{"id":909,"date":"2024-03-22T12:48:25","date_gmt":"2024-03-22T12:48:25","guid":{"rendered":"http:\/\/the-codest.localhost\/?page_id=909"},"modified":"2026-09-27T21:29:43","modified_gmt":"2026-09-27T21:29:43","slug":"ai-services","status":"publish","type":"page","link":"https:\/\/thecodest.co\/en\/services\/ai-services\/","title":{"rendered":"AI Services"},"content":{"rendered":"
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AI Services<\/p>

Find out where AI earns its place in your workflow.<\/h1>

Start with a task, a decision or a product need. We help evaluate an AI use case, test a focused prototype or plan an integration, with an agreed scope and criteria for deciding what comes next.<\/p>\n<\/div> Discuss your AI use case<\/a> <\/div>\n <\/section>\n

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Three ways to approach the work.<\/h2>

Start with the question, not the model.<\/h2>

What needs to improve, and how would you recognise a useful result? Consider the current workflow, available data, constraints and the people who will use or review the output.<\/p>\n<\/div>

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Validate a use case with a focused prototype.<\/h3> <\/article>\n
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Plan or build an AI integration.<\/h3> <\/article>\n
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Use AI within software engineering.<\/h3> <\/article>\n <\/div>\n <\/div>\n <\/section>\n
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Agree what a useful result must demonstrate.<\/h2>
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Workflow value<\/h3>

Identify the task or decision being supported and compare the result with the way the work is done today.<\/p>\n<\/div> <\/article>\n

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Quality and limits<\/h3>

Use relevant examples to assess useful output, failure cases and the circumstances that require human intervention.<\/p>\n<\/div> <\/article>\n

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Data and dependencies<\/h3>

Define permitted data, access, external dependencies and the reviews your organisation requires. Do not treat a prototype as approval for production data use.<\/p>\n<\/div> <\/article>\n

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Operational responsibility<\/h3>

Before wider use, agree who reviews output, handles exceptions and owns maintenance. Include those responsibilities in the implementation scope.<\/p>\n<\/div> <\/article>\n <\/div>\n <\/div>\n <\/section>\n

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A prototype is evidence for a decision, not a production guarantee.<\/h2>

The next step may be a wider implementation, a revised use case or no implementation at all. Production work needs its own scope, evaluation and operational responsibilities.<\/p>\n<\/div> <\/div>\n <\/section>\n

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Questions before committing to more.<\/h2>
\n Do we need to choose a model or platform first?<\/summary>

Start with the workflow, constraints and evaluation question. Technology choices should follow those requirements, not define the problem for you.<\/p>\n<\/div> <\/details>\n

\n Can a prototype be used in production?<\/summary>

Not by default. A successful test does not establish production readiness. Data use, integration, quality checks and ongoing ownership need a separate assessment.<\/p>\n<\/div> <\/details>\n

\n What if we need engineers for a product that already uses AI?<\/summary>

That may be a staff augmentation or product development need. Working on an AI-enabled product and validating a new AI use case are different scopes; start with the responsibility you need.<\/p>\n<\/div> <\/details>\n Compare ways of working<\/a> <\/div>\n <\/section>\n

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Which workflow is worth testing?<\/h2>

Describe the task, the users and the decision you need to make. Include any data, integration or review constraints already known to your team.<\/p>\n<\/div> Discuss your AI use case<\/a> <\/div>\n <\/section>\n\n

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\n <\/contact-form>\n <\/div>\n <\/div>\n\n
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