Software Engineering

Rapid competitive analysis using internal product and sales evidence

Runs fast, “cheap” one-off analyses (e.g., competitive deep-dives) grounded in internal data such as win rates and Gong call snippets/quotes to surface gaps and hypotheses quickly.

Why the human is still essential here

The human frames the analysis, checks correctness, and decides what actions to take; AI compresses research time but requires validation and interpretation.

How people use this

Win/loss theme extraction

Analyze Salesforce opportunities tagged with competitors to surface repeatable win/loss reasons and generate a prioritized list of hypotheses to validate.

Salesforce / ChatGPT

Call-snippet competitor insight mining

Cluster and summarize Gong snippets that mention a competitor to produce evidence-backed talking points and objection handling.

Gong / Claude

Auto-drafted competitive battlecard

Compile findings into a battlecard with key differentiators, landmines, and source links that PMM can edit and publish to the team wiki.

Notion / Confluence / ChatGPT

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