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Venturi - AI

AI INFRASTRUCTURE WORKFLOW

AI Workflows Explained

Our Venturi AI Factory Sizer considers what happens between a user's prompt and an AI-generated response. Whether you are deploying a chatbot, agentic workflow, code assistant, or enterprise AI application, every AI interaction consumes infrastructure resources in predictable ways. Understanding that workflow is the first step toward properly sizing an AI Factory.

WHAT ACTUALLY CONSUMES INFRASTRUCTURE RESOURCES

The Common Myth

Most organizations think: User → AI → Answer.
Reality looks more like: User → Application → Context Retrieval → Model Selection → Inference Pipeline → Response Generation → Logging → Governance.

Every stage introduces resource requirements. Every stage introduces latency. Every stage influences sizing decisions.

STEP 1

The User Request

Every workflow begins with a request.

Examples Include:
  • Asking a chatbot a question
  • Requesting code generation
  • Triggering an AI agent
  • Summarizing documents
  • Searching enterprise knowledge
At this stage, workload characteristics begin to emerge: 
  • Number of users
  • Concurrent users
  • Request frequency
  • Availability expectations
These factors directly impact factory sizing recommendations. 
AI Capacity Planning

STEP 2

Context Retrieval and Augmentation

Models do not automatically know your business. Before generation begins, relevant information may need to be gathered.

Examples Include:
  • SharePoint documents
  • CRM data
  • Email history
  • Knowledge bases
  • Internal procedures
This stage determines: 
  • Data placement
  • Storage requirements
  • Retrieval performance
  • Security controls
AI Capacity Planning

STEP 3

Prefill

This is where things get interesting. The model must process all provided context before it can generate a response. This is often called the prefill phase.

More context means:
  • More memory consumption
  • More GPU utilization
  • Longer processing times
Large context windows frequently become the biggest infrastructure driver.

Examples include:
  • Legal contract/document analysis
  • Reviewing immense codebases
  • Long-form video processing
  • Book or Library ingestion
  • Quarterly performance analyses
  • Legacy system assessments
AI Capacity Planning

STEP 4

Decode

After the model has processed the available context, it begins generating a response. This phase is known as decoding.


During decoding, the model produces output one token at a time, building an answer based on the information, instructions, and business context that were assembled during the previous stages of the workflow.

Whether the result is a customer response, code recommendation, document summary, business insight, or agent action, decoding is where infrastructure resources are transformed into usable outcomes.

The speed and quality of this process are influenced by several factors:

  • Available infrastructure capacity
  • Model complexity
  • Concurrent demand
  • Context size
  • Response length

For organizations, decoding is where technical architecture becomes user experience. Faster, more reliable response generation can improve employee productivity, customer experiences, operational efficiency, and adoption of AI-powered services.

The goal is not simply to generate more tokens. The goal is to deliver relevant, trustworthy outcomes at the speed the business expects.

AI Capacity Planning

STEP 5

Repeat at Scale

One request is easy. Enterprise AI is different.

Now consider:
  • 500+ users
  • Multiple agents
  • Multiple models
  • Peak demand periods

What matters isn't a single request. What matters is concurrency. This is one of the largest drivers of infrastructure requirements discussed during our AI Factory Sizing workshops and technical engagements. 

This prompts us to ask the question: Can work be Reused?

This is where we consider KV Cache. In many scenarios, portions of previously processed context may be reused rather than reprocessed from scratch. 

Potential benefits include:

  • Reduced latency
  • Reduced GPU work
  • Improved throughput
Actual benefits depend on architecture context similarity, model design, storage location, and concurrency patterns, as KV Cache benefits vary by workload. 
AI Capacity Planning

WHY UNDERSTANDING WORKFLOW MATTERS

Explore Your Business's Workflow

Our AI infrastructure is not sized from model parameters alone. It is sized from users, context, concurrency, latency targets, availability requirements, and growth expectations. Understanding the workflow helps to explain why two organizations using the same model may require completely different infrastructure designs. 

The Venturi AI Factory Sizer is a practical tool developed to translate workload requirements into AI Factory architectures, helping organizations navigate complexity and design for desired business outcomes. 
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