Erik Severinghaus
Co-CEO
Bloomfilter
Bloomfilter offers an observability and governance platform for AI agents within the software development lifecycle. Its Agent Miner app automatically captures evidence of agent actions, creates and updates traceability links, and flags ambiguous requirements, providing real‑time insights, audit readiness, and risk scoring across integrated tools like Jira, GitHub, and Azure DevOps.
- Artificial Intelligence
- AI Agents
- Data & Analytics
- Developer Tools
- Enterprise Software
- Software Only
Updated 2 days ago
Funding
Raised to date
$7.1MRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Funding rounds are not available yet.
Founders
Product
Problem
Enterprises deploying AI agents lack visibility into how those agents interact with software development processes, leading to undocumented work, compliance gaps, and inefficiencies such as rework, bottlenecks, and process drift.
Solution
Bloomfilter provides an observability and governance platform for AI agents operating within the software development lifecycle (SDLC). The Agent Miner app continuously captures evidence of agent actions, automatically creates and updates traceability links, and flags unclear requirements or risky artifacts. By unifying data from tools like Jira, GitHub, Azure DevOps, and design systems, the platform surfaces process drift, delays, and bottlenecks across teams. Built‑in audit readiness and risk scoring enable compliance teams to maintain continuous readiness for standards such as SOC 2. The solution delivers real‑time insights to both human and AI participants, allowing hybrid workflows to operate more efficiently and safely.
Target Audience
Primary customers are technical leaders responsible for AI governance—such as AI Ops, platform engineering, and compliance teams—and engineering managers who need to reduce waste and ensure traceability in complex SDLC environments.
Features
- Automatic evidence capture and continuous audit‑readiness reporting for AI‑driven work
- Real‑time detection of ambiguous requirements with auto‑generated traceability links
- Process drift, delay, and bottleneck identification across integrated toolchains (Jira, GitHub, Azure DevOps, Figma, etc.)
- Pre‑screening of artifacts to flag issues before human review, reducing rework
- Unified data layer that aggregates workflow histories, code changes, design iterations, and CI/CD events
- Risk scoring and compliance monitoring with configurable guardrails for high‑stakes deployments
- Dashboard and API access for engineering, PMO, and AI‑Ops teams to monitor agent performance