evergences®ENGAGEMENT BRIEF / 04

ONE REPEATED BUSINESS TASK

Build AI
Models

Teach the task. Test the result.

Adapt an existing model to work your business knows how to do. Choose a valuable outcome, prepare authorized examples, then train, evaluate and deploy within an agreed scope.

What it is
A scoped engagement to fine-tune, evaluate and deploy a model for one valuable, repeated business task.
Who it is for
Business owners and teams with a defined outcome, authorized examples and an accountable owner.
Investment required
Scope-based fees and timing agreed in a proposal. Authorized training and test data, agreed compute access and owner participation in evaluation are needed.
Transformation
Turn business examples into a licensed model adapted to the task, tested against a baseline and prepared for the agreed local or cloud deployment.
Intended benefit
Evidence of how well the model supports the selected task, including quality, speed and business usefulness. Performance and financial gains must be measured.

WHAT YOU RECEIVE

Three useful outputs.

  1. Task, data and model plan

    A defined outcome and baseline, authorized training and held-out test examples, a licensed model choice and an agreed build scope.

  2. Fine-tuned model and evaluation

    A model adapted to the agreed task, with recorded comparisons of quality, speed and task usefulness against the baseline.

  3. Deployment and handover

    Agreed local or compatible cloud serving, operating instructions, documented limits and a plan for reviewed updates.

From outcome to deployment

  1. Choose. Define a repeatable task, a good result and its business value.
  2. Prepare. Clean authorized examples, hold out test data and select a model with suitable license terms.
  3. Train and test. Fine-tune and compare the result with the baseline.
  4. Deploy. Match the model and serving runtime to the agreed local or cloud environment.

What you bring

  • An accountable business owner and a task worth improving.
  • Examples you are authorized to use, with quality criteria and a current baseline.
  • Agreed compute and deployment access, and participation in evaluation and handover.

Keep restricted data within the agreed access and processing boundaries.

Clear boundaries

Excludes foundation-model pretraining, unagreed integrations, ongoing managed hosting, autonomous retraining, security certification and legal advice. Performance and business returns are not promised.

Agree before training. Set license conditions, data permissions, evaluation criteria, deployment responsibilities and human review. Wider rollout follows the agreed checks.

Start with three answers

  1. Which repeated task and business outcome should improve?
  2. What authorized examples and baseline are available?
  3. Who owns evaluation, compute access and deployment?

OUR APPROACH

How we approach model builds

Sean’s eight-step memo explains outcome selection, data preparation, fine-tuning, testing and deployment. It describes an approach, rather than a client performance result.

Read the model-building memo

Discuss the task first.

Email a nonconfidential overview. Deployment follows the agreed scope. Scope, fees and timing agreed in a proposal.

Discuss your model
sean@evergences.comevergences.com/services/build-ai-models/