AI case studies
Explore how real companies build products and use AI. Find clear stories about their work, reported results, and lessons for your own team.
The DenThe Den: How AI Drafts Club Permits and Grants
The Den used ChatGPT Work to pull records from Gmail, Slack, and Drive. The owner drafted permit and grant packets faster while team leaders checked the details.
Proaction: Custom Fleet Demos Built with Codex
Proaction makes fleet software. Colin Knudsen used Codex to build demos from buyer notes. He reported saving time and moving more deals to the next sales step.
NeuroNeuro AI case study: turning feedback into a store pitch
Neuro sells gum and mints. Co-founders Ryan Chen and Kent Yoshimura used ChatGPT Business to study buyer feedback. They used this data to support a retail pitch for CVS. These results were maker-reported and not isolated to AI.
Plex CoffeePlex Coffee AI Case Study: Use the Handbook on Shift
Plex Coffee linked a store handbook to ChatGPT. Staff asked shift questions on an iPad instead of texting managers. Co-founders reported fewer WhatsApp messages and faster onboarding.
Augment CodeAugment Code: Context Search for Large Codebases
Augment Code runs Claude on Google Cloud to help coders read and write big software. The team links AI models with deep code context to speed up work.
CodeRabbitCodeRabbit: AI Code Reviews on Pull Requests
CodeRabbit uses Claude to review pull requests and suggest fixes. Engineering teams must measure whether these automated notes catch real software bugs without slowing down human reviewers.
GreptileGreptile case study: AI agents that review code
Greptile builds AI code review agents that inspect entire codebases. We evaluate whether multi-hop context searches catch software bugs while managing compute costs.
QodoQodo Case Study: Checking Code Made by AI
Qodo uses Claude to review pull requests, build tests, and spot bugs. This case study outlines how teams inspect automated code changes with outside tests.
BitoBito: Track Time Spent on Code Reviews and Fixes
Bito builds AI agents to help software engineers write and review code updates. The vendor reports faster reviews and high dollar returns on review spending. Engineering teams can test their own real review time and fees to measure true savings.
cubiccubic: AI Code Reviews and Shipping Speeds
cubic uses Claude to inspect software changes and organize code files. The company reported two-minute review feedback and faster release cycles. Teams can test these review steps before trusting automated suggestions.
FactoryFactory: autonomous software Droids with Claude
Factory builds AI Droids for software teams. Using Claude models, these tools read project tickets and draft pull requests. Human engineers still inspect the proposed code changes before merging them into main branches.
WarpWarp: stay in control when AI runs commands
Anthropic reported that Warp connects developers to Claude inside a modern terminal. Warp reports millions of agent sessions. Teams must still set clear limits before agents run shell commands or edit source code.
READ. CHECK. TRY.
Learn from a story. Plan a small test.
A good AI story shows the real work. It shows the steps, results, and unknown parts. Test tools on your own small jobs first. Do this check before spending any company cash.
Does AI help with code?
Smart coding tools can change many software files. Yet every change must pass real project rules. Track wait times, human work, reviews, and errors. Typed lines and accepted code show raw work. They do not prove that real users got help.
Read the Rakuten story with other research reports. Study methods and test jobs are quite different. Comparing them helps you ask better buying questions. Ask if tools really help the whole project. Big firm stories are ideas, not money guarantees.
Can AI help a small business?
Start with a repeating job with clear steps. It could mean updating sales leads or notes. Draw every step of the current work first. Write down who owns data and fixes errors. This keeps fancy software from making messy double records.
The System AI setup works inside familiar screens. The Oversee tool gathers facts for human review. You do not need to copy everything here. Find the work that costs you too much. Then make sure humans can inspect every result.
Is the work worth the cost?
Write down your start numbers before any test. Track time spent, errors, reviews, and final value. Count jobs that failed or changed goals too. Moving extra work to others saves no money. Seller claims are not audited company profit gains.
Use the chart to pick proof you need. Small tests only need good work at fair prices. Systems facing users need safety and recovery checks. State your basic thoughts clearly without wild guesses.
Check the claim against the facts
| The claim | What to check | What else to count |
|---|---|---|
| Code gets done faster | Time spent on the same checked task | Time spent on reviews and fixes |
| Daily work costs less | Cost of each task done right | Setup time and the cost of errors |
| Sales grow | Real sales tied to the work | Costs, refunds, and money spent finding buyers |
| The tool works on its own | A task done without a person stepping in | Access rules and what happens if it fails |
Common questions
What is a case study about an AI helper?
It looks at how a team used a tool. The tool might read files, change code, or check a result. A useful study shows what people did too.
Are these stories about Roseram customers?
These pages use public stories and studies. The group that published each source owns its claims. We have not run those tests ourselves. A story does not mean the team uses Roseram.
Can a small business learn from a large one?
Yes. Pick one useful idea from the story. Try a small test with sample data. Check your own costs and results before doing more.
Browse every AI case study
- The Den: How AI Drafts Club Permits and Grants
- Proaction: Custom Fleet Demos Built with Codex
- Neuro AI case study: turning feedback into a store pitch
- Plex Coffee AI Case Study: Use the Handbook on Shift
- Augment Code: Context Search for Large Codebases
- CodeRabbit: AI Code Reviews on Pull Requests
- Greptile case study: AI agents that review code
- Qodo Case Study: Checking Code Made by AI
- Bito: Track Time Spent on Code Reviews and Fixes
- cubic: AI Code Reviews and Shipping Speeds
- Factory: autonomous software Droids with Claude
- Warp: stay in control when AI runs commands
- Graphite: review code changes in small steps
- JetBrains Junie case study: checking code changes
- Tabnine case study: when fast code help works
- CircleCI Chunk: check an AI fix for a failed build
- Semgrep: Filtering Code Security Alerts with Claude
- Sentry: Automated Bug Fixing with Claude Managed Agents
- GitLab AI case study: Duo features and Claude integration
- Postman: Coordinating API Specs, Code, and Tests
- Mintlify AI Case Study: Keep Software Guides Aligned with Code
- CodeWords study: why chat setups still need clear rules
- Lazy AI Case Study: Building Apps with Claude
- Anything AI case study: checking app completion rates
- Emergent: Autonomous Coding Agents with Claude
- Base44 case study: building full apps from plain words
- Lovable Case Study: Software Creation with Conversational AI
- Bubble AI case study: chat and visual tools
- Study on Replit Agent: Building Software From Plain Text
- Intercom Fin: check which customer problems AI solves
- Decagon case study: personal help without made-up facts
- Assembled case study: make the AI handoff work well
- Kodif case study: customer support automation with Claude
- Tidio Lyro Case Study: Support Automation for Online Stores
- Chatbase: Support Across Many Chat Apps
- Vambe Case Study: Sales Chats Need Firm Proof
- Impel AI Case Study: Training Teams with Roleplay
- Grab Merchant AI: check how it helps local shops
- Lyft AI support: fast fixes and human help
- Hebbia case study: searching complex financial documents
- Rogo: Generating Financial Files with AI
- Chronograph Case Study: Scaling Private Equity Data Work with Claude
- BlueFlame AI and Financial Document Analysis
- Ramp: How Claude Code Speeds Software Delivery
- Brex AI Spending and Expense Review Study
- bunq Finn: AI Banking Support and Multi-Step Checks
- N26 AI Case Study: Clear Tasks and Limits
- Qonto AI study: draft bills and check payments
- OffDeal case study: using one agent for M&A advisory
- AirOps AI Content Workflow Case Study
- Clay AI Case Study: Prospect Research and Sales Outreach
- Copy.ai Case Study: Marketing Content on Claude
- Supermetrics AI: check the number before changing the ad
- Triple Whale AI Study: E-Commerce Store Analytics
- Descript case study: video cuts need a clear timeline
- OpusClip uses Claude Code to build revenue tools
- Gradial Case Study: Marketing Campaign Execution
- Mutiny Case Study: AI Sales Pitch Decks
- Advolve AI Review: Managing Digital Ads with Claude
- Amira Learning Uses Claude for Reading Practice
- StudyFetch and Claude: Turn Course Notes into AI Study Tools
- Study of RileyBot in the Classroom
- MagicSchool AI: Help Teachers Check Every Draft
- Solvely AI case study: interactive diagrams for student learning
- Pollo AI study: how to make video ads that keep real product facts
- LegalOn Codex study: track cost per accepted change
- Radisson Hotel Group AI Case Study: Chat Discovery and Direct Bookings
- Jump Trading AI Review: Testing Research Agents
- Chatham Financial trade checks with AI
- Basis AI Case Study: Testing Spreadsheets Across Tabs
- Ollama: Running Open Models on Your Own Hardware
- Aider: Review AI Code Edits Before You Save
- Dify: check which data each AI tool can use
- Langfuse: How Developers Trace and Test AI Systems
- OpenHands Agent Canvas Case Study
- Continue Case Study: Coding Help and Upkeep
- Tabby case study: test self-hosted code helpers
- MLflow: Test AI Work Against Clear Rules
- Haystack case study: test finding files and writing answers separately
- Docling: Parsing Complex Documents for AI
- Rakuten Claude Code case study: check the whole release
- Accenture and GitHub Copilot: Developer Output Study
- Does AI Help Coders Work Faster? A Study by METR
- System AI: turn WhatsApp notes into work tasks
- Oversee: Search Faster and Keep People in Charge
- Flow AI Uses Voice Tasks and Strong Data Rules
- MSH Studios: Building Frame My Home and Spellbound with Bolt
- GenAIPI: Building a Software Business on Replit
- Photo AI: From Selfies to AI Photos and Videos
- Midjourney: How People Make Images with Text Prompts
- Cursor: review an AI code edit
- Aragon AI: AI Headshot Generator Analysis
- GitHub Copilot: AI Code Help in Developer Workflows
- Gamma: Turn Text Prompts into Slide Decks
- Perplexity: check the sources behind an AI answer
- Notion AI: Built-In Tools for Team Docs
- ChatGPT: turn a short note into a clear draft
- Anthropic Claude: Turn Raw Files into Working Drafts
- Zapier AI: Connect AI Models to Daily Apps
- ShipFast: Launch Web Apps Fast With Code Starters
