Why not just use Atlassian Rovo or Microsoft Copilot?

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.

How is this different from Confluence or a wiki?

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.

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

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.

Where does our data live?

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.

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

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.

Who decides what actually enters the knowledge base?

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.

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

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.

How long until it is useful?

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.

What does it cost to run our own model?

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

Book a demo

The IT Second Brain · On-premises

Stop solving the same incident twice.

Your team already solves hard problems every week. Stackbridge turns the tickets and threads they produce into a knowledge base that writes itself, so nothing has to be solved, answered, or learned twice.

Book a demo See how it works

Nothing to write up by hand
A person approves every entry
Runs on your infrastructure

incidents47 messages

maya

checkout is down, customers can't pay 😱 anyone know why?

tom

started right after the 2pm release i think

maya

we hit something like this months ago too?

sam

can't find the old thread anywhere 🙄

tom

rolled back the release, payments working again ✅

… then it's gone

IncidentP1checkout

Checkout went down after a release

What happened: A broken setting in the 2pm release stopped customers from paying.
How it was fixed: Rolled back to the previous release.
How to prevent it: Block a release automatically if checkout can't complete a test payment.

checkoutreleasepaymentsrollback

Approved by tomSearchable forever

Try it on your own thread further down the page.

Running in production today at

A managed IT services provider
A 24/7 platform on-call rotation
An EU engineering team, self-hosted

Connects to the stack your team already lives in

Jira
Slack
Microsoft Teams
Confluence
GitHub
Bitbucket

Every repeated incident is a problem someone on your team already solved. Every resignation walks the answer out the door.

Twice

How many times you pay for the same incident. Once when it happens, and again when it happens to someone who wasn’t there the first time.

One

How many people have to leave for the only real account of how something works to leave with them.

3am

When you find out whether last time’s fix was ever written down, or just typed into a thread and forgotten.

The problem

Your team already solved it. Nobody can find it.

Every IT organisation loses knowledge it has already paid for. It happens in three ways. None of them look like a crisis on the day they happen, which is exactly why they go unfixed for years. Most teams recognise at least two of these immediately.

The same incident, twice

It was fixed at 3am, eight months ago. Whoever fixed it wrote the answer in a thread nobody can find now. So the next outage gets solved from scratch, at the same cost, by someone else.

You pay for the same outage twice

Knowledge that walks out the door

A senior engineer resigns and the undocumented half of your platform leaves with them. Nothing was ever hidden. It just never got written down, and four weeks of notice is not long enough to reconstruct nine years.

Months of ramp-up. One person, single point of failure.

The same question, forever

What is the proxy? Who owns this cluster? How do I get access? Answered in chat again and again by whoever is senior enough to know. It is an interrupt tax, and it is paid by exactly the people you can least afford to interrupt.

Your best engineers, answering the same question

Stackbridge exists to stop all three. It captures what your team works out, structures it while the context is still fresh, and puts it back in front of the next person who needs it. Your knowledge stops depending on who happens to still be in the room.

How it works

Chaos in. Memory out.

Four moving parts. One of them is a person, and none of them is extra work for your team.

Connect your stack
Point Stackbridge at Jira, Slack, Teams, Confluence, GitHub or Bitbucket. Tickets and threads flow in on their own. Your team changes nothing about how they work.
Your LLM structures it
Every messy thread becomes a typed entry: what broke, how it was fixed, how to stop it happening again. Your choice of model: Claude, OpenAI, or one running on your own hardware.
A human approves it
Nothing enters the knowledge base on the AI’s say-so. Everything lands as a draft for review. Approve or reject from the web app or straight from the Slack thread, with every action attributed.
It finds the engineer
Ask in plain English and get the exact past fix, not a link to a thread nobody has time to read. When a repeat incident lands, Stackbridge posts the closest match into the channel before anyone thinks to ask.

Try it

Watch it happen, four ways.

Knowledge gets in from the tools your team already uses, and you can run each path here. Pasting is the fallback, not the plan.

#platform-incidentsLive

priya09:41

anyone seen the payment webhooks stop retrying? queue is backing up

dave09:41

not me. is it prod only?

Stackbridge watches the channels you choose and replies only on a high-confidence match, so it never becomes noise.

acme/platform-docsmain

4 documents found. Nothing extracted yet.

3 entries ready for review1 untouched, so nothing was re-read for nothing

Markdown in the repos your team already maintains becomes searchable knowledge. Run it on demand, or from CI on every merge.

Platform Engineeringspace · synced hourly

4 documents found. Nothing extracted yet.

2 entries ready for review2 untouched, so nothing was re-read for nothing

Every page is version-checked first, so only what actually changed is re-read. A page deleted upstream archives its entry instead of leaving a stale answer behind.

A thread nobody will find againEditable

Paste your own. Nothing you type here leaves this page.⌘ ↵

Nothing captured yet

Press extract

This runs a simplified extractor in your browser, so your text stays with you. The product uses your own LLM and reads far messier input than this. Bring a real incident and we'll run the real one on the call.

In the product

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

Results come from your own incidents and runbooks, and each one opens on the entry itself, with the fix and who approved it, so the person reading it at 3am can check it. Screenshots of the running product, not comps.

Stackbridge search results for “postgres replication lag”: a P2 incident titled PostgreSQL Replica Lag Exceeds 5 Minutes, two runbooks and a how-to about PostgreSQL replication, then less similar incidents, each with its type label and a match score. — Search for “postgres replication lag”: the past incident first, then the runbooks and the how-to for it, each matched by meaning and by the exact words.

The Stackbridge incident entry PostgreSQL Replica Lag Exceeds 5 Minutes: its error signature, root cause, the fix applied, prevention and a timeline, beside a panel showing it was approved, by whom and when. — The incident itself: root cause, fix, prevention and timeline, who approved it, and a prompt to confirm it is still right or report it wrong.

The Stackbridge entries list: twenty entries with tabs by type and filters for pending review and severity, showing how-tos, a runbook, incidents and a reference entry, each with its type label, service and tags. — The entries list: every entry typed and tagged, filterable by type, by severity and by what is still pending review.

Features

Everything your team knows, working for you

Nine things a wiki cannot do, because a wiki does not know an incident happened.

Semantic search

Ask in plain English or paste the raw error. Get back the specific incident that matched, the fix that worked, and who found it. Not a list of documents you still have to dig through.

why is checkout failing after a release3 matches

Proactive matching

New incident lands? Stackbridge posts the closest past fixes into the thread before anyone has to ask.

Structured, not dumped

Every fix lands in a typed schema with severity, service, and error signature. No free-text dumps nobody can find when it actually matters.

Auto runbooks

When the same error keeps coming back, one click turns every past occurrence into a runbook for that service, so the next on-call engineer inherits a playbook instead of a blank page and a pager.

Survives attrition

When a senior engineer leaves, their fixes stay, and so does the reasoning. Decision records capture why it was built this way; service cards capture who owned it and what breaks.

Answers where you already are

Ask in Slack or Teams without opening a tab. Or from inside Claude Code and Cursor. Stackbridge speaks MCP, so your coding agent can search past incidents and file new ones itself.

Knows what it doesn't know

Every unanswered question is logged and clustered. You get a ranked list of the knowledge you're missing, plus an AI-guided interview to get it out of the person leaving on Friday.

Audit & compliance

Versioned, attributed, logged, exportable. The paper trail auditors ask for, without anyone having to produce it.

One knowledge schema · six entry types

Incidents
Runbooks
How-tos
Reference
Decisions
Service cards

Compare

“But Rovo already does this.”

It searches what somebody already wrote down, which is a different job from making sure it got written down at all. Here is the honest difference between the four things a team reaches for when the pager goes off, including the last row none of them can answer.

Who has to write it down?

Stackbridge

Nobody. It reads the ticket and the thread

Partly:
Rovo · Copilot · Glean · Nobody, but it can only surface what someone already wrote
No:
Wiki / Confluence · A person, after the incident, on their own time
Partly:
Slack search · Nobody, but nothing is written either

Is it still true in six months?

Stackbridge

Confluence re-syncs hourly; repos version-diffed on demand

Partly:
Rovo · Copilot · Glean · Indexes what is there, including the page that went stale
No:
Wiki / Confluence · Rots quietly from the day it is published
Partly:
Slack search · Accurate, but scrolled past retention

Can you find a fix from a symptom?

Stackbridge

Semantic search plus exact error-signature match

Partly:
Rovo · Copilot · Glean · Strong search, if somebody documented the fix at all
No:
Wiki / Confluence · Only if you guess the words someone titled it
No:
Slack search · Keyword-only, and the fix is 200 messages down

Does it reach you during the incident?

Stackbridge

Posts the closest past fix into the thread, unasked

No:
Rovo · Copilot · Glean · You have to stop and go ask it
No:
Wiki / Confluence · Sits in a tab nobody opens at 3am
No:
Slack search · You are already in it, and still searching

Can it run with no internet egress?

Stackbridge

On-premises via Helm, local model, no inbound ports

Partly:
Rovo · Copilot · Glean · VPC and regional options exist, but the vendor still operates it
Partly:
Wiki / Confluence · Data Center edition, at Data Center prices
No:
Slack search · SaaS only

Does it tell you what it is missing?

Stackbridge

Ranked gap report from every unanswered question

No:
Rovo · Copilot · Glean · Returns its best guess and stops
No:
Wiki / Confluence · No idea what was never written
No:
Slack search · No

Can you prove who approved a fact, and that it was not altered?

Stackbridge

Named approver, hash-chained audit trail, per-entry read log

No:
Rovo · Copilot · Glean · Indexes everything; nothing is reviewed or approved
No:
Wiki / Confluence · Page history, editable by anyone with edit rights
No:
Slack search · No

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

See it on your own incidents

Model Context Protocol

Your knowledge, inside every AI tool

Connect Claude Code, Claude Desktop, Cursor, or any other MCP client. One command, a browser login, done. No tokens to copy, org-scoped by design.

Pull your team's real incident fixes, cited and attributed, right into your chat
Solved something new? Your agent files it back in one sentence
Self-hosted with Stackbridge, so your knowledge never leaves your network

~/payments-api

$ claude mcp add stackbridge \

https://sb.acme.com/mcp

✓ Authenticated as you@acme.com

you › why did checkout go down after the release?

claude › Your team hit this in March. A broken setting in the release stopped payments. Fix: rolled back the release. Here's the runbook

Security

Your knowledge never leaves your walls

Written for the security review, not around it. This is the section your CISO will read first, so it is the one we made most specific.

Fully on-premises deployable

Run entirely inside your own infrastructure via Helm on your Kubernetes, fully air-gapped if needed. Nothing leaves your network.

No training on your data

Your incidents and fixes are never used to train models. Your knowledge stays yours, and the export button gives all of it back at any time.

Bring your own model. No lock-in.

Claude, OpenAI, or a local model on your own hardware. Chat and search can even run on different machines, so a shared GPU box handles the heavy model while embeddings stay next to the app.

No inbound ports. Not even for Slack.

Slack normally needs a public URL to call into. Stackbridge dials out instead and holds the connection open, so the full Slack integration works with nothing exposed to the internet at all.

The paperwork, already done

A one-click export of your approved knowledge and its complete audit trail, tamper-evident so an auditor can verify nothing was edited after the fact, SSO through Entra ID, Google or GitHub, and role-based access down to read-only.

A human gate on everything

The AI proposes; a person approves. Nothing reaches the knowledge base unreviewed, and every approval carries a name and a timestamp.

For regulated environments

Built to survive your auditor, not to dodge them

Stackbridge is not itself ISO 27001 certified. That certifies an organisation, not a product. And no software can be “NIS2 compliant” on your behalf, because NIS2 obligates you, not your vendor. What we can do is answer the questions your auditor will actually ask, and show the code behind each answer.

So we keep a written control mapping against ISO/IEC 27001 Annex A and NIS2 Article 21(2) that states the gaps as plainly as the coverage, and we send it to anyone evaluating us. If a vendor’s security page has no gaps on it, you are reading marketing.

Role-based access with privileged actions separated and logged
Removing someone revokes their sessions and their AI-tool grants
Integration credentials encrypted at rest, with documented key rotation
Full audit trail of who changed what, exportable on demand
Runs entirely inside your perimeter, air-gapped if you need it
A published vulnerability disclosure route with stated timelines

The cost of forgetting

You have already paid to solve this once.

A repeat incident is a fix your team already found, paid for a second time by people who were never told it existed.

A single repeat P1, conservatively

Engineers pulled onto the call: 6
Hours before anyone finds the old fix: × 5
Fully-loaded hourly cost: × €95
Payroll alone: €2,850

Before the customers who noticed, the release you had to freeze, and the post-mortem that fixed nothing. We quote per deployment, never per seat, and we would rather you judged us on repeats prevented.

Pricing

Quoted per deployment, not per seat.

What you pay depends on the size of your team, how many sources you connect and the support you want. Nothing is priced per user, so the number does not move when the team grows.

On-premises

How Stackbridge is sold

Custom

Deployed on your own Kubernetes via Helm, air-gapped if you need it, running a model you choose. Your knowledge never leaves your network.

Your infrastructure, your model
No inbound ports required
Air-gapped installs supported
Installed with you, not shipped as a download

Book a demo

Hosted

On request

If starting on your own infrastructure is not practical yet, we can talk about hosting it for you in the EU. It is a conversation, not the default.

EU-hosted
Same product, same integrations
A path onto your own infrastructure later
Discussed case by case

Talk to us

Included either way

Every integration
Unlimited users
SSO with Entra ID, Google or GitHub
Audit log and GDPR export
Semantic search, runbooks and MCP
Bring your own LLM

Every engagement starts the same way: a 20-minute call on one of your own incidents, then we connect your first integration with you. No credit card, no self-serve maze, no sales cycle. You will have a number before the second call.

FAQ

The questions buyers actually ask

Stop losing what you already paid for.

Bring one real incident from the past month. In 20 minutes we'll show you what your team would have found in seconds instead of hours. You keep the answer either way, and there is no second call unless you ask for one.

We built Stackbridge because we were the ones being paged. Every install is set up with you, on your infrastructure, by the people who wrote it. That is also why we take on a limited number each quarter.

Already running Stackbridge? Sign in at your own install's address. We never hold your knowledge base.

Stop solving the same incident twice.

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

One knowledge schema · six entry types

“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

You have already paid to solve this once.

Quoted per deployment, not per seat.

On-premises

Hosted

Included either way

The questions buyers actually ask

Why not just use Atlassian Rovo or Microsoft Copilot?

How is this different from Confluence or a wiki?

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

Where does our data live?

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

Who decides what actually enters the knowledge base?

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

How long until it is useful?

What does it cost to run our own model?

Stop losing what you already paid for.