Feature-level code observability

See what every PR breaks — before your users do.

Dynvo maps every feature and user flow from git history — no SDK, no instrumentation — scores them by health, risk and coverage, then overlays live Sentry errors and PostHog usage. One living map, read by your team and your AI agents.

Scan your repoSee what you get
drag the map · scroll to explore ↓
The problems we solve

The questions your tools
can’t answer.

Five conversations happening in every engineering org right now — each answered with an instrument we validated in public. The numbers in the chips are ours: measured, not promised.

Agents write half our code — does anyone know what’s happening in there?
Per feature: how much is AI-authored, whether it sticks or gets rewritten within a month, where it introduces defects — and whether a human actually reviewed any of it.
defect tracing · 92% precision
This PR looks fine. What does it actually touch?
Every pull request annotated with the features and user journeys it reaches, their validated fix-risk and blast radius — plus a warning when you’re editing code whose author left.
risk predictor · AUC 0.78
I just inherited this codebase. What am I standing on?
A 48-hour audit: the feature map, bus factor and orphaned code, licenses and CVEs joined to the features that import them, and a costed remediation plan — a PDF you can hand to a board.
SBOM · 455/455 vs npm audit
Tests are green. Why don’t I feel safe?
Coverage in product language: which user journeys are exercised end-to-end, which only by integration tests — and which are bare.
journey coverage · 0.878 verified
We bought insight tools before. They went stale in a quarter.
Feature names frozen across rescans, trends on stable identity lines, and a weekly digest that reports movement — and stays silent when there is none.
identity pinning · 0 renames
Every instrument ships with a pre-registered validation gate — methodology public, engine source-available.See a real sample audit →
What you actually get

No new dashboard to babysit.

The map lands where your team already works: a risk comment on every pull request, a weekly digest in Slack. The dashboard is there when you want to go deeper.

In your pull requests

Every PR is annotated with the features and flows it touches, their health and coverage — and the live production signal on exactly that code.

In your Slack

Monday morning, the whole team sees the same picture: what got riskier, what's untested where users actually are, and where the fires keep starting.

Under the hood

Where those comments come from.

Every risk line above is read off a map — one scan turns your code into a skill-map of your product. Four layers; each diagram on the right is a live 3D model of that layer. See it on a real scan →

01The code

Your code & infrastructure

Services, gateways, queues, datastores — the real architecture in your repository, exactly as it’s wired together. This is the ground truth we read.

code · architecture
02The city

What your users actually get

The product as people use it — features become districts, and the flows users travel between them become the roads of a living city.

product · city
03Dynvo

One scan maps code to product

The membrane between the code and the city. A scan line sweeps the whole repository and reconstructs the map — this is our integration, doing its work.

dynvo · scan
04The scan result

A skill-map of your product

Product features and user flows, each scored. Feature names surface over the city; red hotspots flag what needs attention or a refactor; little carts are live PostHog traffic mapped onto features — so you see where the activity, and the risk, really is.

Product featuresUser flowsHotspotsCoverageHealthPostHog traffic
With integrations
Sentry error→the feature & flow it breaks
PostHog event→the feature & flow it touches

Mapped through the scan live at that commit — runtime lands on the map, not in a separate dashboard.

Explore a real skill-map →

scan · skill-map
The product

One scan from git history. A map for your team and precise context for your AI agent.

We read your repository’s history — not a README, not an SDK — and reconstruct the features, the flows inside them, and the files each one touches. Then we score every feature and attach your runtime signal.

The city

Product, on top

Each district is a product feature; the tower is the feature, the buildings are its developer features, the roads are the flows users travel.

Dynvo

The scan, in the middle

The glowing membrane is the product — the scan that maps the code below into the city above. Its cracks are where risk concentrates.

The code

Real system, underneath

Services, gateways, queues and datastores — the actual architecture. Alignment beams prove every feature is grounded in real code.

Score

Health · risk · churn · coverage

Bug-fix ratio, hotspots, ownership and test coverage — per feature and per flow, so risk stops being invisible.

Overlay

Sentry errors · PostHog usage

Live runtime signal mapped onto the map — what’s breaking, what nobody touches — no SDK, no instrumentation.

Feed

Context for your AI agent

The same map an engineer reads becomes precise, structured context an agent can query over MCP before it edits a line.

Security

Your code stays yours.

Dynvo reads git history to build the map — it doesn’t need to keep your source code at rest. Encrypted in transit, least-privilege access, and a clear data-flow you can audit.

Read the security page
  • No source code stored at rest
  • Encrypted in transit · KMS-backed secrets
  • Read-only git access, least privilege
  • Source-available engine (FSL) — audit every line
Get started

Point Dynvo at a repo. Get the map back in minutes.

Scan your repoRead the docs