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.

Terms of use

01Who these terms are with

02This page does not license the software

03What you may do with this site

04What is ours

05Accuracy, and the limits of a marketing page

06Liability

07Links out

08Privacy

09Changes to these terms

10Law and jurisdiction

Who we are