exployt

About the startup

exployt is a structured AI work environment where multiple AI models can collaborate like a real team. AI models are already intelligent enough to solve many problems, but intelligence alone is not enough. Just like a single person cannot run a large company alone, a single AI model cannot reliably manage complex projects without structure. exployt creates that structure. It gives AI models clear roles, responsibilities, rules, permissions, communication, transparency, and management. This allows different models — from any provider, local or cloud-based — to work together like specialized AI employees inside your own digital AI company. -------------------- Everything You Need for Your Own AI Company Build faster with AI agents that collaborate, not compete. 🤖 Multi-AI Orchestration: Your AI Chief Officers lead your AI company — managing hundreds of agents in parallel across 400+ models, including local ones. 💸 Up to 70% Lower Cost: The AI manager runs each task on the cheapest capable model and shadow agents handle the rest — up to ~70% saved for quick ROI. 🤝 Collaboration, Your Way: AI created teams, coding buddies, sequences and visual node-based workflows — choose exactly how your agents work together. 🔒 Security You Control: Granular permissions — agents touch only what you approve, a control AI grants access live from your rules, all GDPR-compliant. 🔍 Total Transparency: Watch agents work in real-time — live logs, progress, instant feedback, and every file change, decision, learning, message traced. 🧩 Ready-Made Modules: Community-created modules — account systems, payments, UI, agent setups — work out of the box and cut ~50% off a project. -------------------- Cut Your AI Costs by up to 70% Bring your own Claude Code or OpenAI Codex subscription — exployt saves you up to 70% on top of it. Three things make that possible. 🥷 Shadow agents do the heavy lifting: A cheap shadow model does almost everything — reading into the codebase, researching, and packaging a complete handover. Your expensive main agent is woken for a single turn to execute that fully prepared work order, then its session ends and the shadow carries on. You pay premium rates only for the moments that truly need premium intelligence. 🧠 Task-specific memory — never start from zero: Every learning, insight, and decision an agent gains is saved right to the task it belongs to — and so is every code change that task touched, its full file history, and a usage map showing which methods call which. A dedicated error database keeps each failure and its fix. So the next agent never begins from scratch: it starts from the exact code, context, and reasoning behind each task — everything already figured out. No repeated research means no repeated spend. 🎯 The right model for every task: exployt routes each task to the right-sized model — simpler work goes automatically to cheaper models, and only the genuinely hard parts reach for the expensive tier. You're never locked in, either: through OpenRouter and Ollama you can tap 400+ models, including local open-source and very cheap options, for example from China, that handle most tasks at often just 10% of the cost — used automatically when you allow it. -------------------- Biggest Agentic AI Problems Solved Every promise below replaces a real friction point in modern AI-assisted development. 🧠 AI With No Memory: Models forget every conversation. We capture chat, file changes, learnings and decisions task by task into a local, semantically searchable memory — so context survives across sessions, projects and teams. ⚖️ No Model Is Best Everywhere: Different providers excel at different work. Route each task to the model that fits, or pin a provider per agent role — mix premium, cheap and local models, and switch an agent's model later without losing its history. 💸 AI Costs Add Up Fast: Burning premium models on every task is wasteful. Shadow agents handle routine work on cheaper models, an AI manager picks the cheapest model strong enough per task, and memory avoids repeat work — up to ~70% saved. 🐛 AI Can't Really Debug: Our debug agents work like a human engineer — set breakpoints, inspect live variables, walk the call stack, read real logs, and even run the app, click through its UI and make screenshots to confirm the fix. 👔 Many Agents, No Manager: Coordinating many AI workers by hand burns the day. AI managers — CXO roles like a CTO or CPO — assign tasks, review output and resolve blockers, while agents coordinate over a dedicated agent-to-agent channel (Ai2Ai). 🗺️ Lost Overview And Control: When AI works fast, you lose sight of what it did. Everything is recorded locally from minute one — toggle tool calls, thinking, file changes and messages, rewind to the start, or find anything by meaning in seconds.