Software Engineering

Building a queryable knowledge base from meeting transcripts and docs

Automates syncing and indexing of meeting transcripts (e.g., Granola) and documents (e.g., Notion) so context can be searched and reused across future conversations and work.

Why the human is still essential here

The human decides what knowledge matters and how it should be structured/used; AI helps with ingestion, organization, and retrieval but the user provides judgment and context.

How people use this

Transcript-to-Notion knowledge sync

Automatically turn each meeting transcript into a structured Notion page with attendees, decisions, action items, and backlinks to related projects.

Granola / Notion

RAG Q&A over company docs

Index Notion docs and meeting notes into a vector store so a chat interface can answer questions with citations to the original sources.

LlamaIndex / Pinecone / OpenAI API

Semantic enterprise search layer

Deploy a unified internal search experience across Slack, docs, and tickets so users can retrieve relevant context with permissions respected.

Glean

Community stories (1)

LinkedIn

Most people use AI coding tools to write code.

Most people use AI coding tools to write code. I'm using Claude Code to help build a personal operating system as a CEO.

I've hooked it up to many different tools that are important to us: Todoist, Slack, Linear, Notion, Salesforce, Gong, email, and so on. I've found it to be far more useful and sticky than the vanilla ChatGPT approach.


I now think of it as a mini coach+chief of staff. Some particularly valuable ways I'm using it right now or things I'm experimenting with:


🧠 Distillation of knowledge

I have a knowledge base that auto-syncs meeting transcripts from Granola, documents from Notion, indexes them by type (1:1s, customer calls, leadership), and makes them queryable across any conversation. It syncs to my Mac hourly. This means I can go see someone at their desk, Granola the conversation, and the context is now available for any future work I want to do with Claude.


🧘‍♂️ Removing distractions

A small but useful skill I've built is the slack-cleanup skill. It scans my 170+ channels, checks 6 months of activity, cross-references Salesforce and many other tools before deciding what to leave. Keeps me focused.


💭 Self-reflection and persistent memory

It knows our yearly initiatives, quarterly targets, and I write a weekly check-in before the week starts. So when I ask "what should I focus on today?", it doesn't just read my calendar. It checks whether my week is tracking against what actually matters. It's a gentle and useful accountability system.


🧐 Thinking quality

I'm experimenting with a /frame skill that takes messy context and distils it into a one-sentence problem, the binding constraint, and the eigenquestion — the question whose answer determines the answers to all the other questions. I use it to force forward progress on hard problems.


🔎 "Cheap", one-off analyses

For example, a competitive deep-dive into win rates, backed by specific customer quotes on gaps, with Gong snippets. That was a multi-day project compressed into minutes, backed by hard evidence, so I can sense check correctness.


Ultimately, I think the most benefit comes from work that would never have happened in the first place because it was far too expensive to do, vs. making me faster at what I already do. I recommend!

SW
Stephen WhitworthCEO at incident.io
Feb 25, 2026