Hand your repetitive work to AI modules

Problem

Your central 'info@', 'support@' or CRM inbox receives hundreds of requests a day. An agent has to open, read and categorise each one. About 70% of this work is repetitive evaluation — one of the very first jobs an AI can take over.

Solution

Each incoming message is sent to an LLM and turned into a structured decision: predefined category + priority score + routing target. Output JSON flows through n8n to your CRM, Slack or Jira.

Capabilities

• Multi-level category tree (Department → Topic → Sub-topic)
• 0-100 priority scoring + 'urgency' trigger flag
• Dynamic rule engine: '3+ requests in 24h from same customer = VIP'
• One-click integration APIs for CRM / Helpdesk / Slack
• 'Needs review' queue + human approval for unknown topics
• Monthly distribution report: which categories are growing or shrinking?

Workflow

Expected outcomes

• First-response time drops from 4 hours to 20 minutes
• Mis-routed requests drop from 30% to 5%
• About $120/month in LLM cost replaces a full-time triage seat

Example sector uses

Problem

Executives end up reading 12 PDF reports over the weekend. An 80-page sector analysis carries 3 pages of real value; finding which 3 is itself work. Meeting transcripts get saved and forgotten — action items never land on anyone's calendar.

Solution

On upload a document is chunked and sent to an LLM; out comes a structured payload: summary + all action items + owners + dates + risk flags. Action items are auto-created in Asana/Notion/Linear and the owner is notified.

Capabilities

• PDF, DOCX, email thread and meeting transcript support
• Automatic action extraction: 'who, will do what, by when'
• Executive summary (3 paragraphs + 5 key messages)
• Smart matching of dates, people and company names
• Risk / warning flags: 'contract expiring', 'delay risk'
• Auto-task creation in Asana / Notion / Linear / Trello

Workflow

Expected outcomes

• 80-page report → 3-minute summary + 12 action items
• Executive 'paperwork' time drops from 8 hours/week to 90 minutes
• Meeting action follow-through rate goes from 35% to 85%

Example sector uses

Problem

A new hire spends their first week asking 'who should I ask about this?' A seasoned employee asks a teammate on Slack 'what was the procedure again?' six times a day. All the knowledge exists, but it's scattered across 14 places (Notion + Drive + Confluence + SharePoint + an old wiki + email). You have a wiki — nobody can search it.

Solution

An ingestion pipeline indexes all sources (Drive, Notion, Confluence, SharePoint, Postgres tables) into a vector DB daily, plus a Slack / Teams bot that answers questions with citations. Every question is logged — knowledge gaps become visible.

Capabilities

• RAG-grounded answers with citations (doc link on every reply)
• Role-based access: 'finance docs only for the finance team'
• Multi-source ingestion (14+ adapters)
• Slack / Microsoft Teams bot — zero install for users
• Monthly 'unanswered questions' report = your documentation gap
• On low confidence falls back to 'I don't have that in my docs'

Workflow

Expected outcomes

• New-hire onboarding drops from 3 weeks to 1 week
• Seniors' 'answering questions' load drops by 70%
• Thousands of monthly questions answered instantly — no queue

Example sector uses