
Supports 100M+ documents using sharding, partitioning, and compression for cost-effective scaling.
Processes new documents continuously, ensuring up-to-date results for users across dynamic repositories.
Provides both programmatic access and an intuitive UI, serving researchers, admins, and enterprise portals.
Captures search histories, performance metrics, and feedback for continuous improvement.
Retrieve precise, citation-linked answers in seconds, cutting down research cycles by up to 60%.
Curate, audit, and monitor content collections with structured metadata and quality control workflows.
Benefit from modular, API-driven architecture with secure deployment across cloud or on-premises.
Ensure secure, role-based access and verifiable, auditable results across sensitive repositories.
Leverage faster knowledge access to support data-driven strategies and innovation pipelines.
Enable personalized, accurate responses in portals or apps, enhancing trust and satisfaction.
Book a live demo to explore semantic, citation-backed search across millions of documents.
Reduced search effort by 60% for research-intensive teams.
Delivered millisecond-level latency on 10M+ documents.
Optimized infrastructure costs by 25% through smart indexing.
Boosted trust and adoption with 100% citation-backed responses.
This solution surpasses the limitations of traditional keyword-based search by combining semantic embeddings, multimodal parsing, and RAG to provide accurate, verifiable, and scalable knowledge retrieval.
Q 1. How is this different from traditional search engines?
It goes beyond keywords, using semantic embeddings and RAG for context and citations.
Q 2. Does it support multimodal content?
Yes, it handles text, tables, figures, and OCR-processed content.
Q 3. Is it scalable for 100M+ documents?
Yes, proven with vector sharding, partitioning, and compression.
Q 4. Can it integrate with our existing systems?
Yes, via REST APIs and modular architecture.
Q 5. How secure is the platform?
It includes role-based access, enterprise IAM, and compliance guardrails.
