Deploy Haystack AI
of 
Search
We use the modular and production-focused Haystack AI framework by deepset to build highly reliable and configurable search and Question Answering (QA) systems. Our expertise centers on leveraging its advanced RAG components and pre-built pipelines to deliver superior, ready-to-deploy, extractive and generative search applications for enterprise clients
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Modular Search Frameworks for Real-World Data
Haystack is an open-source framework designed to help developers build powerful, customized search and QA systems using Large Language Models. Unlike other frameworks, Haystack prioritizes building complex data flows (Pipelines) using modular components (Nodes) that focus on the retrieval, processing, and generation stages. This component-driven approach makes it ideal for building industrial-grade search solutions.
Search Experiences that Discover and Explain
Compliance, research, and support - Haystack powers meaningful discovery.
Compliance Monitoring
Instantly query regulatory documents and cite relevant sections for real-time compliance checks.
Customer Support Automation
A bot retrieves answers from product manuals and FAQs, enhancing customer support efficiency.
Pharmaceutical Research
Search engine for accessing and analyzing complex scientific literature in real-time.
Media Monitoring
Summarizes news from a large document corpus, keeping you up-to-date on global events.
Legal E-Discovery
Extracts specific facts and dates from litigation documents, streamlining legal research.
Technology Stack
Haystack integrating OpenAI, Hugging Face, and Elasticsearch for advanced enterprise search capabilities.
Scalable Search Systems
That Empower Discovery end-to-end hybrid search pipelines engineered for reliability and scale.
Custom RAG Pipelines
Utilize advanced Nodes like re-rankers and filters for efficient data processing.
Extractive Question Answering Systems
Cite exact document passages for verification using advanced Readers.
Enterprise Semantic Search Engines
Replace keyword search tools with document stores for accurate semantic search.
Hybrid Search Implementations
Combine BM25 and vector search for enhanced, context-aware results.
Document Processing Workflows
Clean, split, and index new data for faster and more accurate retrieval.
Our Journey of Making Great Things
Clients Served
Projects Completed
Countries Reached
Awards Won
The Engine behind modern Enterprise Search
Flexible, transparent, and optimized for real-world scale.
Production-
Ready Pipelines
Hybrid Search Mastery
Clear Data
Flow
Modular Component System
Extractive QA Focus
What Our Clients Say About Us
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Parliament of India
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Jatin Kapoor
Founder, Credeb Advisors LLP
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Frequently Asked Questions
Its pipeline-based architecture supports modular retrievers, rankers, readers, and generators that can be tuned for high-volume deployments.
Yes. Haystack supports hybrid search (BM25 + embeddings), improving recall and relevance for real-world datasets.
No. It integrates with OpenAI, HuggingFace models, and custom on-premise models for flexible deployment.
Yes. Readers can pull exact passages from documents, enabling auditable and compliant search responses.
Document stores and pipelines allow efficient chunking, indexing, and re-indexing for dynamic datasets.