Data & access
Define sources, update frequency, formats and access rights. Cleaning, chunking and indexing affect downstream quality.
AI / REFERENCE ARCHITECTURE
Knowledge Bases & RAG
Connect document processing, vector retrieval and inference around your data scale.
Hardware and application reference imageryBUILD AROUND THE WORKLOAD
Work through the technical requirements
Embedding & retrieval
Assess CPU, RAM and storage from document counts, vector dimensions and query load, deciding whether embedding needs acceleration.
Inference & integration
Size inference around retrieved content, context and concurrency, distinguishing retrieval from generation bottlenecks.
Evaluation & iteration
Use source-verifiable test questions to assess retrieval, grounding and access isolation. RAG does not guarantee correct answers.
REFERENCE WORKFLOW
Define the workflow before the hardware.
- 01Workload
- 02Compute
- 03Data & links
- 04Validate & operate
This is an architecture planning approach, not a completed case study or performance commitment. Agree software, equipment and service scope per project.
What do we need next?
Applications and models, data scale, user or node counts, latency and throughput goals, existing resources, budget and site constraints. Existing measurements help narrow the shortlist.
Turn requirements into a reviewable plan
Discuss selection, bills of materials, samples and procurement on a project basis. Model availability, implementation, installation and validation scope require agreement after requirements review.
Discuss your technical requirements ↗LET’S BUILD YOUR NEXT CONFIGURATION
Discuss your requirements ↗