Question 1

How is this different from Confluence or a wiki?

Accepted Answer

A wiki depends on someone remembering to write things down, and someone else remembering the page exists. Stackbridge captures knowledge from the tools your team already uses, structures it into a typed schema, and pushes it back at the moment of need — into the Slack thread, the search box, or your coding agent. It is memory, not a filing cabinet. And you do not have to migrate: point it at your existing Confluence space and it syncs hourly, so the knowledge base is not empty on day one.

Question 2

Isn’t this just ChatGPT with our documents in it?

Accepted Answer

No. A general assistant answers from a pile of text you pasted in. Stackbridge extracts every incident into structured fields — severity, service, error signature, root cause, fix, prevention — so it can match a new incident to an old one by symptom and by exact error signature, then post that match into the channel unprompted. It also tracks what it could not answer and turns that into a ranked list of the knowledge you are missing. None of that falls out of a chat window.

Question 3

Where does our data live? Can we run it on-prem?

Accepted Answer

Yes — fully on-prem deployable via Helm on your own Kubernetes, including air-gapped. Your knowledge never has to leave your infrastructure. We never train on customer data, and you can bring your own LLM (a private Claude key or a fully local model), so no content is sent to any third party you have not chosen yourself.

Question 4

Our security team will not open a port for this. Does it still work?

Accepted Answer

Yes. The whole platform runs inside your network, and the Slack integration can dial out to Slack rather than waiting to be called — so the bot, slash commands and thread capture all work with nothing exposed to the internet. The AI runs on hardware you choose: a local model on your own box, or your own API key.

Question 5

Who decides what actually enters the knowledge base?

Accepted Answer

A person, always. Everything captured lands as a draft for review — nothing is published by the AI alone. Approve or reject from the web app or straight from the Slack thread, with every action attributed and logged. Read-only roles exist for people who should see the knowledge base but never change it.

Question 6

Does it work inside Claude Code, Cursor, and other AI tools?

Accepted Answer

Yes. Stackbridge exposes your knowledge base over the Model Context Protocol. Connect any MCP client with one command and a browser login — no tokens to copy — and your coding agent can search past incidents and file new fixes without leaving the editor. Access stays scoped to your organisation and to the signed-in user’s role.

Question 7

How long until it is useful?

Accepted Answer

Connect your tools in an afternoon. Point it at the Confluence space you already have and it structures what is already written down, so it answers questions from day one; the incident memory then compounds from every ticket that closes after that. Stackbridge also shows you its own arithmetic — engineer-hours saved from prevented repeats, measured against your real approved incidents rather than a slide.

Question 8

What does it cost to run our own model?

Accepted Answer

Nothing extra from us — bring-your-own-model is included on every plan, not an upsell. Embeddings run locally by default on a small open model, and chat and search can point at different machines, so a shared GPU box handles the heavy model while embeddings stay next to the app. On CPU-only hardware it is slower, not broken; extraction is queued and given a longer budget precisely for that case.

	Stackbridge	Wiki / Confluence	Slack search	ChatGPT & co.
Who has to write it down?	Nobody. It reads the ticket and the thread	A person, after the incident, on their own time	Nobody — but nothing is written either	You paste the context in by hand, every time
Is it still true in six months?	Re-synced and version-diffed as sources change	Rots quietly from the day it is published	Accurate, but scrolled past retention	Knows nothing about your systems at all
Can you find a fix from a symptom?	Semantic search plus exact error-signature match	Only if you guess the words someone titled it	Keyword-only, and the fix is 200 messages down	Confidently invents one
Does it reach you during the incident?	Posts the closest past fix into the thread, unasked	Sits in a tab nobody opens at 3am	You are already in it, and still searching	Another window, another copy-paste
Can it run with no internet egress?	On-prem via Helm, local model, no inbound ports	Data Center edition, at Data Center prices	SaaS only	Your incidents leave your network
Does it tell you what it is missing?	Ranked gap report from every unanswered question	No idea what was never written	No	No

Stop solving the same incident twice.

Checkout went down after a release

Chaos in. Memory out.

Connect your stack

Your LLM structures it

A human approves it

It finds the engineer

Ask a question. Get the answer your team already found.

Everything your team knows, working for you

Semantic search

Proactive matching

Structured, not dumped

Auto runbooks

Survives attrition

Answers where you already are

Knows what it doesn't know

Audit & compliance

“We already have Confluence.”

Your knowledge, inside every AI tool

Your knowledge never leaves your walls

Fully on-prem deployable

No training on your data

Bring your own model. No lock-in.

No inbound ports. Not even for Slack.

The paperwork, already done

A human gate on everything

One repeat outage costs more than a year of this.

Priced against the cost of forgetting

Team

Business

Enterprise

Enterprise+

The questions buyers actually ask

Stop losing what you already paid for.