Reduce AI library integration complexity, minimise runtime failures, and ship AI products faster with zero breaking changes.
Get early accessBuilt for stacks running
PyTorch 2.2 drops Python 3.8 support. LangChain rewrites its API. You find out in production — at 2 am, when your model stops loading.
The average ML team loses 14+ hours per incident tracing which transitive dependency caused the model to stop loading. That's sprint velocity, gone.
Dependabot and Renovate treat torch like it's jQuery. They have no concept of CUDA compatibility, model weights, or framework API contracts.
Add the GitHub Action or run the CLI. DepShield scans your
requirements.txt,
pyproject.toml,
and lock files instantly.
Our model understands AI-specific constraints: CUDA versions, model API contracts, framework compatibility matrices, and breaking change patterns — not just SemVer.
Receive prioritised fixes, safe upgrade paths, and compatibility scores — not just a list of CVEs. One-click PRs. No more staring at changelogs.
Flags libraries with upcoming breaking changes 2–4 weeks before release so you can plan upgrades, not react to them.
Visual map of your entire AI library graph, with compatibility risk highlighted at every edge. Spot conflicts before they hit CI.
One-click pull requests with tested, safe upgrade paths — including pinned transitive dependencies. Mergeable on day one.
Tracks GPU driver and CUDA version constraints across PyTorch, TensorFlow, JAX, and triton. No more silent GPU fallbacks.
Instant notifications when a new library version drops — with a compatibility verdict before you even think about upgrading.
Every recommendation comes with a confidence score based on community adoption, test coverage, and historical breakage rates.
Join the waitlist. Get early access, lock in founder pricing, and help shape the product.