How it works

Less than you would expect. No new workflows. No process-mapping project. Nothing to instrument by hand. Bloomfilter reads the systems you already run and the event logs they already produce: Jira, GitHub, the CI/CD pipeline. The one active step is a plugin your developers add to their agent: Claude Code, Cursor, Codex. It binds a coding session to a ticket in a click. The build is Forward Deployed. Our engineers stand up the connections and the first process model. Your team reviews it. It does not build it.

A working connection and a first read, not a kickoff deck. Week one is access and instrumentation. Bloomfilter connects to your systems, the plugin goes to a first team of developers, and the event streams start flowing. Most of the elapsed time is your security review and access provisioning, not engineering work, because nothing new gets built. From your side, the room is whoever owns tool access and one engineering lead to point at the first workflow. That is the footprint.

No. The foundation is process intelligence for human work, and it comes first. Bloomfilter reads the SDLC event logs you already generate and builds the process baseline with or without agents in the picture. Where agents are running, their activity overlays that baseline. Where they are not yet, the baseline still stands on its own. Standing it up before agents scale is the point. It is your before-state. Without it, agent impact is a number with nothing to measure against.

Then you are the normal case. Every SDLC toolchain is wired differently, and that is the exact problem Bloomfilter was built for. Delivery tools do not produce clean event logs on their own. Turning raw tool output into process-aware structure takes an organization-specific transformation. That transformation is the product. If a tool emits an event log or exposes an API, it can be a source. Where the links between systems are missing, Bloomfilter reconstructs them from what is there: ticket keys, branch names, PR numbers, deploy IDs. Homegrown tools included.

By handing them something they can read. The plugin is open source. Your security team can review every line that touches a developer's machine and fork it if they want full control. Deploy in your own data cloud or private environment and your data stays in your account. SaaS is available when that fits. The plugin writes its logs to the developer's machine or to an environment you control, and strips sensitive content there. Bloomfilter is SOC 2 Type II audited, and reports are available to customers under NDA. Capture is configurable. Prompts can be excluded entirely, and usually are. What Bloomfilter needs is the event stream and the ticket binding, not the contents of the conversation or your code. The core transformation runs on deterministic rules, not a model, so every number is auditable.

No. Self-sufficiency is the design goal, not an upsell. Process expectations live in a configuration file, closer to structured settings than to code. A senior engineer can author one in about twenty minutes. The process model has a drag-and-drop editor. Routine views come as saved filters. The config is text, so it version-controls alongside everything else. Your center of excellence can train its own authors. Forward Deployed at the start, run by your team after. If changing a process expectation meant filing a ticket with us every time, we would have built the wrong product.