Your AI Answers From
Your Knowledge.
Operational RAG systems that connect LLMs to your documents, databases, and internal knowledge. Agix Technologies provides RAG AI services with verified citations, role-based access control, and zero hallucinations.
RAG & Knowledge AI Market Momentum
RAG, AI That Knows What You Know
Retrieval-Augmented Generation (RAG) connects AI models to proprietary knowledge, allowing them to retrieve relevant, up-to-date information from documents and enterprise data instead of relying only on training data.
With RAG AI services, relevant context is provided before generating answers, supporting accurate responses with source attribution, access controls, and data freshness.
“A hallucinating AI isn't just wrong, it's a liability. RAG with proper grounding is what makes AI safe to deploy on your most sensitive knowledge.”
From Documents → Search → Retrieval → Grounded Answers
Six RAG Capabilities.
One Production System.
Every pipeline we build is tested for retrieval precision, grounded against hallucination, and monitored for knowledge freshness in production.
Where RAG Moves the Needle Most
We've deployed RAG AI services across financial services, healthcare, legal, logistics, and enterprise operations. Domain-specific RAG can support these workflows while keeping retrieval grounded in current business knowledge. ROI is typically visible within 60 days.
Discuss Your Use CaseRAG That Survives Production
Most RAG demos work on clean PDFs. Real production systems face messy data, access control, stale content, and thousands of concurrent queries. Our RAG AI services are built for that reality.
From Raw Documents to
Production RAG in 8–12 Weeks
Our RAG development services follow a milestone-driven process with a working retrieval baseline by week 3, not a system you see for the first time at go-live.
Inventory all knowledge sources; documents, wikis, databases, APIs. Assess format diversity, access permissions, and update frequency. Define the query types the system must handle.
Build document parsers for each content type. Tune chunking strategy and embedding model. Populate vector database with metadata-tagged chunks. Deliver a retrieval baseline you can test.
Evaluate recall@K against real queries. Layer hybrid BM25 + vector search and cross-encoder re-ranking. Integrate LLM with grounding prompts and citation extraction. Implement RBAC at the retrieval layer for secure RAG model integration.
Deploy chat UI or REST API. Connect to your existing tools; Slack, Teams, CRM, helpdesk. Build admin panel for knowledge source management and query analytics.
Go live with full observability; retrieval quality metrics, query latency, citation accuracy. Automated sync pipeline keeps the index fresh as documents change.
Industry-Leading Tools for RAG Pipelines
Pricing That Fits Your Project
Knowledge discovery, ingestion, clean chunking, vector indexing, secure RAG pipeline, and internal chat UI with source-linked answers.
Multi-source ingestion, advanced chunking, role-based access control, monitoring, and API + UI integration.
Large-scale ingestion, strict governance, private LLM grounding, multi-role access control, and high availability with full audit logging.
All pricing is project-based. You own the IP, source code, and all systems we build. Contact us for a scoped estimate.
Real-World RAG and Knowledge AI Success Stories
FintechHow Agix Technologies engineered an end-to-end AI document processing system that achieves 99.5% extraction accuracy across 5.7M+ financial…
The Challenge
Processing financial documents manually, income verification, deposit verification, tax return analysis, is slow,…
The Outcome
Measured 90 days post-deployment against pre-deployment baselines.
Extraction Accuracy
Avg Processing Time
Financial IntelligenceAgix Technologies built the NLP and semantic search layer that powers AlphaSense, replacing weeks of analyst research with minutes by…
The Challenge
Financial analysts at major investment firms spend 40–60% of their time reading, summarizing, and cross-referencing…
The Outcome
Measured across AlphaSense enterprise deployments serving investment and strategy teams.
Signal Accuracy
Research Time
Higher EducationAgix Technologies built Dartmouth College's AI IT Helpdesk, a RAG-driven support engine integrated with Active Directory, campus identity…
The Challenge
Dartmouth's IT challenge wasn't complexity, it was volume. The same questions, asked thousands of times, by students…
The Outcome
Measured across Fall 2024 – Spring 2025 academic year, against the same period prior year. 18,842 total tickets…
Ticket Volume
Avg Resolution
Insights on RAG and Knowledge AI
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Read articleRAG Pipeline FAQs, Answered by Experts
Yes. We implement role-based access control at the retrieval layer, not just at the UI. Before any document chunk is returned to the LLM, we verify the requesting user has permission to view that document. This means users can never receive answers grounded in content they're not authorized to see, even indirectly.
100%. The vector index, ingestion pipeline, retrieval API, and all application code are yours. We deploy to your cloud account (AWS, Azure, GCP, or on-premise) and hand off full documentation. There is no ongoing vendor lock-in, you can maintain, extend, or migrate the system independently after handoff.
Fine-tuning changes how a model behaves based on training examples, while RAG provides current external knowledge at inference time. For frequently changing business information, RAG delivers more practical data access without retraining the underlying model.
Agentic RAG combines retrieval with AI agents that can plan, select information sources, and perform multi-step tasks. Traditional RAG responds to a defined query, while Agentic RAG as a Service dynamically retrieves information as part of a broader, more complex workflow.
Yes. Multimodal RAG can combine text, images, tables, and other structured or unstructured sources, so retrieval is not limited to conventional text documents. The right approach depends on your source formats and retrieval requirements.
Pricing depends on data volume, source complexity, retrieval requirements, integrations, security controls, and deployment model. Agix Technologies projects currently range from $8,000–$75,000+ depending on scope. RAG as a Service can also be structured around your required infrastructure and ongoing management needs.
The typical Agix Technologies delivery timeline is 8–12 weeks, beginning with a knowledge audit and moving through ingestion, retrieval optimization, integration, deployment, and monitoring. More complex environments may require additional time based on data volume and governance requirements.
Your Knowledge. Your AI. Zero Hallucinations.
Book a free knowledge audit and we'll tell you exactly what retrieval accuracy is possible with your current documents, before you commit.