Use case · EngineeringAn AI that knows why every line of code exists.
Your developers have Claude Code or Cursor, and a repository. What those tools lack is everything else: the ticket, the customer who asked, the architecture decision, last year's incident, the meeting where it was decided. We link all of it in a company memory exposed via MCP, and we work with your team to build the agents that assemble incident context, keep documentation current and get a new developer up to speed in a day.
Five questions your team asks every week.
The code says what the system does. Why it does it, what has broken before and what was decided are spread across tickets, Slack, postmortems and a few people. Here is what changes with a memory that links them.
| Today | With the company memory | |
|---|---|---|
| What is this feature linked to? | A commit, a ticket with no context, an outdated Notion page, with no link between them. | The ticket, the customer who asked for it, the meeting where it was decided and the pull request, linked. |
| Is this bug already known, and how did we fix it last time? | Similar tickets with no link between them, the fix in a pull request nobody can find. | Comparable incidents, their root cause, the fix and the pull request, linked to the affected service. |
| Why this architecture, and who decided on it? | A decision made in a Slack thread or a meeting, known to the two people who made it, one of whom has left. | The decision, the alternatives ruled out, the reasoning and the date, retrieved with their source. |
| What is the current documentation for this service? | An outdated README, three Notion pages, and the real answer in a Slack thread. | The authoritative version, linked to the code and recent decisions, with what has changed since it was written. |
| What should a developer starting Monday on this service read? | Two weeks reading outdated documents and asking questions on Slack. | The service onboarding guide: architecture, decisions, recent incidents, people to know, sourced. |
The engineering team's tools.
We replace nothing. The tools your team already uses feed the memory continuously, within each repository's access rights, and your AI tools query it via MCP.
GitHub- GitLab
- Bitbucket
Linear
Jira
Notion
Confluence- Notion
Google Drive
- Datadog
- Sentry
- PagerDuty
- Grafana
Slack
Microsoft Teams
Google Meet
Claude Code- Cursor
Copilot
Is one of your tools missing from this list? We connect it during deployment.
What changes for the engineering team.
AI coding tools that know the company, not just the repository.
Claude Code, Cursor and your agents start with the full context via MCP: the ticket, the customer, the decision, the past incident. The code they produce meets the real need, not just the prompt.
Incidents resolved with history, not from memory.
When an incident opens, comparable cases, the previous fix, the latest deployments and the architecture decision arrive before the on-call engineer has to look for them. The memory updates itself: no more postmortem to dig out of a folder.
Documentation that no longer goes stale in silence.
Every page keeps its date and source. When the code or a decision changes, the affected page is flagged, and release notes are written from what was actually shipped.
Your engineering processes, as they actually happen.
Before building anything, our interview agents talk to every member of the team and map the real process, not the one in the wiki: the steps, the tools, who decides, and where it gets stuck.
The incident, from alert to postmortem.
Who gets paged, where people look for the history, how the fix is validated and deployed, what gets written afterwards, and what does not. Including the steps nobody documented.
The technical decision, from Slack thread to production.
How an architecture question comes up, who decides, where the decision is written down (or not), how it reaches the people who were not there, and what is left of it six months later.
The friction points.
The bug fixed twice by two people, the decision made in Slack and lost, the documentation nobody reads anymore because it is wrong, the new developer asking the same questions as the previous one: that is where the first agents get built.
The agents we build with your team.
No off-the-shelf agents. Each one is designed with your engineering team, for a specific need, on top of a memory where all the data is already linked. Four examples among many others.
Incident context
Comparable incidents and their fixes, the latest deployments on the service, the architecture decision that explains it, the current runbook and the people who know the topic, gathered in seconds in the incident channel.
Who stays in control. The on-call engineer decides. The agent does not touch production.
Review context
The ticket and the original customer request, the architecture decisions involved, past incidents on the same module and the team's conventions, attached to the pull request so the reviewer judges substance, not just style.
Who stays in control. The reviewer approves. The agent merges nothing.
Release notes and documentation
Release notes written from pull requests, tickets and decisions, in the right tone for each audience (customers, support, sales), and the affected documentation pages flagged or updated.
Who stays in control. The team reviews before publishing.
Service onboarding
One guide per service: the architecture and the reasoning behind it, recent decisions, the last six months of incidents, the fragile parts, the people to know, and the questions previous new hires asked. Built from the memory, current on day one.
Who stays in control. The engineering lead reviews before handing it over.
These four agents are only a sample. Once the team's data is linked, an agent can be built for every task that deserves one: security questionnaires and the technical sections of RFPs, tech debt and dependency reviews, tracking postmortem action items, answers for support about how the system behaves. Tell us what takes up your team's time, and we will build the agent for it.
The questions each role asks.
Each role puts these questions to the memory through MCP, from Claude Code, Cursor or the AI the team already uses, or to your agents. Every answer cites its sources.
Software Engineer
Finds why a piece of code exists and a service's history without interrupting the team.
- What is this feature linked to: ticket, customer, meeting, pull request?
- How did we fix this type of incident last time?
- What is the architecture decision behind this choice, and who made it?
- Which documentation is still valid for this service?
- Who knows this module, and where do things stand on it?
On-Call Engineer
Shows up to an incident with the history, not a blank page.
- Have we had this alert before, and what caused it?
- What has been deployed on this service since yesterday?
- What is the current runbook for this service, and who last changed it?
- Which services depend on this one?
- Who do I need to notify, and what did we tell customers last time?
Engineering Lead
Sees tech debt, incidents and pending decisions without waiting for the weekly sync.
- Which incidents this quarter share the same root cause?
- Which architecture decisions are still waiting to be made?
- Which action items from last month's postmortem are still open?
- Which promises made to customers have an unplanned technical impact?
- What should a developer starting Monday on this service read?
Start with engineering. The same memory then serves the others.
The Company Brain is not a development tool: it is the company's memory. The code, decisions and incidents linked for the engineering team also serve the product team that prioritizes, support answering questions about how the system behaves, and sales filling out a security questionnaire. Start with one area, measure the result, then decide whether to expand.
Frequently asked questions
The questions engineering teams ask before getting started. If yours is not here, ask us directly.
They keep using them. The memory is exposed via MCP: their tools query it directly, within their access rights, and start with the company's context (the ticket, the customer, the decision, the past incident) instead of only the open repository.
Not without your consent. You choose the models, including open-source models hosted in your own infrastructure. The memory links the code to everything else without copying it anywhere, and every query respects the repository's access rights.
You do not have to. The difference is maintenance: our ingestion agents maintain the connections, the access rights per person and per agent, and the provenance of every fact, without taking up your team's time every week. If your tool meets the need, we connect to it.
No, it is the most common situation. The memory starts from what is true today (the code, the tickets, recent decisions), and our interview agents reconstruct the rest with the team. Documentation becomes an outcome again, not a prerequisite.
Most often one service, with incident context and onboarding for new hires: two cases where the benefit is measurable from the first incident and the first new hire. We measure, then we expand.
Let's talk about your engineering team.
Thirty minutes: your tools, your services, and the first agent that would give your team time back.