Enterprise RAG & AI Integrations
Your AI is only as useful as the knowledge it can reach. RAG connects the two.
Request an Architecture ReviewWhat RAG Solves for Enterprise
Three root causes of GenAI failure in enterprise environments — all solved by a well-designed RAG architecture.
Models hallucinate without grounding
Foundation models trained on general internet data do not know your products, your policies, your contracts, or your procedures. Without grounding in your actual knowledge, they generate confident-sounding answers that are factually wrong for your context. RAG is how you close that gap.
Enterprise knowledge is locked and scattered
Your most valuable institutional knowledge lives in SharePoint folders, PDF manuals, Confluence wikis, Salesforce records, ServiceNow tickets, and database tables — not in formats that AI can readily access. RAG builds the infrastructure to make that knowledge AI-accessible.
Generic outputs are not enterprise outputs
An AI system that does not know your specific policies, product specifications, compliance requirements, and historical precedents cannot produce outputs that are actually useful in your workflows. Grounding the model in your knowledge is what transforms generic capability into operational value.
The RAG Architecture Stack
A production-grade enterprise RAG system has five distinct architectural layers. Each one has design decisions that compound across the full pipeline — getting them right is what separates working prototypes from reliable production systems.
Document Ingestion & Chunking
Raw documents — PDFs, Word files, HTML pages, database records — are ingested, cleaned, and split into semantically coherent chunks. Chunking strategy dramatically affects retrieval quality; we tune it for your content type and query patterns.
- Format-specific parsers for PDF, DOCX, HTML, JSON, and structured data
- Semantic chunking that preserves context across document boundaries
- Metadata extraction and tagging for filtering at retrieval time
Embedding & Vector Storage
Each chunk is converted to a dense vector embedding using the appropriate embedding model, then stored in a vector database optimized for similarity search at your scale.
- Embedding model selection based on your domain and language requirements
- Vector database design and deployment (Pinecone, Weaviate, pgvector, Chroma)
- Hybrid search configuration combining vector and keyword retrieval
Retrieval & Reranking
When a query arrives, the retrieval system surfaces the most relevant chunks using vector similarity, keyword matching, and metadata filters. A reranking step then re-orders results by relevance to the specific query before they reach the model.
- Query expansion and reformulation for recall improvement
- Cross-encoder reranking to improve precision of top results
- Metadata filtering to scope retrieval to relevant document subsets
Prompt Orchestration
Retrieved context is assembled with the user query into a structured prompt. The orchestration layer manages context window budget, source attribution, and guardrail application.
- Context assembly and token budget management
- Source attribution for traceability of AI-generated answers
- Guardrail application to prevent out-of-scope responses
Output Filtering & Logging
Every AI output is filtered for policy compliance and logged with full provenance — which documents were retrieved, which chunks were used, and what the model generated. This creates the audit trail that enterprise governance requires.
- Content policy filtering for compliance and brand safety
- Full provenance logging: query, retrieved chunks, model output
- Confidence scoring and uncertain-answer flagging
Enterprise Integration Targets
We have built integrations across the full enterprise software stack. If your knowledge lives there, we can connect AI to it — securely, with appropriate access controls, and with full observability.
SharePoint
Document Management
Salesforce
CRM
ServiceNow
ITSM
SAP
ERP
Confluence
Wiki & Documentation
Databricks
Data Platform
Snowflake
Data Warehouse
Custom Databases
SQL / NoSQL / Vector
Microsoft 365
Productivity Suite
Google Workspace
Productivity Suite
Workday
HR & Finance
Custom APIs
Bespoke Integration
Enterprise RAG is Not a Tutorial Project
Building a RAG prototype with public documents and a demo UI takes an afternoon. Building a production RAG system that handles real enterprise constraints requires careful design across six dimensions that most tutorials skip entirely.
Data access controls — retrieved documents must respect the querying user's permissions
PII and sensitive data — some documents should never surface to some users
Source freshness — stale knowledge in the index produces stale AI answers
Retrieval coverage — gaps in indexed content create confident-sounding gaps in AI output
Latency — production RAG systems must retrieve and respond within acceptable SLAs
Cost — embedding, storage, and inference costs compound at enterprise query volumes
Request an Architecture Review
Tell us about your knowledge infrastructure — where your data lives, what integrations you need, and what AI use case you are trying to enable. We will scope the right RAG architecture for your environment.