What you can do with CDviz

Leadership wants DORA numbers, but today they are stitched together by hand from several dashboards every sprint. Get the four indicators (deployment frequency, lead time for changes, change failure rate and time to restore) as a live dashboard instead of a hand-maintained spreadsheet.

Without a shared timeline of changes across tools, mean-time-to-restore is spent reconstructing context from Slack threads and git logs. Instantly see what changed, where, and when the moment an incident hits.

Audit and compliance reviews ask who deployed what, when, and to which environment. Answering means digging across CI logs, ticketing systems and chat history. Keep a tamper-evident, queryable history for compliance and post-mortems instead.

Each tool has its own webhook payload shape, so one coherent view means writing and maintaining glue code for every integration. Normalize events from GitHub, GitLab, Jenkins, ArgoCD and more into one consistent schema so downstream dashboards and automation do not care which tool emitted them.

CI says it deployed, but what is actually running? Drift between pipeline intent and cluster reality hides failed rollouts and silent rollbacks. Emit a CDEvent whenever a workload is deployed, upgraded or rolled back, reflecting real state not intent.

You want a Slack ping on production deploys or a smoke-test job when a service upgrades, but wiring that per-tool is repetitive and brittle. React to delivery events (notify, gate, or kick off a follow-up workflow) without polling or bespoke webhook plumbing.

Delivery data is scattered and ephemeral across tool UIs and logs, so simple historical questions are surprisingly hard. Run ad-hoc SQL over your full delivery history, for example “how many times did service X deploy to prod last month?”. JSONB gives access to the CDEvents payload.

Naive event tables get slow and expensive as history grows, pushing teams to throw away the very data needed for trends. Keep months of delivery events for trend analysis while queries stay fast and storage stays bounded.

Building DORA and deployment dashboards by hand means learning the data model, writing SQL and laying out panels before you see a single number. Stand up delivery dashboards in minutes instead.

Aggregate metrics hide the detail engineers need: which version, which environment, which deploy is behind a spike or a regression. See deployment frequency and history per service and environment, then drill down to the individual events behind a number.

Your CI runs on a public provider, but the events you care about must land in an internal cluster you do not want to expose to the internet just to receive webhooks. Bridge the two without opening an inbound hole in your private network.

Your IDP shows services and ownership, but not what version of each artifact was published, signed, and deployed, and when. Developers still chase that across registries, CI and chat.

You want an AI agent to answer questions about your SDLC (what shipped, when, how often, what failed) without handing it credentials to every CI, registry and cluster, fanning out calls across all of them, or losing access to historical data the tools age out.