The usual CI feedback loop looks like: commit, push, wait several minutes for a hosted runner to spin up, read the log, repeat. Preloop collapses that loop by running the exact same GitHub Actions workflow locally, each job in its own hardware-isolated microVM that boots in under 200ms — fast enough that testing your uncommitted, in-progress changes feels instant rather than batched. Because it reads your working tree as-is (including uncommitted and untracked files), a failing step can hold its microVM open with the toolchain, services, and caches exactly as the test left them — you attach a debugger, fix the code, and retry just that step instead of rerunning the whole pipeline from scratch. It's explicitly built around AI coding agents generating code fast enough that manual review can't keep pace: Preloop verifies that generated code actually passes CI before a human even looks at it, using the real GitHub Actions wire protocol rather than a reimplementation.
| # | Tool | Best for | Price |
|---|---|---|---|
| 1 | Professionnels de la génération de leads, équipes commerciales, chercheurs de marché, spécialistes SEO local, campagnes B2B | — | |
| 2 | Équipes développant des agents IA en production, responsables IA, équipes sécurité/conformité dans les secteurs régulés | — | |
| 3 | PME et agences qui veulent automatiser le support client multilingue sans écrire de script manuellement | — | |
| 4 | Équipes d'ingénierie IA/ML qui doivent nettoyer les données avant de les envoyer à un LLM ou une base vectorielle | — | |
| 5 | Équipes déployant des agents IA connectés à des systèmes réels (bases de données, API) et voulant vérifier les actions avant exécution | — | |
| 6 | Développeurs et équipes construisant des serveurs/agents MCP pour Claude, ChatGPT ou des systèmes d'agents internes | — | |
| 7 | Utilisateurs mobiles qui veulent de l'aide IA directement dans leur clavier (messages, mails, notes) sans changer d'appli | — | |
| 8 | Analystes de données, veille concurrentielle, génération de leads, chercheurs produit, entreprises ayant besoin d'extraction de données web structurées et fiables | — | |
| 9 | Insurers | Auto repairers and dealerships | Fleet/rental companies | Claims adjusters | — | |
| 10 | Mobile app developers | Edge/wearable device builders | Privacy-focused AI products | — | |
| 11 | Étudiants | — | |
| 12 | Étudiants | — | |
| 13 | Étudiants | — | |
| 14 | Étudiants | — | |
| 15 | Étudiants | — | |
| 16 | Étudiants | — | |
| 17 | Développeurs | — | |
| 18 | Étudiants | — | |
| 19 | Développeurs | — | |
| 20 | Développeurs | — |
Type in a search term like "plumbers in Chicago" and get back a spreadsheet of every matching business on Google Maps — names, phone numbers, emails, ratings — instead of manually copying details out of Google Maps one listing at a time.
An observability and reliability layer for AI agents in production that scores every agent run in real time and can pause a risky one before it acts, with a free tier up to 25,000 spans/month.
A chatbot that reads your website and support docs on its own, answers customer questions automatically, and gets better every time a human corrects a wrong answer — no need to manually write and update a script.
An API that automatically strips personal information like names and addresses out of text before it's sent to an AI model, so sensitive data never actually reaches the AI provider.
Double-checks what an AI agent is about to do against your real, live data before letting it happen — so it can't confidently take an action based on outdated or wrong information.
A cloud hosting platform built specifically for MCP servers — the plumbing that lets AI assistants like Claude or ChatGPT call your tools and data — so a developer can deploy, monitor, and scale an MCP integration without building the hosting infrastructu
A remote-controlled, disguised web browser for AI agents — it lets an AI click around real websites like a person instead of getting blocked by "prove you're not a robot" checks, and can teach itself to repeat the task next time.
AI that looks at photos of a damaged car or house and writes up a repair estimate in seconds — the kind of assessment that normally takes a human adjuster days.
Open-source on-device AI runtime that runs LLMs, vision models and speech models directly on phones, laptops and wearables — no cloud round-trip required.
Joy Morris, Dave Morris
