EquAI Factory

Trust and delivery

Company-controlled AI needs clear operating discipline

See how hosting, model ownership, backup paths, and change control stay clear before launch for private or company-controlled AI.

See if your workflow is a fit
  • Private cloud, VPC, or on-prem
  • Change control before expansion

Control summary

Sample outline

Control stays clear from review to launch

The workflow only goes live once the hosting setup, model choice, exception path, and backup logic are clear.

Workflow
Named business workflow with a business owner and technology owner
Hosting
Private cloud, VPC, or on-prem depending on fit
Model approach
Open-source model with a tuning and upgrade plan
Exception path
Ambiguous or low-confidence cases escalate

Clear hosting choices, tighter scope, and fewer hidden assumptions.

Control boundaries

What stays under your control

The company keeps the workflow scope, model choice, and hosting setup visible instead of hiding them behind an external API.

  • Hosting

    Private cloud, VPC, or on-prem, depending on fit.

  • Data

    Each stage names the data it needs, from workflow shape to representative inputs.

  • Access

    Limited to the workflow being evaluated, not the whole environment.

  • Human review

    Approvals, overrides, and escalation are documented before rollout.

  • Evaluation

    Success criteria are defined before live rollout expands.

  • Change control

    Prompt, policy, threshold, and model changes follow a documented review path.

Data by stage

What data is needed at each stage

What each stage needs, in the order the engagement moves.

  1. Stage 01

    Initial review

    Workflow shape, volume, hosting constraints, and system landscape.

  2. Stage 02

    Pilot scoping

    Representative inputs, policy references, model approach, and ownership.

  3. Stage 03

    Go-live readiness

    Success criteria, backup rules, hosting setup, and operator feedback paths.

Control detail

How each control is handled

Hosting, access, sensitive reviews, human checkpoints, evaluation, and change control are clear before the workflow touches real work.

  • Control 01

    How hosting choices are handled

    • The first workflow can run in private cloud, VPC, or on-prem depending on fit.
    • The job is to get one important workflow working under company control, not to start an abstract infrastructure debate.

  • Control 02

    How access boundaries are handled

    • Access stays limited to the workflow being evaluated, not the whole environment.
    • The goal is a company-controlled workflow layer with clear review and change paths.
    • The deployment surface stays narrow before the team decides to expand.

  • Control 03

    How sensitive workflow reviews are handled

    • Workflow review can begin under NDA before deeper materials are shared.
    • Early discussions usually cover workflow steps, review points, and systems involved.
    • Deeper operational detail only becomes necessary once there is a credible next step.

  • Control 04

    How human checkpoints are designed

    • Approvals, overrides, and escalation paths are documented before rollout.
    • Ambiguous or low-confidence cases stay visible to human reviewers.
    • Operator feedback becomes part of the improvement loop instead of disappearing into the system.

  • Control 05

    How pilots are evaluated before expansion

    • Success criteria are defined before live rollout expands.
    • Review rates, misses, overrides, queue outcomes, and lower outside dependence stay visible once live.
    • Expansion is recommended only when the workflow meets the agreed success criteria.

  • Control 06

    How change control works after go-live

    • Prompt, policy, threshold, and model changes need a documented review path.
    • The team should know who approved the change, what changed, and what needs retesting.
    • Operational ownership stays clear after launch instead of drifting over time.

Company-controlled stack

The same boundaries, as layers

Each layer names who controls it, so nothing is hidden behind an external API.

  1. Company controlled

    Data and policy layer

    The company keeps control of the data, access rules, and policies tied to the workflow.

  2. Open models

    Model layer

    Choose the model that fits the workflow instead of being locked into one vendor roadmap.

  3. Equ with your team

    Adaptation layer

    Prompting, retrieval, tuning, and evaluation are tailored to the company workflow.

  4. Company-controlled infrastructure

    Serving layer

    The system runs in private cloud, VPC, or on-prem depending on the workflow and control needs.

  5. Equ with your team

    Workflow layer

    The model becomes a working business process with review, escalation, and rollout logic.

Keep reading

Next step

Bring the workflow to review

Use this page to confirm the operating model, then review the workflow that needs more control.

See if your workflow is a fit