AI Integration

Start AI requirements with daily work. Finding the right company document, reviewing long text and preparing a draft are different scenarios. We identify the user, data source and expected output together. If support needs an answer to a product question, the documents to use and the person checking the answer define the initial scope. This example anchors the business goal.

Understand information together with its source. Data preparation considers files, documents, record fields and content versions. A support assistant using product manuals must distinguish old and current editions. Repeated text, missing fields and access restrictions may be part of the initial review. We clarify the source by discussing which records you treat as valid and who approves changes.

Define users' next steps when sources are insufficient. An assistant saying “I don't know”, requesting more information or referring a question to an authorised person can be specified through acceptance examples. A product condition absent from documents may lead users to the relevant team. This supports working within known source boundaries. We consider questions that should receive answers alongside those that cannot.

Editors may check drafts before publication; internal support users may inspect source documents. We discuss which outputs are automatic, which await approval and when a process stops. Fault reporting, correction owners and assessment history can also be scoped.

Explain a concrete consequence of a wrong answer to help choose a checkpoint. For example, identify who would notice and correct an incorrect product feature in sales copy.

Describe success through real questions. Evaluation sets may include user questions, expected answers and sources. A common support question and one with missing information can require different behaviour in the same test. Questions without sources, with false assumptions or challenging access boundaries can also be selected. We agree coverage and acceptable outcomes together.

Updating a document, changing a model connection or adjusting response instructions can trigger another evaluation. This compares selected business cases as a whole rather than one impressive answer. Accuracy, grounding, unanswered-question behaviour and human checks are project-specific criteria.

Bring frequent questions and expert-approved answers to make the first release's evaluation plan concrete. Errors reported during use may help identify further examples.

Narrow requirements by data type. Natural language processing and text analysis may include classification, summarisation or information extraction. If you want incoming requests grouped into team topics, share valid categories and confusing examples. For a bespoke assistant, sources and user access are assessed together. Output format and checking steps are defined for each scenario.

Predictive analytics requires historical structure and a target decision; image processing or object recognition requires images and acceptance conditions. Data review clarifies scope for these service areas.

Demand trends and recognising objects require different preparation. Explain data format and the screen or action receiving the result, so the first assessment centres on one clear purpose.

Consider checking alongside production. AI Content CRM is a showcase product developed on request. If your team wants to move from an idea through drafting, editor review and publication preparation, fields and roles can be defined in discussion. Use the product page as a scope starting point. Explain what information should enter drafts and who gives final approval within your publishing practices.

Build the first release around an assessable use case. Pilot discussions cover sources, roles, output formats and evaluation sets. An assistant answering from product manuals might initially use a specific document group. Connections, interfaces, citations, unanswered questions and human review may be separate delivery items. We agree acceptance examples together after discovery.

Owners are discussed for new documents, removed access, changed providers and faulty answers. Written proposals define usage tracking and maintenance tasks. Model optimisation starts with the behaviour to improve, described through evaluation examples.

Software uses the standard subscription model; source code under another arrangement requires a separate written agreement. Share the question your pilot should resolve to connect development and operation to the same goal.

Bring a clear purpose to discovery. Our guide on where to start with AI integration helps connect data and business needs. Choose a sample question, expected answer and source as you read. The ALGO project describes AI-prepared Beta language courses and ongoing teacher review, illustrating assessment and human checks after content preparation. The app interface and linked application page are in Turkish.

Which files will be used, who may access them, how is a correct answer defined and who checks before publication? Answer through a support or content task. Discuss flows for on-request solutions such as AI Content CRM, including preparation and provider costs.

Sharing existing sources and a first use case through the contact form is enough to begin. We can then define an evaluation set, pilot and written delivery scope together.