RAG Solutions
For Every Use Case
Custom retrieval-augmented generation systems designed for your specific domain, compliance requirements, and accuracy needs.
Enterprise Knowledge Base AI
The Problem
Teams waste hours searching through documentation, wikis, and internal systems. Critical information exists but isn't discoverable when needed.
Why Normal AI Fails
Generic chatbots lack access to your internal knowledge. Public AI services can't index your private documentation, and even if they could, security policies prevent uploading sensitive content.
How RAG Fixes It
RAG ingests your entire knowledge base—Confluence, SharePoint, wikis, documentation—into a vector database. Semantic search retrieves relevant context, and the LLM generates answers grounded in your actual documentation with source citations.
Document ingestion → Chunking & embedding → pgvector storage → Hybrid search (semantic + keyword) → Context injection → LLM generation with citations
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Customer Support Contextual AI
The Problem
Support teams struggle to find accurate answers quickly. Product documentation, FAQs, and troubleshooting guides are scattered across multiple systems.
Why Normal AI Fails
Traditional search returns irrelevant results. Support agents copy-paste from outdated docs. Customers wait longer, and resolution quality varies by agent experience.
How RAG Fixes It
RAG unifies all support materials—product docs, ticket history, knowledge articles—into a single searchable system. Support agents query in natural language and receive accurate, cited answers. The system learns from feedback to improve retrieval over time.
Multi-source ingestion (docs, tickets, KB) → Metadata tagging → Vector embedding → Query understanding → Top-k retrieval → Answer generation with confidence scores
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Legal / Compliance Document AI
The Problem
Legal teams need to find relevant clauses, precedents, and compliance requirements across thousands of contracts and regulatory documents.
Why Normal AI Fails
Keyword search misses semantic relationships. "Termination clause" won't find "early exit provision" even though they're equivalent. Manual review is slow and error-prone.
How RAG Fixes It
RAG indexes all legal documents with semantic understanding. Queries like "What are the termination conditions?" retrieve relevant clauses regardless of exact wording. Every answer cites source documents for audit trails. Compliance officers can verify claims instantly.
Document parsing (PDF, Word) → Legal entity extraction → Semantic chunking → Vector storage → Query expansion → Citation generation → Audit logging
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Internal SOP & Policy AI
The Problem
Employees can't find current policies and procedures. HR, IT, and operations maintain separate documentation that's often outdated or contradictory.
Why Normal AI Fails
Policy documents live in silos. Employees don't know where to look. When they find something, it might be outdated. No single source of truth exists.
How RAG Fixes It
RAG creates a unified policy knowledge base. Employees ask questions in plain language: "What's the remote work policy?" The system retrieves current policies, highlights relevant sections, and provides citations. Version control ensures only current policies are retrieved.
Policy document ingestion → Version tracking → Metadata enrichment → Access control integration → Natural language query → Policy retrieval with version info
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Sales Enablement AI
The Problem
Sales teams need product information, competitive intelligence, and pricing details instantly during customer conversations. Information is scattered across CRM, product docs, and internal wikis.
Why Normal AI Fails
Sales reps waste time searching for answers mid-call. Product information is outdated. Competitive intelligence isn't centralized. This slows deals and reduces win rates.
How RAG Fixes It
RAG aggregates product specs, competitive analysis, pricing sheets, and case studies into a searchable system. Sales reps query during calls: "What are our advantages over Competitor X?" The system returns accurate, cited answers with source documents for follow-up.
Multi-source aggregation (CRM, docs, competitive intel) → Real-time updates → Query routing → Answer generation → Source attribution → Integration with sales tools
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Healthcare / Clinical Notes AI
The Problem
Clinicians need to search patient histories, clinical guidelines, and research literature quickly. Information is buried in EHR systems and medical databases.
Why Normal AI Fails
EHR search is keyword-based and misses semantic relationships. Clinical guidelines are updated frequently. Research literature is vast and hard to navigate. Clinicians make decisions without full context.
How RAG Fixes It
RAG indexes clinical notes, guidelines, and research papers with medical terminology understanding. Queries like "treatment options for condition X in patient with comorbidities Y" retrieve relevant guidelines and research. All answers cite sources for clinical validation. HIPAA-compliant deployment ensures patient data privacy.
Clinical document ingestion → Medical entity recognition → HIPAA-compliant storage → Semantic search → Evidence retrieval → Citation with source links → Audit logging
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