What does it take to implement?
Every SDLC is configured differently.
The layer has to match the process as it really runs, not a template. That work is engineering. So engineers do it.
Bloomfilter Forward Deployed is the implementation model. Engineers embed with the team, instrument the real toolchain, and stand up the first measured baseline. The engagement ends in self-sufficiency, not dependency.
Implementation has five stages.None of them change how teams work.
Connect
Source systems connect read-only. Jira, GitHub, Azure DevOps, CI. Historical event logs ingest first, so the map opens with historical data instead of a blank slate. Nothing new to install. Nothing to track by hand.
Configure
Encode the organization's definition of the process. The stages, the gates, the paths each team really uses, including the steps that only exist in practice. The process model is a configuration file. Version-controlled, readable, and owned by the team that inherits it.
Baseline
The first measured baseline. Cycle time, throughput, rework, adherence. By stage and by team. Every number that follows is measured against it. This is the point where the ROI question becomes answerable.
Attribute
Agent telemetry comes online. Every session captured at the source and bound to the work item it touched. The plugin is open source, so security reads every line before it reaches a single machine. Raw prompts can be excluded from the data model.
Transition
The team is trained to operate and extend the layer. Process changes can be configured in minutes, not hours, and self-served by your team.
Questions worth asking first.
Less than you'd 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 doesn't 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's 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 aren't yet, the baseline still stands on its own.
Standing it up before agents scale is the point. It's your before-state. Without it, agent impact is a number with nothing to measure against.
Then you're the normal case. Every SDLC toolchain is wired differently, and that's the exact problem Bloomfilter was built for.
Clean event logs don't fall out of delivery tools the way they do out of a system like SAP. 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's 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.
Your data stays inside the boundary you've already approved. Logs are written to the developer's local machine or to an environment you control. Nothing leaves it.
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'd have built the wrong product.
The baseline is step one, not the finish line.
With it in place, measurement moves from activity to outcome: cycle time, rework, throughput, read against the before-state. Then the loop closes. Patterns in what works feed back into how agents operate, and the intelligence compounds, because it's specific to your history and doesn't reset.
Forward Deployed doesn't pack up when the baseline lands. Processes evolve. Teams reorganize. New agent platforms arrive. The engagement keeps pace: new workflows, new toolchains, the transition to agentic operations.
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