--- title: "AI & intelligent automation" description: "Private enterprise AI, generative AI, AI agents, retrieval-augmented generation (RAG) and document intelligence. Automate workflows and make your business knowledge easier to use." canonical: "https://juhenza.com/solutions/ai-intelligent-automation" last-updated: 2026-10-15 --- # AI & intelligent automation > Private enterprise AI, generative AI, AI agents, retrieval-augmented generation (RAG) and document intelligence. Automate workflows and make your business knowledge easier to use. ## Your knowledge. Put to work. Private enterprise RAG development connects approved documents to citation-backed answers and AI automation. We scope self-hosted LLM document intelligence, role-based retrieval and AI agents around your data boundaries and human review, with Docker and Linux deployment where appropriate. ## Fit For teams exploring private enterprise AI, document intelligence, AI agents and workflow automation. ## Scope - **Enterprise AI & RAG:** Search and question-answering over approved knowledge sources, with retrieval, source references and access boundaries. - **AI agents & workflow automation:** Connect model reasoning to defined tools and actions, with approval steps where mistakes carry consequences. - **Document intelligence:** Extract, classify and organize information from documents, with validation and exception handling. - **Machine learning & analytics:** Explore prediction, natural language processing and computer vision where data quality and evaluation support the use case. ## Process 1. **Choose a measurable task:** Understand the current workflow, available data and how the team will judge a useful result. 2. **Evaluate before expanding:** Prototype with representative inputs, inspect failures and compare results against an agreed baseline. 3. **Integrate with oversight:** Connect the workflow, define review boundaries and prepare monitoring, feedback and handover. ## Examples ### Knowledge search - Challenge: Important answers are scattered across policies, manuals and internal documents. - Approach: Index approved sources, retrieve relevant passages and return answers with references while respecting access rules. - Deliverable: A knowledge assistant prototype, ingestion pipeline and an evaluation set built around real questions. ### Document processing - Challenge: People repeatedly copy fields from incoming documents into business systems. - Approach: Extract the required information, validate it against business rules and route uncertain results to a reviewer. - Deliverable: A document-processing workflow with review queues and structured outputs. ### An assisted workflow - Challenge: A repetitive task spans several tools but still needs human judgement at key points. - Approach: Define allowed actions, connect the necessary APIs and put approval gates before consequential changes. - Deliverable: A bounded AI agent with tool permissions, activity logs and a clear fallback path. ## FAQ ### Will our data train a public model? Data handling depends on the selected model provider and deployment. Retention, training use, access controls and hosting requirements must be reviewed and agreed before using your data. ### Can AI run without human review? Some low-risk tasks can be automated. Decisions with operational, financial or other significant consequences need boundaries and appropriate review. The design should account for incorrect or incomplete outputs. ### Do we need a large dataset to begin? Not always. A document assistant may start with a focused set of reliable sources. Custom machine learning may require more representative data; discovery establishes what is feasible. ## Reference architecture A reference document-assisted workflow. Source access, answer generation and action approval remain separate decisions. ### Sources Approved documents → Access & retrieval Retrieve relevant material from approved sources within the requesting user’s permissions. Review: Check document freshness, deletion, source ownership and permission enforcement. ### Reasoning Model & instructions → Evidence & evaluation Produce a draft grounded in retrieved evidence, with an evaluation process for unsupported or incorrect answers. Review: Test missing evidence, conflicting sources, malicious document instructions and failure to answer. ### Review Human approval → Recorded action Let a person inspect a draft before an approved integration changes a record or sends a message. Review: Define which actions require approval, audit records, retries and a way to stop the workflow. Illustrative planning model; scope and deliverables are agreed per project. [AI automation readiness checklist](https://juhenza.com/resources/ai-automation-readiness) ## Technology choices Model choice follows the evidence and the task ### Sources Document parsing · Retrieval · Permissions Choose source formats and retrieval behavior around the approved material and each user’s access. ### Application Model APIs · Node.js · TypeScript Compare candidate model behavior on representative tasks before committing to a provider or deployment arrangement. ### Operations Evaluation sets · Logs · Review gates Define an evaluation process and monitor answer quality, latency and operating cost within the agreed privacy boundaries. ## Quality checks before handover ### Grounded output Evaluate answers against expected results and check that cited passages actually support the answer. ### Safe boundaries Test restricted documents, missing evidence and unsupported tasks; keep consequential actions behind agreed approvals. ### Repeatable evaluation Record evaluation cases and limitations so changes to models, prompts or source data can be reviewed before release. ## Preparing for a project Bring a repetitive task, representative non-sensitive examples, data-access requirements and a way to assess whether the output is useful. 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