Question 1

Why not just use Atlassian Rovo or Microsoft Copilot?

Accepted Answer

Because they answer a different question. Rovo and Copilot search what your team already wrote down. The knowledge that costs you most was never written down at all. Stackbridge captures it at the moment it exists: the Slack thread where the incident was actually solved, the ticket as it closes, the interview with the engineer working their notice period. A human then approves it, so what comes back was checked by someone on your team, with a named approver and a tamper-evident trail behind it. Rovo searches what you wrote down; Stackbridge makes sure it got written down, and can prove who knew what, when. In practice they sit side by side: keep Rovo for finding documents, use Stackbridge for the operational knowledge that never becomes one.

Question 2

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 3

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. That is what lets it 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 4

Where does our data live?

Accepted Answer

On your infrastructure. On-premises is how Stackbridge is primarily sold: deployed via Helm on your own Kubernetes, air-gapped if you need it, with no inbound ports required. Your knowledge never has to leave your network. 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. Cloud-hosted plans exist for smaller teams who want to start quickly.

Question 5

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 6

Who decides what actually enters the knowledge base?

Accepted Answer

A person, always. That is the product, not a formality. Everything captured lands as a draft; nothing is published by the AI alone. Review is built to take seconds rather than minutes: approve, reject or jump to the next entry from the keyboard, or straight from the Slack thread where the work actually happened. Every decision is attributed to a named person and written to a tamper-evident, hash-chained audit trail, so months later you can show who approved a fact and that the record has not been altered since. Read-only roles exist for people who should see the knowledge base but never change it. That approval layer is also the part nobody can copy: anyone can re-index your Confluence, but only your team has the judgment about what was worth keeping.

Question 7

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. Your coding agent can then 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 8

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 9

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	Rovo · Copilot · Glean	Wiki / Confluence	Slack search
Who has to write it down?	Nobody. It reads the ticket and the thread	Nobody, but it can only surface what someone already wrote	A person, after the incident, on their own time	Nobody, but nothing is written either
Is it still true in six months?	Confluence re-syncs hourly; repos version-diffed on demand	Indexes what is there, including the page that went stale	Rots quietly from the day it is published	Accurate, but scrolled past retention
Can you find a fix from a symptom?	Semantic search plus exact error-signature match	Strong search, if somebody documented the fix at all	Only if you guess the words someone titled it	Keyword-only, and the fix is 200 messages down
Does it reach you during the incident?	Posts the closest past fix into the thread, unasked	You have to stop and go ask it	Sits in a tab nobody opens at 3am	You are already in it, and still searching
Can it run with no internet egress?	On-premises via Helm, local model, no inbound ports	VPC and regional options exist, but the vendor still operates it	Data Center edition, at Data Center prices	SaaS only
Does it tell you what it is missing?	Ranked gap report from every unanswered question	Returns its best guess and stops	No idea what was never written	No
Can you prove who approved a fact, and that it was not altered?	Named approver, hash-chained audit trail, per-entry read log	Indexes everything; nothing is reviewed or approved	Page history, editable by anyone with edit rights	No

Stop solving the same incident twice.

Checkout went down after a release

Your team already solved it. Nobody can find it.

The same incident, twice

Knowledge that walks out the door

The same question, forever

Chaos in. Memory out.

Connect your stack

Your LLM structures it

A human approves it

It finds the engineer

Watch it happen, four ways.

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

“But Rovo already does this.”

Your knowledge, inside every AI tool

Your knowledge never leaves your walls

Fully on-premises 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

Built to survive your auditor, not to dodge them

One repeat outage costs more than a year of this.

Quoted per deployment, not per seat.

On-premises

Hosted

The questions buyers actually ask

Stop losing what you already paid for.