SurfSense · LLM Wiki · Documentation · Automation

AI-native documents and reporting for developers

LLM Wiki + SurfSense: a corporate knowledge base that assembles and updates itself automatically.

3 blocksSurfSense + LLM WikiDevelopers / PMs / Tech leads
AI-native documents and reporting for developers
0manual copy-paste
24/7Wiki update
set it and forget it
IPcontrol over data

Our clients

Clients and partners

Capital Group
FSK Group
SMLT
Tochno
Dogma
Sber City
FM Logistic
Danone
Relief Center
Pandora
AI-native documents and reporting for developers
Saint-Gobain
Askona
FIX PRICE
Snezhnaia Koroleva
Muztorg
TVOE
Greenway
Polaris
Campari
Yandex
Lenta
International perfume and cosmetics brand
AI-native documents and reporting for developers
RAEC
EKF
L'Etoile
Inventive Retail Group

The Problem

"How am I supposed to cram 1000 tickets into a prompt?"

What you get

Key Takeaways

  1. Set up SurfSense as a corporate data gateway
  2. Deploy the SurfSense (Extraction) → LLM Wiki (Synthesis) pipeline
  3. Build the structure of the Wiki repository
  4. Learn to write synthesizer prompts
  5. Automate the "evening update" of the knowledge base
  6. Set up a knowledge graph in Obsidian / Notion
  7. Ensure data security and control over IP
  8. Master the methodology for auditing an automated Wiki

Program

Workshop Program

01 · SurfSense

Infrastructure — "we stop being data couriers"

Configuring connectors to Jira, Slack, Confluence and Notion.

02 · Karpathy's TRD + Claude / OpenClaw

Memory architecture

A prompt turns the data stream into structured Markdown files.

03 · Obsidian / Notion

Visualization and search

Checking links: a Jira task ↔ a director's contact in the Wiki.

How it works

How it works

1Delta collection
2Synthesis
3Morning query

More

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