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Knowledge base digest

AI implementation cases

A live-style feed from our knowledge base: compact cases that show how bots and agents turn documents, messages, statuses, and customer data into working AI processes.

case study

Page year: 2026

Artefact and IBM watsonx.ai: customer insight for a major French bank

Useful for sales and marketing: AI turns customer data into clear segments and helps build offers, communications, and upsell hypotheses.

What to apply: Start with a read-only agent that reads approved sources, checks statuses, and alerts on blockers, owners, and deadline risk.

AI patterns: customer-insight-agent, persona-analysis, data-analysis

Source: IBM Case Studies

industry report

Page year: 2026

Habr: AI workflows, RAG, corporate memory, and autonomous agents

A strong enterprise automation reference: AI becomes a participant in workflows, not only a chat window. It can classify requests, extract document data, notify owners, assign work, and use RAG over company-specific memory.

What to apply: Start with a read-only agent that reads approved sources, checks statuses, and alerts on blockers, owners, and deadline risk.

AI patterns: ai-workflow, corporate-memory-rag, service-desk-agent

Source: Habr / Practice

press case

Page year: 2026

8Flow.ai: automating customer support workflows

Useful for service teams where every case requires many clicks across support tools: start by assisting agents, reducing copy-paste, and alerting on stalled cases.

What to apply: Start with a read-only agent that reads approved sources, checks statuses, and alerts on blockers, owners, and deadline risk.

AI patterns: support-agent-assist, workflow-mining, tool-integration

Source: TechCrunch

industry report

Page year: 2026

Generative AI use cases across marketing, sales, operations, legal, HR, and support

A cross-industry reference that connects quiz answers to business functions and helps choose AI scenarios across sales, marketing, operations, support, HR, and document workflows.

What to apply: Connect customer data, offers, and communication into an AI layer that prepares next actions and follow-up drafts.

AI patterns: sales-support-chatbot, content-generation, document-analysis

Source: McKinsey / QuantumBlack

press case

Page year: 2026

Midea: global AI contact center and customer service operations

Useful for companies with international support, sales, and fragmented channels: the AI layer combines customer requests, profiles, self-service, and operating metrics.

What to apply: Connect customer data, offers, and communication into an AI layer that prepares next actions and follow-up drafts.

AI patterns: contact-center-ai, chatbot, customer-profile

Source: AWS / Amazon Press Center

case study

Page year: 2026

Habr: RAG startup lessons on context quality, model routing, and validation

Useful as an engineering warning: business AI quality depends less on the model alone and more on the cognitive pipeline: context selection, model routing, retrieval quality, validation, and cost control.

What to apply: Connect customer data, offers, and communication into an AI layer that prepares next actions and follow-up drafts.

AI patterns: rag-quality-control, model-routing, context-filtering

Source: Habr / Practice

press case

Page year: 2026

Tektonic AI: GenAI agents for automating business operations

Relevant for sales and operations teams with quote, renewal, approval, and document-heavy workflows: GenAI agents can coordinate dynamic steps across data silos instead of forcing a rigid RPA flow.

What to apply: Start with a read-only agent that reads approved sources, checks statuses, and alerts on blockers, owners, and deadline risk.

AI patterns: genai-agent, quote-renewal-automation, natural-language-workflow

Source: TechCrunch

press case

Page year: 2026

Smartsheet: AI assistant in Slack for organizational knowledge

Fits companies where knowledge is scattered across documents, chats, and departments: an AI assistant inside the work channel speeds up answers and reduces manual search.

What to apply: Start with a read-only agent that reads approved sources, checks statuses, and alerts on blockers, owners, and deadline risk.

AI patterns: employee-assistant, slack-agent, knowledge-search

Source: AWS / Amazon Press Center

case study

Page year: 2026

Habr: AI + RAG inside a reporting system with local inference

Relevant for companies that live in reports and spreadsheets: AI can explain reports, compare datasets, surface anomalies, and suggest the right report, while local inference can protect sensitive internal data.

What to apply: Start with a read-only agent that reads approved sources, checks statuses, and alerts on blockers, owners, and deadline risk.

AI patterns: report-analysis-agent, local-llm, rag-over-reports

Source: Habr / Practice

press case

Page year: 2026

IrisGo: on-device AI desktop agent for business tasks

Relevant for companies that need an agent inside the worker's computer, not only inside one SaaS tool: the pattern is local context, routine task execution, and stronger privacy boundaries.

What to apply: Start with a read-only agent that reads approved sources, checks statuses, and alerts on blockers, owners, and deadline risk.

AI patterns: desktop-agent, on-device-processing, clerical-task-automation

Source: TechCrunch

press case

Page year: 2026

Brightcove: expert bot for internal support knowledge

Shows a safer first step: an internal expert bot on documentation and product notes before external customer automation, helping teams answer faster with fewer escalations.

What to apply: Start with a read-only agent that reads approved sources, checks statuses, and alerts on blockers, owners, and deadline risk.

AI patterns: internal-chatbot, knowledge-base-agent, support-assistant

Source: AWS / Amazon Press Center

case study

Page year: 2026

Habr: production-ready AI agent with RAG, tools, prompts, and CRM actions

A practical pattern for client-facing and internal agents: the agent answers from the knowledge base, creates and checks requests in CRM, schedules meetings, and works only when the surrounding architecture controls retrieval, tools, prompts, and handoffs.

What to apply: Start with a read-only agent that reads approved sources, checks statuses, and alerts on blockers, owners, and deadline risk.

AI patterns: react-agent, advanced-rag, tool-calling

Source: Habr / Practice

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