ACW Soft Solutions

AI and machine learning development

Use historical data to support a specific prediction or classification task. ACW assesses the available evidence, develops the selected model, and tests whether its output is useful enough for the intended workflow.

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Start with the decision the model will support

  • Unclear target

    Define the outcome and the information available when the system must predict it.

  • Unreliable examples

    Check missing values, inconsistent labels, and gaps between historical records and current use.

  • Unproven benefit

    Compare the model with an existing rule, simple forecast, or current process before expanding development.

Develop the model and its place in the workflow

  • Prediction and classification

    Select the approach for the task and assess errors that matter, such as missed cases or false alerts.

  • Workflow integration

    Define how a prediction reaches the user or system and which decisions still require review.

  • Product functionality

    Connect model outputs to an interface or process with an understandable result and a way to handle unsuitable inputs.

  • Deployment planning

    Specify serving, versioning, performance checks, and updates for the environment included in the engagement.

What the engagement produces

  • A task and data assessment, including gaps and permitted uses.

  • A baseline and the model implementation in scope.

  • Evaluation findings on examples kept separate from development.

  • Integration and deployment work where included.

  • Documented limitations and responsibility for monitoring or retraining.

From data review to a release decision

  1. 01.

    Assess the evidence

    Review sample records, outcome definitions, access rights, and retention requirements. Identify data preparation or labelling work before committing to training.

  2. 02.

    Build and compare

    Develop the selected approach against the baseline. Exclude input features that would be unavailable when a prediction is made.

  3. 03.

    Review errors

    Examine performance across relevant groups of examples. Agree what happens when inputs are missing, conditions change, or results fall outside accepted limits.

  4. 04.

    Plan the release

    Confirm integration, running cost, operating owner, and checks for changes in input data or performance.

Accept the model against the task

Acceptance should identify the evaluation data, error measures, processing time, and operating cost. Record which situations are unsupported and who reviews them. A good overall score should not hide failure on an important category of cases.

This example describes a possible scope, not an ACW client result.

Illustrative workflow: request classification

A model proposes a category for an incoming request. Existing rules handle known cases; ambiguous or unsupported requests go to a person. Evaluation compares routing errors and review effort with the current process before automatic routing is considered.

Questions before development

How much data do we need?

It depends on the task, available labels, and acceptable error. A sample review establishes whether the current data supports a meaningful test.

Can you guarantee a target accuracy?

A target can guide evaluation. Achievability depends on the data and task; it needs evidence before becoming an acceptance commitment.

Do we need a custom model?

The assessment compares suitable existing approaches and a custom model where justified. Training from scratch is not the default.

Explore related services

Describe the prediction you need

Tell us the decision, the data available today, and the consequences of an incorrect result.

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