Reduce pre-release risk
Catch performance regressions under blur, noise, occlusion, compression, and lighting shifts before they reach customers. Every release gets a reliability scorecard.
Open prototype — available on PyPI
Reliability testing for Computer Vision, with proof.
VisionOps stress-tests Computer Vision pipelines against realistic corruptions and delivers
evidence-grounded root-cause analysis tied to your actual codebase.
Ship with confidence.
$ pip install visionops
For teams shipping CV in production
Why VisionOps
Cut debug time with repo-aware RCA
Before generating hypotheses, VisionOps builds a structural map of the target codebase—entry points, pipeline stages, data flow, and high-signal files—then combines it with runtime evidence to pinpoint the likely failure stage and suspect files automatically.
Audit-ready evidence
Every run produces structured reports (JSON, Markdown, HTML) with metric deltas, execution diagnostics, and ranked fix suggestions—ready for compliance, post-mortems, or stakeholder review.
7+
corruption families tested per run
3
severity levels per corruption
9+
automated pipeline stages end-to-end
The gap in CV deployment today
Most pipelines look great on clean validation sets, then silently degrade in real-world conditions. Teams discover failures after deployment, not before.
- No systematic corruption testing in CI/CD
- Regressions visible in metrics, but root causes are not
- Debug loops are manual and too slow for release velocity
What VisionOps replaces
- Ad-hoc augmentation scripts with no coverage guarantees
- Manual "run and diff" regression workflows
- Siloed debugging across data, model, and infra teams
- Post-incident analysis that starts from zero context
How VisionOps works
Point VisionOps at any CV repo and get a full reliability report:
01
Sandbox & baseline
Copies the workspace, runs install, baseline inference, and metrics in an isolated sandbox. Captures return codes, stderr, and execution timing.
02
Corruption simulation
Generates blur, noise, occlusion, compression, crop truncation, perspective warp, and lighting variants at multiple severities. Re-runs the full pipeline on each.
03
Regression & stage localization
Compares baseline vs suite metrics, detects regressions, and uses heuristic analysis to localize the likely failing stage in the pipeline.
04
Repo mapping
Scans the repository to build a structural map of pipeline files, entry points, and data flow—giving the RCA engine real code context.
05
Evidence-grounded RCA
Combines runtime signals (metric deltas, logs, return codes) with the repo map to produce ranked hypotheses tied to specific files and stages.
06
Fix suggestions & reports
Generates concrete fix recommendations and writes
summary.json, report.md, and report.html.
Advanced AI agents for RCA, grounded in evidence
Use VisionOps with any OpenAI-compatible API. If no API key is provided, the agents will fall back to deterministic logic.
RepoMapperAgent
Scans the repo and identifies likely pipeline files, stages, and high-signal modules. Produces a structural map used by downstream agents.
RCAAgent
Combines runtime evidence (metrics, logs, execution outcomes) with the repo map into ranked root-cause hypotheses with confidence scores.
FixAgent
Converts rule-based remediations into concrete, prioritized fix suggestions engineers can act on in the same sprint.
Every hypothesis is grounded in metric deltas, per-suite regressions, logs, return codes, stage localization heuristics, and repo-aware file mapping.
What you get
Reliability scorecards
Primary metric deltas and top regressions by suite, exportable as JSON and HTML.
Execution diagnostics
Install failures, command errors, and suite anomalies surfaced with full context including return codes and stderr.
Autoconfig
Point VisionOps at a repo and it infers install, run, and metrics commands, dataset paths, task type, and suite defaults—with confidence metadata.
Local or cloud
Local mode is fully functional today. The AWS CodeBuild path is scaffolded for teams that need secure, ephemeral sandboxing.
- Local:
visionops local-run— runs everything on your machine - AWS:
visionops aws-run— orchestrates CodeBuild, polls status, returns artifacts - Private repos: GitHub App auth for authenticated clones without PATs
Environment health check
visionops doctor validates config, checks API keys, AWS credentials,
and runs autoconfig sanity checks before you commit to a full run.
Coming soon
VisionOps for Enterprise
We are building a production-grade platform with SSO, team dashboards, CI/CD
integrations, SLA-backed uptime, and dedicated support.
Join the waitlist to get early access and shape the roadmap.
- Multi-repo dashboards with historical trend tracking
- GitHub Actions & GitLab CI native integration
- Role-based access and SSO (SAML / OIDC)
- Dedicated cloud sandbox environments
- Custom corruption suites and SLA reporting
- Priority support and onboarding
No spam. We will only reach out when the enterprise version is ready.
Try the working prototype today.
VisionOps is available on PyPI. Install it, point it at your CV repo, and get a reliability report in minutes.
$ pip install visionops