Catch non-determinism before it reaches production.
AI systems produce inconsistent outputs for identical inputs because vector databases rely on non-deterministic floating-point arithmetic that varies across CPUs and hardware.
ReproTest runs automated test suites that execute identical AI pipelines on multiple hardware profiles and flags any output divergence. It integrates into CI/CD and generates pass/fail reports with exact diff locations. Teams get immediate alerts when new model versions or library updates introduce non-determinism.
AI engineers and platform teams building reproducible, auditable, or safety-critical systems in finance, healthcare, SRE, multi-agent operations, and AI alignment research.
First CI-native reproducibility testing platform purpose-built for vector and embedding workloads.
supportive
Executes tests on simulated CPU/GPU combinations
Highlights exact dimensions that differ between runs
GitHub Action and GitLab CI plugins
Stores approved deterministic outputs for regression
Slack and email notifications on divergence
Links test results to specific model commits
Shows overhead introduced by reproducibility fixes
Organization-wide reproducibility score
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| project_id | uuid | No |
| status | text | No |
| created_at | timestamp | No |
Relationships:
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| test_run_id | uuid | No |
| vector_hash | text | No |
| created_at | timestamp | No |
Relationships:
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| test_run_id | uuid | No |
| dimension | int | No |
| expected | text | No |
| actual | text | No |
Relationships:
/api/testTrigger reproducibility test run
/api/reportFetch detailed diff report
1 repo
10 repos
Unlimited
| Month | Users | Conversion | MRR | ARR |
|---|---|---|---|---|
| Month 1 | 55 | 10% | $137 | $1,644 |
| Month 6 | 420 | 16% | $1,680 | $20,160 |
Automated reproducibility testing for vector pipelines that catches drift before production
Offer free setup and onboarding calls to 15 teams posting reproducibility issues on GitHub Discussions in LangChain and LlamaIndex repos. Target SRE and MLOps engineers at fintech startups via targeted LinkedIn outreach.
Experiment tracking
No hardware variance testing
Specialized deterministic diff engine
Proprietary multi-hardware simulation library and growing database of known divergence patterns
CI/CD adoption in AI teams is exploding while regulatory requirements for reproducible models are tightening
Low initial adoption of new CI tool
Provide one-click GitHub Action install
Success: 70% report manual workarounds
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