CogentQAI-AI Stack Builder

CogentQAI Documentation

CogentQAI helps teams evaluate a machine, choose a grounded AI stack architecture, and generate reproducible local deployment files for development and testing.

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What CogentQAI Does

CogentQAI is an AI stack builder for local and self-managed deployment workflows. It helps users move from a hardware check to a practical stack recommendation, then generates the files needed to stand up that environment consistently.

The product is designed for people who want a clearer path into local inference, containerised AI services, and reproducible development environments without guessing which runtime mix fits their machine.

Recent platform capabilities include authenticated access, database-backed scan history, and legal baseline pages such as Terms and EULA. Those additions support a more credible product workflow without changing the core purpose: machine-aware stack generation for practical AI development.

Core Product Flow

  1. 1. Analyse the target hardware automatically or manually.
  2. 2. Review compatibility guidance and capability tier.
  3. 3. Choose stack architecture and application intent.
  4. 4. Generate stack files and installer direction.
  5. 5. Reuse the output to rebuild the environment reliably.

How The Platform Works

Built-in visual
CogentQAI architecture and generation flowA four-stage flow showing hardware analysis, compatibility analysis, stack architecture selection, and generated output.Hardware Analysis
CogentQAI reads machine signals or manual inputs to estimate available CPU, memory, OS fit, and practical local AI headroom.
Compatibility Analysis
The platform translates those machine constraints into model-size guidance, runtime fit, and a safer stack recommendation.
Stack Architecture
The selected architecture defines the technical shape of the environment, including local inference, orchestration, storage, and developer tooling.
Generated Output
CogentQAI produces reproducible setup files so the same environment can be reviewed, installed, and run again with less drift.

The builder is intentionally sequential. It starts with hardware and runtime reality, narrows that into compatibility guidance, then shapes the generated environment around a stack architecture and the application intent you choose.

Architecture Reference

Hardware Analysis

The analysis layer estimates whether a machine is suited to lightweight experimentation, developer-grade local inference, or heavier workstation-style usage. This matters because local AI deployment is constrained by available memory, compute, and OS support more than by feature lists alone.

Compatibility Analysis

Compatibility analysis turns hardware signals into practical model guidance. Instead of assuming every machine can run every model, CogentQAI estimates when a setup is better suited to smaller local models, limited experimentation, or broader runtime support.

Stack Architecture

Stack architecture describes the technical composition of the environment: model runtime, service containers, optional vector database, and developer-facing tools. It is the structural choice that determines how the generated package is assembled.

Application Intent

Application intent tunes the build toward the workflow you are targeting, such as coding support, SaaS-oriented product work, agent experimentation, or creator tooling. Intent does not replace architecture; it refines the recommendation around how the stack will be used.

Generated Stack Output

docker-compose.yml

Describes the core runtime services and how they should start together in a reproducible local environment.

install.ps1

Provides a guided installer path for Windows-based setup so local AI dependencies can be applied in a repeatable order.

models.txt

Captures model recommendations and pull targets so local inference workflows are easier to reproduce across machines.

README.md

Explains the generated stack, expected services, install direction, and the operating assumptions behind the package.

Installer Direction

CogentQAI does not just list components. The generated package also points users toward an installation sequence so local runtimes, containers, and supporting services can be brought up in a more controlled order. That guidance is especially useful when preparing a machine for repeatable local inference workflows or onboarding a second developer to the same environment.

Working Definitions

Local AI Deployment

Running models and supporting services on your own machine or controlled infrastructure instead of depending entirely on hosted APIs.

Hardware-Aware Generation

Selecting a stack shape that reflects the memory, CPU, operating system, and practical inference limits of the target machine.

Model Compatibility

An estimate of which model sizes are realistic for a machine, helping avoid stacks that are technically installable but operationally poor.

Runtime Compatibility

A check that the surrounding tools, containers, and model runtimes fit the operating system and deployment style you intend to use.

AI Infrastructure Readiness

A practical measure of whether a machine is ready for local inference, vector search, container services, and developer tooling together.

Reproducible Environment

A setup that can be recreated with the same files and installer direction, reducing manual drift between machines or rebuilds.

Q&A

Does CogentQAI install the stack for me?

CogentQAI generates the deployment files and installer direction for the stack you choose. It is designed to reduce setup guesswork, but you still review and run the generated package on the target machine.

Why does the platform ask about hardware first?

Local AI workflows are heavily shaped by available memory, CPU capacity, operating system support, and practical runtime limits. Starting with hardware helps avoid recommending stacks that look attractive on paper but perform poorly in practice.

Can I use CogentQAI if I do not know my machine specs?

Yes. The Analysis flow supports both a browser-based scan and a manual questionnaire. That gives you a way to get a grounded recommendation even when you only know a rough amount of RAM, CPU class, or operating system.

What is the difference between architecture and application intent?

Architecture defines the technical shape of the stack, such as runtimes, services, and optional infrastructure. Application intent adjusts that build toward the workflow you care about, such as coding assistance, SaaS product work, agents, or creator tooling.

Use The Builder

Build a machine-aware stack

Start with the Build flow to combine hardware analysis, stack architecture, and application intent into a generated package that better fits the target machine.

Open Stack Builder ->

Review analysis and history

Use the Analysis flow to inspect capability, compatibility, and saved scan history before selecting a stack. This is the best entry point when infrastructure readiness is still unclear.

Open Hardware Analysis ->