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RAG with Hivenet
Build RAG workflows that answer from your own documents.
Use Hivenet to support retrieval-augmented generation with S3-compatible storage, GPU and CPU compute, managed inference, and guided Private AI paths for sensitive or business-critical document systems.
Document AI
Internal knowledge assistants
Enterprise RAG
Private RAG
S3-compatible storage
OpenAI-compatible inference
GPU and CPU compute
Your business knowledge is already there. The hard part is making it usable.
Teams already have the material AI needs: product docs, policies, contracts, tickets, reports, manuals, research notes, meeting transcripts, and internal guides. The problem is that this knowledge is scattered, changes often, and usually lives outside the model.
RAG helps by retrieving the right context before the model answers. That gives teams a practical way to build AI tools that stay closer to their source material without retraining a model every time the documents change.
Support knowledge bases
Policy search
Contract review
Product documentation
Research archives
Internal operations
Education content
The workflow is simple. Production is where the details matter.
Store the source material
Keep documents, exports, PDFs, transcripts, and other source files somewhere your pipeline can access.
Prepare the content
Extract text, preserve useful metadata, clean noisy files, and split documents into chunks that retrieval can use.
Index the knowledge
Turn chunks into embeddings and store them in a vector database, search engine, or hybrid retrieval layer.
Retrieve the right context
When someone asks a question, retrieve the most relevant passages and pass them to the model.
Generate and evaluate the answer
The model answers with retrieved context, while your team checks quality, source grounding, latency, cost, and failure cases.
Hivenet supports the infrastructure around the RAG stack.
RAG is a workflow with several moving parts. Hivenet can support the infrastructure layers teams need for storage, processing, inference, and private deployment planning.
Storage
Keep documents and pipeline files accessible.
Use S3-compatible storage for source files, processed text, datasets, generated outputs, logs, and RAG pipeline artifacts.
Compute
Run processing and self-managed components.
Use GPU or CPU instances for parsing, chunking, embedding jobs, indexing, reranking, evaluation, APIs, notebooks, or your own model-serving stack.
Inference
Connect to a managed model endpoint.
Use Hivenet Inference API when your application needs an OpenAI-compatible endpoint for open source and foundational models without operating the serving layer yourself.
Private AI
Get help with sensitive document systems.
Use Private AI when the workload needs help with data paths, model choice, security review, regional planning, or production rollout.
Start with the operating model.
If you need
Choose
Why
A managed model endpoint
Hivenet Inference API + S3 storage
You keep the app and retrieval layer while Hivenet operates the endpoint
Full control over the stack
Compute with Hivenet + S3 storage
You manage the runtime, model server, retrieval layer, vector database, and app
Sensitive or regulated data paths
Private AI
Hivenet helps scope architecture, access, region, model choice, and rollout
Larger model capacity
RTX 4090 or RTX 5090
Use when memory, model size, or deployment control changes the infrastructure decision
I need storage for source documents
S3-compatible storage
Start by organizing source documents and pipeline artifacts before choosing the model path
Swipe left to see more
What teams build with RAG.
Internal knowledge assistants
Help employees find answers across policies, handbooks, meeting notes, product information, and internal documentation.
Customer support copilots
Retrieve answers from help articles, known issues, tickets, release notes, and product docs before generating a response.
Legal and policy review
Search contracts, policies, reports, regulations, and supporting evidence with source-aware answers.
Research and technical archives
Work across papers, reports, experiment logs, project notes, and technical references without searching each source manually.
Product documentation assistants
Help users or internal teams answer questions from docs, API references, tutorials, changelogs, and release notes.
Education and training tools
Build assistants over course material, guides, study resources, and institutional documents.
Retrieval quality matters as much as model choice.
A strong RAG system depends on the full workflow. The model needs good context. The retrieval layer needs to find the right passages. The data path needs to be clear enough for the team to operate and explain.
Good source material
Clean documents, useful metadata, and clear structure make retrieval more reliable.
Sensible chunking
Chunk size, overlap, headings, and metadata affect whether the system retrieves useful evidence.
The right model path
Smaller models can work well for focused retrieval. Larger models can help with synthesis, reasoning, or longer documents.
Evaluation before scale
Test groundedness, citations, latency, failure cases, and cost before expanding across more teams or more data.
Start with one document set and one useful question.
A strong RAG system depends on the full workflow. The model needs good context. The retrieval layer needs to find the right passages. The data path needs to be clear enough for the team to operate and explain.
Good source material
Clean documents, useful metadata, and clear structure make retrieval more reliable.
Sensible chunking
Chunk size, overlap, headings, and metadata affect whether the system retrieves useful evidence.
The right model path
Smaller models can work well for focused retrieval. Larger models can help with synthesis, reasoning, or longer documents.
Evaluation before scale
Test groundedness, citations, latency, failure cases, and cost before expanding across more teams or more data.
FAQ
Common RAG questions
Bring one RAG workflow to Hivenet.
Share your document type, data size, update frequency, model preference, latency target, region needs, and current architecture. We'll help you choose the right path across S3 storage, Compute, Inference API, and Private AI.
