Know what your code change can break, before it reaches production.
Connect your repositories, databases, and infrastructure. PrashFlow Impact discovers how they're actually related, and when a PR lands, it predicts what can break, runs the PR's own real test suite, verifies each prediction against your real connected infrastructure, and gates CI/CD on that evidence, not an AI's opinion alone.
A CI/CD gate that means it, because it checks its own work
Most impact predictions stop at a confidence percentage. PrashFlow Impact goes further: it can actually run the PR's own tests and check its predictions against your real infrastructure, so a finding that says REPRODUCED means something ran and confirmed it, not that a model sounded sure.
See the actual blast radius
A real System Graph, built from your connected repositories, databases, and infrastructure, not a guess. A PR's impact is walked outward from the code it actually touches.
Predictions that get checked
Real Execution actually clones and runs a PR's own test suite. Real finding verification runs a live database EXPLAIN or a real JVM measurement. A prediction either gets confirmed or disproven, never left as a permanent guess.
AI does the analysis, evidence decides the gate
Your configured AI Gateway provider can identify findings directly from a PR's real diff and the real System Graph. The CI/CD gate itself stays a deterministic policy engine, so the AI never quietly becomes the thing deciding pass or fail.
An agent that can't go rogue
The AI agent's tool registry is closed and read-oriented by design, there is no LLM-invoked execute_command or execute_sql anywhere in the codebase, enforced by a startup assertion, not a setting an admin can toggle on.
A CI/CD pipeline can actually call this
A CLI command returns a real exit code your pipeline can gate on directly, run the deterministic engine, or hand the analysis to your configured AI provider instead, same report format either way.
Own your infrastructure, own your data
Self-hosted with Docker Compose, backed by Postgres you control, the same architecture as every other PrashFlow product. Connection credentials and AI API keys are encrypted at rest and never sent to an LLM.
Every team ships PRs blind to what's downstream
None of these are exotic edge cases. They happen on every team past a handful of services.
You don't know what a PR actually reaches
A one-line change to a shared library can ripple through a dozen services. Without a real dependency graph, nobody finds out until something downstream breaks in production.
A CI gate that runs on gut feeling
Manual review catches what a reviewer happens to notice. A finding that was never actually checked against real infrastructure is just a guess with a confidence percentage attached to it.
An AI tool that can't check its own homework
An LLM will confidently describe a risk that isn't real, or miss one that is, with no way to tell which is which. Nothing changes that unless the prediction actually gets tested against something real.
Predict, test, prove, decide
Every PR goes through the same four real stages.
Connect
Link your repositories, databases, and infrastructure. Discovery builds the real System Graph from what it actually finds.
Predict
A PR lands. The deterministic engine, or your configured AI provider, walks the blast radius and produces findings grounded in the real diff.
Prove
Real Execution runs the PR's own real tests. Real finding verification checks a database plan or a live memory measurement. Predictions get confirmed or disproven.
Decide
A deterministic policy engine turns the current findings into Pass, Warn, or Block, with a written-reason override path for when a human needs to say otherwise.
What's actually built today
No roadmap items, no "coming soon." This is what runs in the app and API right now.
System Graph and Blast Radius
Real discovery scans your connected repositories for API routes and dependency references and proposes them for review. The graph shows exactly which services, APIs, and databases a PR's change actually reaches, with one click to see everything affected at once.
Impact score, from real findings
Every factor in the score only counts if a real finding was actually generated from the diff, never from raw graph connectivity. A safe refactor scores low even in a densely-connected system.
Real Execution
Clones the PR's actual branch and runs its own real test command, against a connected SSH server, Kubernetes cluster, or Docker/Podman host, or a local sandbox. Real exit code, real logs, never simulated.
Real finding verification
Database findings get a real, read-only EXPLAIN against a connected Postgres or MySQL database. Memory and thread findings get checked against a real live JVM measurement over SSH, or a real sandbox's peak memory.
AI PR Analysis
Alongside the deterministic engine, your configured AI Gateway provider can identify findings directly from the PR's real diff patches and the real System Graph, using real provider-native tool-calling, validated against the same finding schema, then optionally auto-verify what it finds against real infrastructure. A button on the PR page, or a CLI command for CI/CD.
CI/CD policy gate
Pass, Warn, or Block, computed deterministically from the current findings, never decided by the AI itself. A written-reason override flips a Block to a logged Warn, never a silent bypass.
Multi-provider AI Gateway
OpenAI, Azure OpenAI, Anthropic, Ollama, vLLM, or any OpenAI-compatible endpoint, each with its own real tool-calling wire protocol, budget and rate limiting, and usage tracking.
Live connection health
A background worker re-probes every reachable connection every few minutes. A connection only ever shows Connected when it was actually, recently reached, never a saved-and-forgotten status.
The AI does the analysis. Evidence still decides.
Your configured AI Gateway provider reads the PR's real diff patches and the real System Graph reach, then reports findings through a strict tool call, not free-form text. Anything that doesn't match the finding schema gets dropped, not guessed at. No provider configured, or no real diff on record, and it says so honestly instead of returning something fabricated.
- Grounded in the PR's real diff text and the real graph, never invents a file or service that wasn't there
- Optional auto-verify runs every applicable finding through the same real EXPLAIN/JVM checks a manual Verify click uses
- Available from the PR page, or the CLI, same report and exit codes your pipeline already expects
Not a simulated test run. An actual one.
Real Execution clones the PR's own branch and runs its real install and test command in a resource-limited, auto-removed sandbox, or against a connected SSH server, Kubernetes cluster, or Docker/Podman host if one's reachable and permitted. Every real, connected database gets exposed to the run through the environment variables its own driver already reads, scoped to exactly the database connections it was actually given, nothing else on the host.
- Real exit code, real logs, real detected sub-projects, monorepo-aware
- Works with Postgres, MySQL, MongoDB, and Redis connections out of the box
- Nothing real reachable, and it says so, never pretends to have run
The AI can look. It was never given a way to touch anything.
The agent's tool registry is closed and read-oriented. There is no LLM-invoked execute_command or execute_sql anywhere in this codebase, enforced by a startup assertion, not an admin toggle.
Your PR
real diff, real repository, stays yoursImpact Engine
graph reach · findings · scoreYour CI/CD gate
Pass · Warn · Block, decided by policy codeYour infrastructure. Your data. Your control.
We deploy the full stack, Postgres, the Express API, and the Next.js web app, directly on infrastructure you own, along with a CLI your CI/CD pipeline calls to gate on real evidence. Nothing about your code, your diffs, or your infrastructure ever leaves your environment.
Questions, answered
See what your next PR actually reaches.
Self-hosted, evidence-based, and ready to gate your pipeline today.