Draft emails, summaries, proposals, reports and knowledge articles from your approved business data, with human review built into every workflow.
Generative AI for business is most useful when it drafts from trusted context and hands the result to an accountable person. DO ALL helps you scope GenAI use cases across ERP, CRM, HRMS and support workflows, using retrieval-augmented generation (RAG) over approved documents, role-based access and clear review steps. Start with a measurable task such as summarising tickets or drafting quotations, then extend to agentic AI workflows only where controls and evidence support them.
Produce a structured starting point for routine documents, replies and summaries.
Draw on approved policies, product data and records instead of open-ended guessing.
Keep review, approval and edit history with the person who owns the result.
Generate quotations, proposals, follow-up emails and letters from CRM, ERP and template data for a person to review and send.
Condense long tickets, case histories, contracts or call notes into key points, decisions and next actions.
Answer questions from approved policies, manuals and product documents, with source references so users can verify the content.
Draft and translate business content in Arabic and English, with terminology and tone reviewed by a fluent speaker before publication.
Assess multi-step AI agents that prepare records or tasks for approval, with limited permissions and a human decision before any irreversible action.
Apply role-based access, data masking, prompt templates, logging and usage policies so sensitive information stays within agreed boundaries.
Draft quotations, tender responses and personalised follow-ups from CRM history and approved product information.
Prepare job descriptions, policy summaries, onboarding content and standard operating procedure drafts for review.
Summarise tickets, suggest replies and turn resolved issues into knowledge base article drafts.
We confirm requirements, dependencies and acceptance criteria with your team. The proposal sets out the included capabilities, integrations and responsibilities.
Prioritise tasks where drafting or summarising time is high and review is practical.
Curate the documents, templates and records the model may use, with access rules.
Test real examples, measure edits and errors, and refine prompts and guardrails.
Extend to more teams with usage monitoring, feedback and periodic quality review.
Generative AI delivers value when it drafts from trusted business context and a named person approves the result. Compare GenAI proposals on grounding, access control and measured quality, not on demonstrations alone.
RAG connects a large language model to selected policies, product documents and records so answers can cite their sources. Curate the content, remove outdated versions and test questions the documents cannot answer.
Start with drafting and summarising, where review is simple. AI agents that gather data or prepare records across several systems need narrower permissions, approval checkpoints and logs that show each step taken.
Confirm where prompts and documents are processed, what providers retain and how personal data is minimised. Align usage policies with UAE data protection requirements and your own information security standards.
Extend generative AI beyond a chat window by connecting it to templates, approved knowledge and review steps that already exist in your business workflows.
Explore these extensions during discovery. Availability, configuration, integrations and service commitments are confirmed in your agreed scope.
Discuss prompt templates for quotations, letters, job descriptions and reports so output follows your structure, terminology and tone, and reviewers know what to check.
Explore showing the documents behind each answer, flagging outdated sources and excluding drafts or superseded policies from retrieval.
Build a set of representative questions and expected answers to test prompts, models and content changes before they reach users, and repeat the tests after updates.
Generative AI creates new text or content, such as drafts, summaries and answers, from instructions and context. In business software it is most useful for routine drafting and knowledge retrieval, with a person reviewing the output before it is used.
Model providers, hosting options and contractual terms are reviewed during scoping. The proposal states where data is processed, what is retained and whether any provider may use it, so you can make an informed decision.
Yes. Models can produce plausible but incorrect content. Grounding on approved sources, showing references, limiting scope and requiring human review reduce the risk but do not remove it.
Arabic and English drafting can be evaluated for your content. Quality varies by task and dialect, so the pilot should include Arabic examples reviewed by fluent staff.
Generative AI produces content in response to a request. Agentic AI plans and carries out several steps, such as gathering data and preparing a record. Agentic workflows need tighter permissions and approval points.
Compare time spent, edit rates, error rates and user satisfaction against a baseline for the chosen task. Include the review effort, not only generation time.
Model choice depends on the task, languages, hosting and data requirements. Options are compared during scoping and the proposal names the model provider and processing location.
Approved documents can be indexed for retrieval so answers draw on their content. Scanned files, tables and Arabic text should be tested for extraction quality first.
Combine a usage policy, role-based access, masking where practical and logging. Provide an approved internal tool so staff are less likely to paste data into public AI services.
Feedback is sent privately to the DO ALL team by email. Public review publishing is currently paused.
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