Software Engineering

Rapid competitive analysis using internal product and sales evidence

Runs fast, β€œcheap” 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

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Related Prompts (4)

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.


πŸ§˜β€β™‚οΈ 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.


πŸ’­ 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 β€” the question whose answer determines the answers to all the other questions. I use it to force forward progress on hard problems.


πŸ”Ž "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