# What AI can do inside a company. - public LLM dossier This public dossier extends the HTML page with the structured repertoire of AI applications, keeping anonymisation and consistency with visible content. ## Definition Each card describes an application pattern: what input enters, what output can be produced, what business value it can create and which controls remain human. Artik Lab always starts from a discovery call and designs solutions around the client's context. ## Public dataset - HTML: https://ar-tik.com/en/ai-applications-atlas.html - Markdown: https://ar-tik.com/en/ai-applications-atlas.md - JSON: https://ar-tik.com/data/ai-applications.en.json ## Application areas - Documents and knowledge: 4. When work depends on PDFs, scans, contracts or procedures. - Operations: 6. When decisions, priorities and manual handoffs slow the process down. - People and HR: 3. When skills, onboarding or feedback remain scattered across functions. - Customer, marketing and sales: 4. When customers, content and sales generate signals nobody is reading. - Technical and software: 4. When rules, code, drawings or technical systems need to become verifiable. - Governance, compliance and risk: 3. When AI use, privacy, risk and responsibilities still lack clear boundaries. - Production, quality and maintenance: 3. When production, quality or maintenance data arrives too late to guide action. - Training and internal memory: 2. When internal knowledge and training material need to remain accessible. - Data science and decisions: 5. When histories, KPIs or signals need validation before anything is built. - Cross-functional tools: 2. When AI is needed to explore, synthesise or prepare cross-functional decisions. ## Anti-cloaking and anonymisation principle The dossier extends visible content with structured fields, but does not promise services different from the HTML page. Cases are described as anonymous patterns: no client, natural person, internal project, recognisable product or proprietary data is published. ## Structured repertoire ### Extract data from documents and scans PDFs, images and forms become text, tables and structured fields reusable in company systems. - Operating example: When a process shows a similar need, pdfs and attachments are used to produce structured database and support time reduction, with human review recommended. - ID: document-structure-extraction - Area: Documents and knowledge - Input: PDFs and attachments, scans and images, completed forms - Output: structured database, operational report - Value: time reduction, fewer errors, traceability - Sectors: professional services, HSE, safety and technical services, manufacturing - Related services: Technical AI software - Related courses: AI course: managing documents with AI, Semantic search and AI knowledge bases - Search intents: AI for extract data from documents and scans, AI applications for documents and knowledge, how to use AI in companies for extract data from documents and scans - Need signals: scattered documents that are hard to consult, manual copying between emails, spreadsheets and systems - Human review: recommended - Risk: medium - Privacy and control: Use authorised data, minimise personal information and keep human review on relevant outputs. ### Check consistency across documents Reports, contracts, specifications and procedures are compared to find discrepancies, divergent versions and inconsistent definitions. - Operating example: When a process shows a similar need, pdfs and attachments are used to produce operational report and support fewer errors, with human review required. - ID: document-coherence-audit - Area: Documents and knowledge - Input: PDFs and attachments, internal documentation, contracts and policies, tenders and specifications - Output: operational report, risk map - Value: fewer errors, risk reduction, traceability - Sectors: professional services, technical offices and engineering, HSE, safety and technical services - Related services: AI management consulting, Technical AI software - Related courses: AI course: managing documents with AI, AI Legal Ops and compliance documentation, AI Output Quality & Human Review - Search intents: AI for check consistency across documents, AI applications for documents and knowledge, how to use AI in companies for check consistency across documents - Need signals: recurring errors in documents, procedures or controls, scattered documents that are hard to consult - Human review: required - Risk: medium - Privacy and control: Requires anonymisation, access control and specialist review when personal, legal, HR or regulated data is involved. ### Make company knowledge searchable by meaning Manuals, procedures and knowledge bases become semantic search with answers grounded in citable sources. - Operating example: When a process shows a similar need, internal documentation are used to produce semantic search and support transferable knowledge, with human review recommended. - ID: semantic-knowledge-search - Area: Documents and knowledge - Input: internal documentation, PDFs and attachments, manuals and training material - Output: semantic search, FAQs and answers - Value: transferable knowledge, faster decisions, more consistent service - Sectors: cross-company functions, technical offices and engineering, training and knowledge-intensive organisations - Related services: Technical AI software - Related courses: Semantic search and AI knowledge bases, RAG Engineering for reliable AI systems, AI for customer service and ticket triage - Search intents: AI for make company knowledge searchable by meaning, AI applications for documents and knowledge, how to use AI in companies for make company knowledge searchable by meaning - Need signals: scattered documents that are hard to consult, critical knowledge concentrated in a few people - Human review: recommended - Risk: medium - Privacy and control: Use authorised data, minimise personal information and keep human review on relevant outputs. ### Turn meetings, emails and tickets into operating memory Transcripts and threads are cleaned, summarised and converted into traceable decisions, tasks, deadlines and risks. - Operating example: When a process shows a similar need, emails and tickets are used to produce actionable digest and support traceability, with human review recommended. - ID: meeting-email-decision-memory - Area: Operations - Input: emails and tickets, transcripts and notes, tickets and requests - Output: actionable digest, roadmap and priorities - Value: traceability, faster decisions, transferable knowledge - Sectors: cross-company functions, professional services, technical offices and engineering - Related services: AI management consulting - Related courses: AI Operations, AI Workflow Redesign Lab - Search intents: AI for turn meetings, emails and tickets into operating memory, AI applications for operations, how to use AI in companies for turn meetings, emails and tickets into operating memory - Need signals: recurring decisions that are slow or based on incomplete information, critical knowledge concentrated in a few people - Human review: recommended - Risk: low - Privacy and control: Use authorised data, minimise personal information and keep human review on relevant outputs. ### Generate controlled documents from templates Reports, letters, contracts, FAQs and communications are produced from data and templates, with formal consistency and human review. - Operating example: When a process shows a similar need, structured database are used to produce controlled drafts and support time reduction, with human review required. - ID: controlled-document-generation - Area: Documents and knowledge - Input: structured database, internal documentation, contracts and policies - Output: controlled drafts, FAQs and answers - Value: time reduction, fewer errors, more governable compliance - Sectors: professional services, finance, control and regulated services, HSE, safety and technical services - Related services: AI management consulting - Related courses: AI course: managing documents with AI, AI Legal Ops and compliance documentation, AI Output Quality & Human Review - Search intents: AI for generate controlled documents from templates, AI applications for documents and knowledge, how to use AI in companies for generate controlled documents from templates - Need signals: manual copying between emails, spreadsheets and systems, recurring errors in documents, procedures or controls - Human review: required - Risk: medium - Privacy and control: Requires anonymisation, access control and specialist review when personal, legal, HR or regulated data is involved. ### Map processes and redesign workflows Real work is reconstructed as-is, read for bottlenecks and transformed into a to-be scenario with priorities and controls. - Operating example: When a process shows a similar need, transcripts and notes are used to produce roadmap and priorities and support clearer priorities, with human review recommended. - ID: process-mapping-redesign - Area: Operations - Input: transcripts and notes, logs and process states, emails and tickets, spreadsheets - Output: roadmap and priorities, business case - Value: clearer priorities, faster decisions, avoided costs - Sectors: cross-company functions, manufacturing, professional services - Related services: AI management consulting - Related courses: AI Workflow Redesign Lab, AI Business Case & ROI Sprint - Search intents: AI for map processes and redesign workflows, AI applications for operations, how to use AI in companies for map processes and redesign workflows - Need signals: recurring decisions that are slow or based on incomplete information, manual copying between emails, spreadsheets and systems, AI already used without shared rules - Human review: recommended - Risk: medium - Privacy and control: Use authorised data, minimise personal information and keep human review on relevant outputs. ### Triage emails, tickets and requests Incoming communications are classified by urgency, topic, responsibility and required action, with controlled response drafts. - Operating example: When a process shows a similar need, emails and tickets are used to produce actionable digest and support time reduction, with human review recommended. - ID: email-ticket-triage - Area: Operations - Input: emails and tickets, tickets and requests, internal documentation - Output: actionable digest, controlled drafts, priority ranking - Value: time reduction, more consistent service, clearer priorities - Sectors: cross-company functions, HSE, safety and technical services, technical offices and engineering - Related services: Technical AI software - Related courses: AI for customer service and ticket triage, AI Operations - Search intents: AI for triage emails, tickets and requests, AI applications for operations, how to use AI in companies for triage emails, tickets and requests - Need signals: manual copying between emails, spreadsheets and systems, recurring decisions that are slow or based on incomplete information - Human review: recommended - Risk: medium - Privacy and control: Use authorised data, minimise personal information and keep human review on relevant outputs. ### Plan shifts, resources and priorities Availability, constraints, skills, leave and demand are combined to propose feasible and explainable plans. - Operating example: When a process shows a similar need, spreadsheets are used to produce plan and assignments and support production efficiency, with human review required. - ID: scheduling-resource-allocation - Area: Operations - Input: spreadsheets, ERP and business systems, KPIs and time series - Output: plan and assignments, dashboards and filtered views - Value: production efficiency, faster decisions, avoided costs - Sectors: manufacturing, logistics and supply chain, HSE, safety and technical services - Related services: Technical AI software, Agentic data analysis - Related courses: AI Operations - Search intents: AI for plan shifts, resources and priorities, AI applications for operations, how to use AI in companies for plan shifts, resources and priorities - Need signals: planning that is still highly manual, historical data available but not turned into signals - Human review: required - Risk: medium - Privacy and control: Use authorised data, minimise personal information and keep human review on relevant outputs. ### Forecast demand and workload Historical orders, revenue, tickets or production become operating forecasts for purchasing, shifts and capacity. - Operating example: When a process shows a similar need, transactions and purchases are used to produce verifiable forecast and support faster decisions, with human review recommended. - ID: demand-workload-forecast - Area: Operations - Input: transactions and purchases, KPIs and time series, production data - Output: verifiable forecast, dashboards and filtered views - Value: faster decisions, avoided costs, production efficiency - Sectors: retail and e-commerce, manufacturing, logistics and supply chain - Related services: Agentic data analysis - Related courses: AI Business Case & ROI Sprint, AI Operations - Search intents: AI for forecast demand and workload, AI applications for operations, how to use AI in companies for forecast demand and workload - Need signals: historical data available but not turned into signals, planning that is still highly manual - Human review: recommended - Risk: medium - Privacy and control: Use authorised data, minimise personal information and keep human review on relevant outputs. ### Keep requirements, decisions and stakeholders alive Project meetings and documents feed an evolving dossier with requirements, latent conflicts, decisions and issues. - Operating example: When a process shows a similar need, transcripts and notes are used to produce roadmap and priorities and support traceability, with human review required. - ID: project-requirements-memory - Area: Operations - Input: transcripts and notes, requirements and specifications, internal documentation - Output: roadmap and priorities, risk map - Value: traceability, fewer errors, transferable knowledge - Sectors: technical offices and engineering, professional services, manufacturing - Related services: AI management consulting, Technical AI software - Related courses: AI Workflow Redesign Lab, AI Software Engineering - Search intents: AI for keep requirements, decisions and stakeholders alive, AI applications for operations, how to use AI in companies for keep requirements, decisions and stakeholders alive - Need signals: recurring decisions that are slow or based on incomplete information, critical knowledge concentrated in a few people - Human review: required - Risk: medium - Privacy and control: Use authorised data, minimise personal information and keep human review on relevant outputs. ### Read customer feedback, reviews and tickets Unstructured texts are aggregated by theme, sentiment, recurring needs and priority actions. - Operating example: When a process shows a similar need, text feedback are used to produce operational report and support more consistent service, with human review recommended. - ID: customer-feedback-intelligence - Area: Customer, marketing and sales - Input: text feedback, tickets and requests, public sources - Output: operational report, priority ranking - Value: more consistent service, recovered commercial value, clearer priorities - Sectors: retail and e-commerce, cross-company functions, professional services - Related services: Agentic data analysis - Related courses: AI for customer service and ticket triage, AI course: AI-driven marketing and communication - Search intents: AI for read customer feedback, reviews and tickets, AI applications for customer, marketing and sales, how to use AI in companies for read customer feedback, reviews and tickets - Need signals: abundant feedback that is not analysed, recurring decisions that are slow or based on incomplete information - Human review: recommended - Risk: medium - Privacy and control: Use authorised data, minimise personal information and keep human review on relevant outputs. ### Discover market and target needs Public sources and provided material are synthesised into maps of pain points, language, segments, partners and opportunities. - Operating example: When a process shows a similar need, public sources are used to produce operational report and support recovered commercial value, with human review recommended. - ID: market-customer-discovery - Area: Customer, marketing and sales - Input: public sources, text feedback, internal documentation - Output: operational report, business case - Value: recovered commercial value, clearer priorities, faster decisions - Sectors: retail and e-commerce, professional services, public bodies and territory - Related services: AI management consulting - Related courses: AI course: AI-driven marketing and communication, AI course: B2C and B2B sales with AI - Search intents: AI for discover market and target needs, AI applications for customer, marketing and sales, how to use AI in companies for discover market and target needs - Need signals: abundant feedback that is not analysed, recurring decisions that are slow or based on incomplete information - Human review: recommended - Risk: medium - Privacy and control: Use authorised data, minimise personal information and keep human review on relevant outputs. ### Codify brand voice and content Interviews, approved examples and commercial material become operating guidelines and coherent multi-channel drafts. - Operating example: When a process shows a similar need, internal documentation are used to produce policies and guardrails and support time reduction, with human review required. - ID: brand-voice-content-engine - Area: Customer, marketing and sales - Input: internal documentation, text feedback, public sources - Output: policies and guardrails, controlled drafts - Value: time reduction, recovered commercial value, traceability - Sectors: cross-company functions, retail and e-commerce, public bodies and territory - Related services: AI management consulting - Related courses: AI Brand Voice and communication, AI course: AI-driven marketing and communication - Search intents: AI for codify brand voice and content, AI applications for customer, marketing and sales, how to use AI in companies for codify brand voice and content - Need signals: recurring errors in documents, procedures or controls, manual copying between emails, spreadsheets and systems - Human review: required - Risk: low - Privacy and control: Use authorised data, minimise personal information and keep human review on relevant outputs. ### Support sales, pricing and recommendations Purchase history, catalogs and competitive information help build pitches, bundles, commercial priorities and price scenarios. - Operating example: When a process shows a similar need, transactions and purchases are used to produce operational recommendations and support recovered commercial value, with human review required. - ID: sales-pricing-recommendations - Area: Customer, marketing and sales - Input: transactions and purchases, internal documentation, public sources - Output: operational recommendations, business case - Value: recovered commercial value, faster decisions, clearer priorities - Sectors: retail and e-commerce, cross-company functions - Related services: Agentic data analysis - Related courses: AI course: B2C and B2B sales with AI, AI Business Case & ROI Sprint - Search intents: AI for support sales, pricing and recommendations, AI applications for customer, marketing and sales, how to use AI in companies for support sales, pricing and recommendations - Need signals: historical data available but not turned into signals, recurring decisions that are slow or based on incomplete information - Human review: required - Risk: medium - Privacy and control: Use authorised data, minimise personal information and keep human review on relevant outputs. ### Map skills and capability needs Skills, roles, future goals and trends are connected to define development, upskilling and reskilling priorities. - Operating example: When a process shows a similar need, aggregated hr data are used to produce roadmap and priorities and support transferable knowledge, with human review required. - ID: hr-competence-map - Area: People and HR - Input: aggregated HR data, internal documentation, public sources - Output: roadmap and priorities, operational report - Value: transferable knowledge, clearer priorities, faster training - Sectors: cross-company functions, training and knowledge-intensive organisations - Related services: AI management consulting - Related courses: AI People Ops, AI Adoption Manager / AI Champions - Search intents: AI for map skills and capability needs, AI applications for people and hr, how to use AI in companies for map skills and capability needs - Need signals: critical knowledge concentrated in a few people, AI already used without shared rules - Human review: required - Risk: medium - Privacy and control: Requires anonymisation, access control and specialist review when personal, legal, HR or regulated data is involved. ### Support recruiting and onboarding Job descriptions, applications and onboarding material are structured to prepare evaluations, communications and initial paths. - Operating example: When a process shows a similar need, cvs and applications are used to produce operational report and support time reduction, with human review required. - ID: recruiting-onboarding-support - Area: People and HR - Input: CVs and applications, aggregated HR data, manuals and training material - Output: operational report, controlled drafts - Value: time reduction, fewer errors, faster training - Sectors: cross-company functions, training and knowledge-intensive organisations - Related services: AI management consulting - Related courses: AI People Ops - Search intents: AI for support recruiting and onboarding, AI applications for people and hr, how to use AI in companies for support recruiting and onboarding - Need signals: manual copying between emails, spreadsheets and systems, critical knowledge concentrated in a few people - Human review: required - Risk: high - Privacy and control: Requires anonymisation, access control and specialist review when personal, legal, HR or regulated data is involved. ### Simplify recurring HR policies and requests Policies, benefits, procedures and recurring requests become FAQs, drafts and guided paths under HR control. - Operating example: When a process shows a similar need, aggregated hr data are used to produce faqs and answers and support more consistent service, with human review required. - ID: hr-policy-requests - Area: People and HR - Input: aggregated HR data, internal documentation, contracts and policies - Output: FAQs and answers, controlled drafts - Value: more consistent service, time reduction, more governable compliance - Sectors: cross-company functions - Related services: AI management consulting - Related courses: AI People Ops, Secure AI at Work - Search intents: AI for simplify recurring HR policies and requests, AI applications for people and hr, how to use AI in companies for simplify recurring HR policies and requests - Need signals: manual copying between emails, spreadsheets and systems, scattered documents that are hard to consult - Human review: required - Risk: high - Privacy and control: Requires anonymisation, access control and specialist review when personal, legal, HR or regulated data is involved. ### Define requirements, MVP and acceptance criteria A technical need becomes requirements, user stories, non-functional constraints, estimates and first-release boundaries. - Operating example: When a process shows a similar need, requirements and specifications are used to produce roadmap and priorities and support fewer errors, with human review required. - ID: software-requirements-and-mvp - Area: Technical and software - Input: requirements and specifications, transcripts and notes, internal documentation - Output: roadmap and priorities, tests and checklists - Value: fewer errors, traceability, avoided costs - Sectors: technical offices and engineering, manufacturing - Related services: Technical AI software - Related courses: AI Software Engineering, AI Coding Agents for software teams - Search intents: AI for define requirements, MVP and acceptance criteria, AI applications for technical and software, how to use AI in companies for define requirements, MVP and acceptance criteria - Need signals: recurring decisions that are slow or based on incomplete information, recurring errors in documents, procedures or controls - Human review: required - Risk: medium - Privacy and control: Treat code, specifications, industrial data and operational images as intellectual property; publish only anonymised examples. ### Accelerate development, refactoring and tests Existing code and specifications guide controlled code generation, unit tests, refactoring and quality audits. - Operating example: When a process shows a similar need, code and repositories are used to produce tests and checklists and support time reduction, with human review required. - ID: ai-assisted-coding-quality - Area: Technical and software - Input: code and repositories, requirements and specifications - Output: tests and checklists, operational report - Value: time reduction, fewer errors, traceability - Sectors: technical offices and engineering - Related services: Technical AI software - Related courses: AI Coding Agents for software teams, Secure AI SDLC, AI Software Engineering - Search intents: AI for accelerate development, refactoring and tests, AI applications for technical and software, how to use AI in companies for accelerate development, refactoring and tests - Need signals: recurring errors in documents, procedures or controls, manual copying between emails, spreadsheets and systems - Human review: required - Risk: high - Privacy and control: Treat code, specifications, industrial data and operational images as intellectual property; publish only anonymised examples. ### Read specifications and produce technical documentation Tenders, specifications, reports and technical sheets are analysed for critical requirements, risks and documentation drafts. - Operating example: When a process shows a similar need, tenders and specifications are used to produce operational report and support risk reduction, with human review required. - ID: technical-tender-documentation - Area: Technical and software - Input: tenders and specifications, internal documentation, technical drawings - Output: operational report, controlled drafts, risk map - Value: risk reduction, fewer errors, traceability - Sectors: technical offices and engineering, professional services, manufacturing - Related services: Technical AI software - Related courses: AI course: managing documents with AI, AI Output Quality & Human Review - Search intents: AI for read specifications and produce technical documentation, AI applications for technical and software, how to use AI in companies for read specifications and produce technical documentation - Need signals: scattered documents that are hard to consult, recurring errors in documents, procedures or controls - Human review: required - Risk: high - Privacy and control: Treat code, specifications, industrial data and operational images as intellectual property; publish only anonymised examples. ### Interpret images, drawings and technical material Photos, drawings and renders become descriptive sheets, component analyses, dimensions and verifiable technical narratives. - Operating example: When a process shows a similar need, operational photos are used to produce operational report and support transferable knowledge, with human review required. - ID: visual-technical-analysis - Area: Technical and software - Input: operational photos, technical drawings, internal documentation - Output: operational report, controlled drafts - Value: transferable knowledge, faster decisions, fewer errors - Sectors: technical offices and engineering, manufacturing - Related services: Technical AI software - Related courses: AI Software Engineering, AI Output Quality & Human Review - Search intents: AI for interpret images, drawings and technical material, AI applications for technical and software, how to use AI in companies for interpret images, drawings and technical material - Need signals: critical knowledge concentrated in a few people, scattered documents that are hard to consult - Human review: required - Risk: medium - Privacy and control: Treat code, specifications, industrial data and operational images as intellectual property; publish only anonymised examples. ### Build AI governance, policies and risk matrix Activities, data and decisions are classified into autonomy, supervision or exclusion zones with clear operating rules. - Operating example: When a process shows a similar need, internal documentation are used to produce policies and guardrails and support risk reduction, with human review required. - ID: ai-governance-policy-risk - Area: Governance, compliance and risk - Input: internal documentation, policies and guidelines, transcripts and notes - Output: policies and guardrails, risk map, roadmap and priorities - Value: risk reduction, more governable compliance, clearer priorities - Sectors: cross-company functions, finance, control and regulated services, HSE, safety and technical services - Related services: AI management consulting - Related courses: Operational AI Governance, Secure AI at Work, Managing AI - Search intents: AI for build AI governance, policies and risk matrix, AI applications for governance, compliance and risk, how to use AI in companies for build AI governance, policies and risk matrix - Need signals: AI already used without shared rules, recurring decisions that are slow or based on incomplete information - Human review: required - Risk: high - Privacy and control: Requires anonymisation, access control and specialist review when personal, legal, HR or regulated data is involved. ### Prepare compliance, legal and privacy documents Contracts, notices, registers, procedures and letters are prepared as preliminary support to be reviewed by specialists. - Operating example: When a process shows a similar need, contracts and policies are used to produce controlled drafts and support time reduction, with human review required. - ID: compliance-legal-privacy-drafting - Area: Governance, compliance and risk - Input: contracts and policies, internal documentation, completed forms - Output: controlled drafts, risk map - Value: time reduction, more governable compliance, risk reduction - Sectors: professional services, finance, control and regulated services, cross-company functions - Related services: AI management consulting - Related courses: AI Legal Ops and compliance documentation, Operational AI Governance - Search intents: AI for prepare compliance, legal and privacy documents, AI applications for governance, compliance and risk, how to use AI in companies for prepare compliance, legal and privacy documents - Need signals: manual copying between emails, spreadsheets and systems, recurring errors in documents, procedures or controls - Human review: required - Risk: high - Privacy and control: Requires anonymisation, access control and specialist review when personal, legal, HR or regulated data is involved. ### Test AI assistants against misuse Chatbots and assistants are stressed with manipulation, data leakage and conflicting instruction scenarios, then hardened with guardrails. - Operating example: When a process shows a similar need, internal documentation are used to produce tests and checklists and support risk reduction, with human review required. - ID: ai-system-security-tests - Area: Governance, compliance and risk - Input: internal documentation, requirements and specifications, policies and guidelines - Output: tests and checklists, policies and guardrails, operational report - Value: risk reduction, more governable compliance, more consistent service - Sectors: technical offices and engineering, cross-company functions - Related services: Technical AI software - Related courses: Secure AI SDLC, Secure AI at Work - Search intents: AI for test AI assistants against misuse, AI applications for governance, compliance and risk, how to use AI in companies for test AI assistants against misuse - Need signals: AI already used without shared rules, recurring errors in documents, procedures or controls - Human review: required - Risk: high - Privacy and control: Treat code, specifications, industrial data and operational images as intellectual property; publish only anonymised examples. ### Analyse HSE anomalies from operational images Site or department photos are read to identify non-compliance, risks and preventive measures to verify. - Operating example: When a process shows a similar need, operational photos are used to produce operational report and support risk reduction, with human review required. - ID: hse-visual-inspection - Area: Production, quality and maintenance - Input: operational photos, internal documentation - Output: operational report, risk map - Value: risk reduction, faster decisions, more governable compliance - Sectors: HSE, safety and technical services, manufacturing - Related services: Technical AI software - Related courses: AI for quality and non-conformities, AI Operations - Search intents: AI for analyse HSE anomalies from operational images, AI applications for production, quality and maintenance, how to use AI in companies for analyse HSE anomalies from operational images - Need signals: recurring errors in documents, procedures or controls, manual copying between emails, spreadsheets and systems - Human review: required - Risk: high - Privacy and control: Treat code, specifications, industrial data and operational images as intellectual property; publish only anonymised examples. ### Optimise production, orders and quality Customer schedules, ERP, cycles, non-conformities and historical costs support priorities, quotes and corrective actions. - Operating example: When a process shows a similar need, erp and business systems are used to produce plan and assignments and support production efficiency, with human review required. - ID: production-planning-quality - Area: Production, quality and maintenance - Input: ERP and business systems, production data, spreadsheets - Output: plan and assignments, operational report, operational recommendations - Value: production efficiency, fewer errors, avoided costs - Sectors: manufacturing, logistics and supply chain - Related services: Technical AI software, Agentic data analysis - Related courses: AI Operations, AI for quality and non-conformities - Search intents: AI for optimise production, orders and quality, AI applications for production, quality and maintenance, how to use AI in companies for optimise production, orders and quality - Need signals: planning that is still highly manual, recurring errors in documents, procedures or controls - Human review: required - Risk: medium - Privacy and control: Treat code, specifications, industrial data and operational images as intellectual property; publish only anonymised examples. ### Manage maintenance, assets and spare parts Failure history, sensors and interventions become control priorities, maintenance windows and operating alerts. - Operating example: When a process shows a similar need, sensors and telemetry are used to produce alerts and thresholds and support production efficiency, with human review required. - ID: maintenance-and-asset-risk - Area: Production, quality and maintenance - Input: sensors and telemetry, production data, logs and process states - Output: alerts and thresholds, priority ranking, dashboards and filtered views - Value: production efficiency, avoided costs, risk reduction - Sectors: manufacturing, logistics and supply chain - Related services: Agentic data analysis - Related courses: AI Operations, AI Business Case & ROI Sprint - Search intents: AI for manage maintenance, assets and spare parts, AI applications for production, quality and maintenance, how to use AI in companies for manage maintenance, assets and spare parts - Need signals: historical data available but not turned into signals, planning that is still highly manual - Human review: required - Risk: medium - Privacy and control: Treat code, specifications, industrial data and operational images as intellectual property; publish only anonymised examples. ### Create training, quizzes and slides from internal material Manuals, slides and scattered documents become syllabi, quizzes, case studies and role-based learning material. - Operating example: When a process shows a similar need, manuals and training material are used to produce faqs and answers and support faster training, with human review recommended. - ID: internal-training-assets - Area: Training and internal memory - Input: manuals and training material, internal documentation, transcripts and notes - Output: FAQs and answers, controlled drafts - Value: faster training, transferable knowledge, more consistent service - Sectors: training and knowledge-intensive organisations, cross-company functions - Related services: AI management consulting - Related courses: Role-Based AI Literacy & Responsible Use, Managing AI for mixed company teams, AI course: managing documents with AI - Search intents: AI for create training, quizzes and slides from internal material, AI applications for training and internal memory, how to use AI in companies for create training, quizzes and slides from internal material - Need signals: critical knowledge concentrated in a few people, scattered documents that are hard to consult - Human review: recommended - Risk: low - Privacy and control: Use authorised data, minimise personal information and keep human review on relevant outputs. ### Build assistants for company memory Internal documentation feeds Q&A assistants, including voice interfaces, that answer with sources and clear usage boundaries. - Operating example: When a process shows a similar need, internal documentation are used to produce semantic search and support transferable knowledge, with human review required. - ID: company-memory-assistants - Area: Training and internal memory - Input: internal documentation, manuals and training material, policies and guidelines - Output: semantic search, FAQs and answers, policies and guardrails - Value: transferable knowledge, more consistent service, time reduction - Sectors: cross-company functions, manufacturing, training and knowledge-intensive organisations - Related services: Technical AI software - Related courses: RAG Engineering for reliable AI systems, Semantic search and AI knowledge bases, Secure AI at Work - Search intents: AI for build assistants for company memory, AI applications for training and internal memory, how to use AI in companies for build assistants for company memory - Need signals: critical knowledge concentrated in a few people, scattered documents that are hard to consult - Human review: required - Risk: medium - Privacy and control: Use authorised data, minimise personal information and keep human review on relevant outputs. ### Produce executive reports and visual assets Data, KPIs and heterogeneous material become narrative reports, infographics, presentations and coherent visual content. - Operating example: When a process shows a similar need, kpis and time series are used to produce operational report and support faster decisions, with human review recommended. - ID: executive-reports-visual-assets - Area: Cross-functional tools - Input: KPIs and time series, spreadsheets, internal documentation - Output: operational report, dashboards and filtered views, controlled drafts - Value: faster decisions, traceability, recovered commercial value - Sectors: cross-company functions - Related services: AI management consulting - Related courses: AI Output Quality & Human Review, AI Brand Voice and communication - Search intents: AI for produce executive reports and visual assets, AI applications for cross-functional tools, how to use AI in companies for produce executive reports and visual assets - Need signals: historical data available but not turned into signals, manual copying between emails, spreadsheets and systems - Human review: recommended - Risk: low - Privacy and control: Use authorised data, minimise personal information and keep human review on relevant outputs. ### Detect anomalies and degradation in machinery Time series and industrial sensors are used for alerts, degradation analysis and predictive maintenance with verifiable thresholds. - Operating example: When a process shows a similar need, sensors and telemetry are used to produce alerts and thresholds and support production efficiency, with human review required. - ID: predictive-maintenance-anomalies - Area: Data science and decisions - Input: sensors and telemetry, production data, KPIs and time series - Output: alerts and thresholds, verifiable forecast, dashboards and filtered views - Value: production efficiency, avoided costs, risk reduction - Sectors: manufacturing, logistics and supply chain - Related services: Agentic data analysis, Technical AI software - Related courses: AI Business Case & ROI Sprint, AI Operations - Search intents: AI for detect anomalies and degradation in machinery, AI applications for data science and decisions, how to use AI in companies for detect anomalies and degradation in machinery - Need signals: historical data available but not turned into signals, planning that is still highly manual - Human review: required - Risk: medium - Privacy and control: Treat code, specifications, industrial data and operational images as intellectual property; publish only anonymised examples. ### Segment customers, churn and cross-selling Transactional and behavioural histories become segments, risk rankings, bundles and differentiated commercial actions. - Operating example: When a process shows a similar need, transactions and purchases are used to produce priority ranking and support recovered commercial value, with human review required. - ID: customer-segmentation-churn-crosssell - Area: Data science and decisions - Input: transactions and purchases, text feedback, KPIs and time series - Output: priority ranking, operational recommendations, business case - Value: recovered commercial value, clearer priorities, more consistent service - Sectors: retail and e-commerce, finance, control and regulated services - Related services: Agentic data analysis - Related courses: AI course: B2C and B2B sales with AI, AI Business Case & ROI Sprint - Search intents: AI for segment customers, churn and cross-selling, AI applications for data science and decisions, how to use AI in companies for segment customers, churn and cross-selling - Need signals: historical data available but not turned into signals, abundant feedback that is not analysed - Human review: required - Risk: medium - Privacy and control: Requires anonymisation, access control and specialist review when personal, legal, HR or regulated data is involved. ### Optimise energy, quality and line performance Telemetry, consumption, quality and machine parameters reveal efficient profiles, waste and operating recommendations. - Operating example: When a process shows a similar need, sensors and telemetry are used to produce dashboards and filtered views and support production efficiency, with human review required. - ID: energy-line-optimization - Area: Data science and decisions - Input: sensors and telemetry, production data, KPIs and time series - Output: dashboards and filtered views, operational recommendations, business case - Value: production efficiency, avoided costs, faster decisions - Sectors: manufacturing - Related services: Agentic data analysis - Related courses: AI Business Case & ROI Sprint, AI for quality and non-conformities - Search intents: AI for optimise energy, quality and line performance, AI applications for data science and decisions, how to use AI in companies for optimise energy, quality and line performance - Need signals: historical data available but not turned into signals, recurring errors in documents, procedures or controls - Human review: required - Risk: medium - Privacy and control: Treat code, specifications, industrial data and operational images as intellectual property; publish only anonymised examples. ### Analyse territories, profitability and trends Aggregated fiscal, territorial or commercial data become maps, clusters, profitability drivers and decision roadmaps. - Operating example: When a process shows a similar need, transactions and purchases are used to produce dashboards and filtered views and support faster decisions, with human review recommended. - ID: territorial-profitability-analytics - Area: Data science and decisions - Input: transactions and purchases, public sources, KPIs and time series - Output: dashboards and filtered views, operational report, business case - Value: faster decisions, clearer priorities, recovered commercial value - Sectors: public bodies and territory, retail and e-commerce, finance, control and regulated services - Related services: Agentic data analysis - Related courses: AI Business Case & ROI Sprint - Search intents: AI for analyse territories, profitability and trends, AI applications for data science and decisions, how to use AI in companies for analyse territories, profitability and trends - Need signals: historical data available but not turned into signals, recurring decisions that are slow or based on incomplete information - Human review: recommended - Risk: medium - Privacy and control: Use authorised data, minimise personal information and keep human review on relevant outputs. ### Know when not to build a model The first value can be a negative verdict: available data does not yet contain the useful signal and collection must improve. - Operating example: When a process shows a similar need, kpis and time series are used to produce operational report and support avoided costs, with human review recommended. - ID: data-quality-go-no-go - Area: Data science and decisions - Input: KPIs and time series, transactions and purchases, logs and process states - Output: operational report, business case, roadmap and priorities - Value: avoided costs, clearer priorities, traceability - Sectors: cross-company functions - Related services: Agentic data analysis - Related courses: AI Business Case & ROI Sprint - Search intents: AI for know when not to build a model, AI applications for data science and decisions, how to use AI in companies for know when not to build a model - Need signals: historical data available but not turned into signals, recurring decisions that are slow or based on incomplete information - Human review: recommended - Risk: low - Privacy and control: Use authorised data, minimise personal information and keep human review on relevant outputs. ### Use AI as a discovery lab Cases, material and constraints are explored to generate hypotheses, scenarios, concepts, role simulations and opportunities to verify. - Operating example: When a process shows a similar need, internal documentation are used to produce operational report and support recovered commercial value, with human review recommended. - ID: creative-rnd-discovery - Area: Cross-functional tools - Input: internal documentation, text feedback, public sources - Output: operational report, operational recommendations, controlled drafts - Value: recovered commercial value, clearer priorities, faster decisions - Sectors: cross-company functions - Related services: AI management consulting - Related courses: AI Workflow Redesign Lab, AI Output Quality & Human Review, AI Brand Voice and communication - Search intents: AI for use AI as a discovery lab, AI applications for cross-functional tools, how to use AI in companies for use AI as a discovery lab - Need signals: recurring decisions that are slow or based on incomplete information, abundant feedback that is not analysed - Human review: recommended - Risk: low - Privacy and control: Use authorised data, minimise personal information and keep human review on relevant outputs. ## From map to real process: start with a call. This page helps orientation. The solution is designed only after reviewing sector, constraints, available data, responsibilities and the decision to improve. 1. **Initial context**: Before the meeting Artik Lab prepares a first reading of public context and any material shared by the company. 2. **Structured conversation**: During the call two or three high-potential workflows are identified, together with constraints, risks and urgencies. 3. **Targeted proposal**: The output is a calibrated path: training, consulting, data analysis or technical prototype, with expected results and control criteria. ## FAQ ### Is the Atlas a catalog of ready-made products? No. It is a map of concrete examples. Artik Lab starts from a discovery call and designs the path around the client's real process. ### Are all applications automations? No. Some are training, some analysis, some technical software or governance. AI can assist, suggest, find signals or draft, while sensitive decisions remain governed. ### How are recognisable cases avoided? Cards aggregate patterns and sectors, removing names, clients, natural persons, proprietary data and details that could identify a project.