Ship sooner with insights on every build.

Buildnote fills the visibility gap before production. It plugs into the CI you already run and turns raw build data into answers, such as which pipelines drag, which tests flake, and exactly what a red build is costing your team. Insight on every build, from the first one.

company/checkout · main · last 30 days

p95 build duration

11m 40s

↓ 32% this month

Failure rate

3.8%

↓ from 9.1%

Flaky tests found

3 fixed this week

25K

Events a day on Free

Events a day on Pro

400

Days of retention on Pro

40+

Integrations and sources

// What Buildnote does

Pipeline performance, watched for you

Execution time and failure rates tracked across every CI environment and branch. When a pipeline degrades, Buildnote ties it to the commit, the error message, and the logs, so the fix starts where the problem did.

Flaky tests, named and shamed

Buildnote ranks your slowest, flakiest, and most failure-prone tests across builds and branches. Stop re-running red pipelines on faith. Know exactly which test to fix and keep merging.

Every build, on a timeline

Drill into any build and see pipelines, jobs, and steps laid end to end, with durations, recorded commands, success rates, and what ran in parallel with what. Triage in minutes, not meetings.

AI Insights on every failure

When a build or test fails, AI Insights explains it as it arrives, covering what failed, the likely reason, and whether it's an infra blip, a real regression, or an unstable test. It also answers plain-English questions with live charts and dashboards.

Query everything with BNQL

BNQL is a read-only query language over every event Buildnote collects, such as builds, tests, commits, and deployments. Develop a query in the editor, then feed a dashboard widget, or script it through the Data API.

Dashboards on a live board

Charts, tables, metrics, gauges, and markdown on a live board, each widget fed by its own query. Auto-refresh keeps a wall display current; import/export moves boards between teams as JSON.

// Integrations

Works with your stack

Buildnote meets the tools you already run. GitHub and GitLab integrations record pipelines, stages, and commits as they happen, and a ready-made GitHub Action wraps the CLI. The CLI and Gradle plugin collect test results in every major format, such as JUnit, xUnit, NUnit, TRX, TestNG, Cucumber, Robot Framework, CTRF, and Dart. Failure notifications land in Slack or Discord, AI Insights runs on Anthropic, OpenAI, or Gemini, and Enterprise teams sign in with SAML SSO.

GitHub

Gradle

Slack

JUnit

Anthropic

QT TestNUnit

GitLab

Cucumber

OpenAITestNG

Discord

DartMSTest

Gemini

FlutterSAML SSO

Robot FrameworkxUnitCTRF

Claude

Claude Code

Cursor

ChatGPT

GitHub Copilot

Windsurf

Zed

Gemini CLI

JetBrains AI

Perplexity

// Build timeline

Find the bottleneck in one look

The timeline lays every job end to end. Here, e2e tests hold the whole pipeline hostage for nine minutes. Buildnote flags it, links the run history, and shows whether it's growing.

checkout0:14

restore cache0:31

install deps1:05

compile2:36

unit tests2:12

e2e tests9:12

deploy0:48

build #4128 · main · 14m 02s total

Flake report · last 14 days

2,381 builds analysed

checkout_flow.spec.ts

18%

payment_retry.spec.ts

11%

auth_token_refresh.spec.ts

inventory_sync.spec.ts

email_webhook.spec.ts

// Flake report

Stop running on faith

Every re-run of a flaky pipeline burns compute and trust. Buildnote traces failures across thousands of builds, ranks the repeat offenders, and with AI Insights tells genuine failures from flaky behaviour so the ticket writes itself.

Why was the last main-branch build so slow?

Build #4128 ran 14m 02s, 6m over the 30-day median. The e2e stage grew 41% after commit 9f3c2ae added 34 scenarios. Duration by stage:

6 steps · 12.4s · BNQL attached · copy to editor · save as dashboard

// AI Insights

Ask your pipeline a question

AI Insights is a chat over your build data: ask in plain English and it writes and validates the query, runs it, and answers with live charts, whole dashboards, collected files, or a build's waterfall. Every reply shows its steps, its cost, and the BNQL it used. Failure summaries triage red builds as they arrive. Bring your own model: Anthropic, Gemini, or OpenAI.

// MCP server

Bring your own assistant

A hosted MCP server connects AI assistants such as Claude, Claude Code and Cursor to your build data mid-conversation. Add one URL, approve access with a browser sign-in, and your assistant can list your teams, write and validate BNQL, run queries, and read files collected during builds. There are no API keys to paste. Every call runs as you, limited to the teams you belong to, with the same role and plan rules the app applies.

// BNQL + dashboards

When you'd rather write the query

BNQL is the read-only query language over every event Buildnote collects, such as builds, tests, commits, and deployments. Develop a query in the editor, paste it into a dashboard widget, or script it through the Data API. Boards live-update on a 24-column grid, auto-refresh for wall displays, and export as JSON, or let AI Insights compose the whole board from one question.

// For engineering leaders

Stop paying for re-runs

It starts with one red build. Nobody is sure whether the change broke something or the pipeline just stumbled, so someone clicks re‑run. Then again, and again. Each attempt is half an hour of an engineer waiting, switching context, and losing the thread of the work they were doing. Spread that across every pull request and every team, and instability becomes a quiet tax on everything you ship. It rarely shows up in a report, so it rarely gets fixed.

Buildnote puts it in plain sight. You see how often your trunk is really green, which failures come from flaky tests and which from the infrastructure underneath, and how many hours the re‑runs are costing. Fix the root cause, and the same numbers show the payoff.

41 re-runs of one pull request

~30 minutes each

20+ hours before it merged

Build stability

16% before

94% after

Infrastructure fixed

See what instability costs
Failure rates, build durations, and the flakiest tests for every repository and branch, so lost engineering time becomes a number you can put in front of the business.
Fix the cause, not the symptom
AI Insights separates infrastructure blips from real regressions and unstable tests, so platform investment goes where it pays back instead of into more re-runs.
Prove the return
Dashboards track trunk stability and pipeline health before and after every change, giving leadership a live view of the improvement rather than an anecdote.