Retrieval-Augmented Generation (RAG) Services

Deliver accurate, compliant, and citation-backed outputs by connecting AI to your enterprise data.

Radiansys builds enterprise-grade Retrieval-Augmented Generation (RAG) systems that connect AI models to your internal data, ensuring every response is accurate, explainable, and audit-ready.

Integrate LLMs with CRMs, ERPs, and document repositories to deliver real-time, context-rich answers.

Implement hybrid search, chunking, and vector indexing to surface verified insights from enterprise data.

Generate source-linked outputs that meet enterprise security and governance standards.

Deploy RAG pipelines on private or hybrid clouds with monitoring, cost controls, and full auditability.

How We Implement RAG Pipelines

At Radiansys, our RAG implementation approach blends deep AI engineering with a clear focus on enterprise reliability and compliance. We design each RAG pipeline to transform unstructured data into usable, source-cited intelligence that integrates seamlessly into business workflows. Beyond retrieval accuracy, we prioritize explainability, governance, and long-term scalability, ensuring every deployment is production-ready and future-proof.

Retrieval Modes

We combine semantic and keyword (hybrid) retrieval enhanced with metadata filters, vector similarity, and time-aware ranking. This multi-layered architecture ensures that responses remain contextually relevant, temporally accurate, and fully aligned with user intent. By balancing semantic precision and metadata filtering, our RAG pipelines deliver pinpoint results even in complex, compliance-heavy environments.

01

Chunking

Our preprocessing pipeline applies token-aware, recursive, and overlapping chunking to preserve narrative coherence within documents. We also dynamically adjust chunk size based on document type—policy manuals, research papers, or transactional records—to maintain clarity and prevent context drift. This ensures that retrieved passages retain semantic continuity, supporting accurate and grounded LLM generation.

02

Reranking

To achieve enterprise-level precision, we implement reranking models like Cohere, bge, and cross-encoders that refine search results beyond initial vector similarity. Each retrieved segment is evaluated on domain relevance, source authority, and recency before final selection. This additional layer of intelligence improves factual grounding, reduces hallucinations, and prioritizes high-confidence sources for LLM responses.

03

Prompting

Our prompting framework introduces guardrails that standardize how queries interact with retrieved knowledge. We use query rewriting, structured instruction templates, and domain-specific system prompts to maintain brand tone, accuracy, and compliance. The result is a stable and controllable generation process that aligns AI outputs with internal communication and governance standards.

04

Answering

We focus on delivering traceable, source-linked answers with dynamic citation generation and fallback logic for unanswerable queries. The pipeline consolidates multi-source responses into cohesive summaries while preserving reference integrity. This transparency builds user trust and creates audit-ready documentation suitable for regulated industries such as healthcare, finance, and legal services.

05

Evaluation

Every RAG deployment undergoes iterative testing for factual faithfulness, latency, and cost efficiency. We integrate automated evaluation loops that benchmark precision and recall across datasets, ensuring continuous optimization. Periodic retraining and metric-based tuning keep retrieval performance aligned with evolving data and compliance requirements.

06

Governance

Our RAG governance layer embeds security and compliance into every phase of the pipeline. We apply PII masking, encryption, role-based access control, and audit logging to meet SOC2, HIPAA, and GDPR standards. Additionally, we enable detailed monitoring dashboards that allow teams to track query patterns, accuracy trends, and data access, ensuring transparent oversight at scale.

07

Use Cases

Knowledge Assistants

Empower employees to quickly find answers from internal policies, SOPs, and training documents. RAG ensures every response is grounded in verified enterprise data with citations, enabling faster, more confident decision-making.

Compliance Research

Support auditors and legal teams with source-linked insights across contracts, laws, and reports. Each result is verifiable and traceable, simplifying audits, compliance reviews, and regulatory submissions.

Customer Support

Deploy fine-tuned AI agents consistent with your brand voice. Ensure every chat, support ticket, and campaign response aligns with enterprise tone and accuracy.

Healthcare

Fine-tuned clinical and biomedical models generate context-aware summaries and HIPAA-compliant recommendations for research and patient communication.

Business Value

25-40% more accurate

RAG pipelines ground responses in verified data, improving factual accuracy and reducing hallucinations across enterprise use cases.

70% faster access

Teams get reliable answers instantly, cutting search time across systems and improving research, support, and decisions.

Audit-ready

Every output includes source citations and version tracking for full transparency and compliance.

Higher adoption rates

Accuracy, explainability, and traceability build user confidence, driving faster enterprise-wide AI adoption.

FAQs

Fine-tuning adjusts a model's behavior using static data; RAG connects it to live, changing data—ensuring freshness and factual consistency.

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