Verified Gallery

Verified Claude Builds Gallery

Real builds are more useful than generic prompts. This gallery collects projects with a named builder, a named Claude surface, public source, credible evidence, and a reusable pattern.

Updated 2026-09-198 minClaude Code projectscase studiesgallery

Key Facts

Page summary for citation

  • Verified Gallery: Real builds are more useful than generic prompts. This gallery collects projects with a named builder, a named Claude surface, public source, credible evidence, and a reusable pattern.
  • Evidence trail: 10 sources, including Anthropic, Anthropic Docs, Anthropic Docs.
  • Internal context: related pages include Zettair, Spotify, Venus Map.

"Claude Code is an active collaborator"

Anthropic launch post - source

What qualifies as a build

A build is included when the public record identifies what was made, who made or deployed it, what Claude surface was involved, and what evidence supports the claim. That evidence can be an official customer story, a public repository, an official product document, or a builder post with enough detail to evaluate.

The gallery intentionally mixes enterprise systems and public projects. Spotify, Ramp, and Rakuten show how large organizations operationalize Claude Code. Zettair shows an individual expert steering an agent through a complex product. Public repositories show the tooling layer that has grown around the ecosystem.

  • Included: case studies, public projects, templates, integrations, build systems, and evidence-backed demos.
  • Excluded: unsourced social claims, prompt-only posts, speculative roadmaps, and duplicate tutorial content.
  • Linked out: step-by-step implementation guidance belongs on sibling sites in the Claude Network.

The missing lens

The useful question is not whether Claude Code can produce code. The useful question is what kind of system the human built around it: a local feedback loop, a CI workflow, a fleet migration system, an MCP-connected operations loop, or a template that packages team norms.

Each project card names that pattern so readers can compare builds across different levels of maturity. A public demo and a production migration agent should not be judged by the same evidence bar, but both can teach something concrete.

Which model and surface built it, as of September 2026

Every card now carries a surface line, and that line is often an admission. The enterprise case studies on this site were published before the current lineup existed, and none of them name a model generation. Their numbers describe Claude Code and Claude Agent SDK as those products worked at the time, so they should not be read as Opus 5 or Fable 5.1 results.

The current generally available lineup is Claude Fable 5.1 for the most demanding long-horizon agentic work, Claude Opus 5 as the recommended starting point for most workloads, Claude Sonnet 5 for the speed and intelligence balance, and Claude Haiku 4.5 as the fastest and cheapest. Anthropic notes that Fable 5.1 defaults to High effort inside Claude Code and Medium on Claude.ai and in Claude Cowork, which is enough on its own to make two runs of the same prompt incomparable across surfaces.

The surface list has also grown past the terminal. A build can now come out of Claude Code, Claude Code on the web, the Claude Agent SDK, Claude in Chrome, or Claude Cowork, and each one leaves a different kind of evidence behind. For the full model comparison and pricing question, see claudecentral.com; this page only cares about attributing a build correctly.

  • Record the surface and the model generation when the source states them, and say so plainly when it does not.
  • Treat undated adoption metrics as claims about a product era, not about a specific model.
  • Effort settings differ per surface, so a Claude Code result and a Cowork result are not the same experiment.

All Gallery Cards

Individual build - Public demo

Zettair search-engine replica

Hugh Williams

View build

A scale-replica search engine over a large English Wikipedia slice, with autosuggest, query-biased snippets, related queries, topic summaries, and an iOS app in progress.

It is one of the clearest public examples of Claude Code amplifying deep domain expertise rather than replacing it.

1.5MWikipedia articles searched
50 QPSreported single-box capacity
0hand-written lines claimed by builder

Claude surface

Claude Code; the source does not name a model generation

Pattern

Senior engineer steering an agent through a full-stack search product.

Enterprise system - Production workflow

Spotify migration agent

Spotify

View build

A background coding agent for complex, semantic code migrations across large repository fleets, integrated into Spotify Fleet Management.

The case shows Claude-powered agents moving beyond local pair-programming into repeatable, organization-scale code transformation.

90%reported time savings
650+agent-generated PRs merged monthly
July 2025integration date reported

Claude surface

Claude Agent SDK; the source does not name a model generation

Pattern

Claude Agent SDK embedded inside an internal fleet-management system.

Enterprise system - Engineering org adoption

Ramp engineering workflows

Ramp

View build

Ramp reports using Claude Code for test automation, documentation, parallel development, incident response prototypes, and project-management-to-code workflows.

It demonstrates Claude Code as a composable developer tool inside an already high-throughput engineering culture.

50%weekly use among engineers, nearly
1M+AI-suggested code lines processed monthly
80%possible incident triage reduction reported

Claude surface

Claude Code and MCP; the source does not name a model generation

Pattern

CLI extensions, parallel sessions, MCP-connected incident response, and ticket-to-code loops.

Enterprise system - Large-company deployment

Rakuten autonomous coding

Rakuten

View build

Rakuten reports Claude Code results across autonomous refactoring, faster feature delivery, code modification accuracy, and managed-agent deployment.

It is a rare public enterprise case with concrete time-to-market and accuracy metrics attached to Claude Code.

79%time-to-market reduction reported
7hsustained autonomous coding reported
99.9%complex modification accuracy reported

Claude surface

Claude Code and managed agents; the source does not name a model generation

Pattern

Autonomous coding plus managed agents spread across business functions.

Official integration - Official workflow

Claude Code GitHub automation

Anthropic

View build

The official GitHub Actions integration exposes Claude Code inside GitHub workflows and is also a base layer for custom automation.

It is the connective tissue between one-off coding sessions and agentic engineering workflows that leave reviewable artifacts.

@claudetrigger surface
PRscreates and edits
SDKbuilt on Agent SDK

Claude surface

Claude Code GitHub Actions

Pattern

Issue and PR workflows where a mention can create PRs, implement changes, or review code.

Enterprise system - Internal examples

Anthropic internal product builds

Anthropic teams

View build

Anthropic describes internal teams using Claude Code for tests, reviews, incidents, prototypes, visualizations, documentation, and knowledge search.

The examples show the same tool being used by engineers, designers, security teams, infrastructure teams, and data scientists.

3xfaster incident scan reported
80%research time reduction reported
Reactvisualizations built by data scientists

Claude surface

Claude Code; the source does not name a model generation

Pattern

Design-to-code loops, incident diagnosis, test writing, React visualizations, and codebase discovery.

Research artifact - Released artifact

Venus elevation map

Anthropic, with Claude Fable 5.1

View build

Anthropic reports that Claude Fable 5.1 trained a neural network to produce a new high-resolution elevation map covering a third of Venus, built from NASA Magellan radar imagery, and released it under a Creative Commons license.

It is the rare frontier-model demonstration that ends in a downloadable artifact other people can use, rather than a benchmark row.

1/3of Venus covered, per the announcement
2-3kmdetail, improved from 10-20km
CCCreative Commons release

Claude surface

Claude Fable 5.1

Pattern

Model trains a neural network over decades-old public instrument data and ships the output as a licensed dataset.

Research artifact - Reported, open-sourcing planned

GPU kernels for open-source science models

Anthropic, with Claude Mythos 5.1

View build

Anthropic reports that Claude Mythos 5.1 wrote custom GPU kernels and cached intermediate results to speed up seven open-source protein and genomics models, working only from publicly available source code.

The verification story is the interesting part: the speedup claim is paired with identical outputs, which is the kind of check a build note can actually evaluate.

Up to 2.5xinference speedup reported
30-60%estimated GPU cost savings
7open-source models optimized

Claude surface

Claude Mythos 5.1, trusted access only

Pattern

Agent reads public source code, writes custom kernels, and proves the change by holding outputs identical.

Primary Sources

Source trail

Links are cited for factual claims on this page. Access dates are kept so future updates can re-check drift.

Claude Code overview

Anthropic Docs - official docs - accessed 2026-07-06

Open citation

Current capability overview: local, web, IDE, git, MCP, hooks, skills, and agents.

Choosing a Claude model

Anthropic Docs - official docs - accessed 2026-09-19

Open citation

Current generally available lineup and official guidance on picking between Fable 5.1, Opus 5, Sonnet 5, and Haiku 4.5.

Claude Opus 5

Anthropic - official blog - accessed 2026-09-19

Open citation

Opus 5 launch on 2026-07-24, the default on Claude Max and the strongest model on Claude Pro.

Ramp Claude Code case study

Anthropic Customer Stories - official case study - accessed 2026-07-06

Open citation

Ramp engineering workflows for test automation, docs, parallel development, incidents, and ticket-to-code.