Evose

Evose is live. We built the operating system for AI-First companies — agents by role, output by design, accountability at scale. This isn't another AI tool. It's how the next generation of companies will be run. → evose.ai

Google Cloud introduced project-level AI spending caps that can pause agent API calls when budgets run out, alongside alerts that identify the services driving a cost spike. Building an AI-native organization means making that spending visible across the agents doing the work. Evose lets teams see which models each agent uses, how much it consumes, and what it costs. For example, a team can check its content agent's model usage and spending separately from its research agent's. Managers can then set budgets and quotas around the agents their organization runs.

Every AI agent needs an owner. That owner decides who can use, edit, or monitor the agent, and who can see what it does and how much it costs. Evose manages agents and people under the same set of rules.

Halloween is this Saturday. Got plans? We asked an Evose agent what to wear and where to go in Chicago. It found five trending costume ideas with nearby stores, plus five events with dates and links. We built the agent with one sentence. A person checked every link before we posted.

Me: grabbing a coffee. My agent: pulled together the client proposal, checked the numbers, and is now waiting for my review.

Your sales agent drafted the quote, matched three lookalike accounts, and asked before sending anything. You approved it at 2:02. Every step is on the record.

Picture a pharma legal team reviewing contracts across multiple jurisdictions. Each jurisdiction has its own rules. Every contract takes hours to review, and a risky clause can be easy to miss. With Evose, an agent handles the initial review while people stay in control: · Knowledge: Retrieve relevant clauses, regulations, and templates from your organization's knowledge base. · Access: Set team-level permissions for agents and knowledge. · Approval: Flagged findings go through human review. · Audit: Track workflow activity, sources, and approvals. AI handles the first pass. Your team makes the final call.

Meet one of my work buddies: the Content Operations Agent It researches campaign context, drafts content, and routes it to human review before anything goes out. On Evose, you can build your own agents around the way you work. What would you want your agent to take off your plate? 👇

Happy Programmers' Day! The tools are changing. The work is changing. But building something that works, solving hard problems, and making good decisions still take skill, curiosity, and care. Here's to the developers who keep figuring things out, one commit at a time.

Easy tasks don't need your priciest model. In Evose, you pick the model for every agent. Claude, GPT, Gemini, Kimi, Grok and more, all in one list. Switch anytime, and add image, video and voice models too. Multi-model. One governed layer.

Connecting AI agents to external tools often means configuring MCP servers one by one and testing each connection. Evose Tool Market brings 137 tools into one place, from Gmail and Google Sheets to Slack, Jira, and GitHub. · Browse, install, and add tools to your agent. · Ask once. Your agent calls the tools it needs. The list keeps growing.

Your AI agents are in production. Do you know which ones your team actually uses and how much they're consuming? Evose Observability gives you a clearer picture: • Conversations, duration, and token usage by app • Usage broken down by team member • Daily trends and an agent leaderboard Stop guessing. Start measuring. Watch it in action 👇

Finding your best ad takes a minute. Finding out what's in it takes someone watching every video. On Evose, one workflow does that for you. Past ads and competitors' ads go in as raw files. It pulls key frames, transcribes the audio, splits each video into shots and tags every shot by the same rules. Set beside your performance data, the tags show which combinations keep appearing in the winners, and new scripts get drafted from those patterns. When the brief changes, drag a node and test the new flow. What used to take weeks of engineering now takes hours.