Humans × Agents. One workspace. OpenMax is the world's first workspace for AI-Native teams. Your people and your AI agents sit in the same channels and work off the same memory. 400+ agents deployed · 8+ channels · 99.9% SLA Try it free for 7 days → openmax.com

Ten minutes to decide whether a product is worth selling. Three days to get there. Tabs everywhere, a spreadsheet scrolled so far the header's gone, fees and freight picked apart by hand. Three days of data entry to support ten minutes of actually deciding. An AI team does that part now. It gives you back the ten minutes, and there's one thing it won't do.

One of our customers told us his AI owned up to a mistake. It finished a task, flagged a step it had botched, redid it, verified the file, and offered to scrap it all if he wasn't happy.

Reps are slammed all day, yet almost none of it goes to the customer. The hours vanish into CRM upkeep and the kind of data entry no one signed up for. OpenMax Workspace shoulders all of it. Drop a lead into the chat and it files the record, weeds out duplicates, and fills in whatever's missing, then surfaces the accounts going cold and even books that first meeting. What used to swallow days now wraps in about ten minutes, so reps can just show up and close. And it slots into whatever CRM and tools you already run on, so nothing gets torn out and rebuilt.

"There's going to be a moment where agent-written exquisite code exceeds human-written error-filled code." — Chamath, All-In E288. Code is just the first place agent work gets checkable. Triage, reporting, QA and monitoring are next — jobs OpenMax AI employees already hold. Which one would you hand off first?

This is everywhere today. An Anthropic researcher @hilbertspaess quit, says the top labs are racing to superintelligence and "gambling with our lives," and it's going viral. What stuck with me is his line about a bet this big getting run out of a private company's Slack. Yeah. A handful of closed labs sprinting to AGI behind a login screen, everyone else just hoping it works out. We do enterprise AI, and we go the other way. We bet on hybrid AI model structures and stay model agnostic. We believe the future of the AI world will have both open-source and closed models co-existing. What we care about is whether a company can actually run and own what it uses, instead of renting its future through one lab's API. Agents run the work, but a person signs off on anything that matters. Nobody's going to remember which lab hit superintelligence first. They'll remember who helped normal companies get real work done with this, cheap, without giving up control. His full thread here: nitter.net/hilbertspaess/status/2…

Hundreds of resumes reviewed in minutes, HR doesn't have to dig through the whole stack. OpenMax Recruiting Agent narrows a large resume pool down to a shortlist ready for review, then keeps the hiring process moving. Candidate ratings, interview scheduling, tailored questions, post-interview feedback, and a reusable talent pool all live in one workspace. See what it can take off your plate: openmax.com

Most SMEs buy AI tools that end up sitting unused. Last week at SCCCI's closed-door session, 40 SME owners spent under 30 mins at the OpenMax workshop and walked out having built their own AI employees on the spot, for marketing, sales, whatever they needed. Thanks SCCCI for having us. What would you hand off to an AI employee first? Try it: openmax.com/?utm_source=x&ut…

Most AI risk today can't be measured or insured. And more companies are putting AI into banking, insurance, and finance. The AI Risk & Assurance Forum on Sept 3 brings together government agencies, insurers, and financial institutions. Our Co-founder @CharliehuAI moderating the "AI Risk in Practice" panel. Thanks AgentSure,CRPO for the invitation. Sept 3, SMU Connexion, Singapore.

OpenMax is now officially SOC 2 Type 1 compliant! Tested and attested by PrescientSecurity under AICPA standards (SSAE 18). The first of many milestones in our data privacy goals — so our customers can have peace of mind about their data. Learn more at openmax.com/

AI phát huy hiệu quả tốt nhất khi được đưa vào đúng những công cụ mà đội ngũ sử dụng hằng ngày. Innovative Hub, một agency thương mại điện tử và marketing hoạt động tại Singapore, Việt Nam và Malaysia, đã kết nối OpenMax với Drive, dashboard, báo cáo và hệ thống chat nội bộ. Những công việc như nghiên cứu khách hàng, phân tích ngành hàng và chuẩn bị proposal cho sales, trước đây mất vài ngày, nay có thể hoàn thành trong 10 phút đến 1 giờ. Đó là lúc agent bắt đầu tạo ra giá trị thật: không phải thêm một chatbot bên ngoài, mà trở thành một phần của workflow hằng ngày.

Vietnam raises an important question: is demand for AI agents really just about labor cost? At Conviction, the answer felt broader than that. Businesses want to grow faster. Teams want to get more done. Young talent wants to use AI to stay competitive and create new opportunities. That is why Vietnam is becoming such an interesting market for OpenMax. The awareness is already here, the digital foundation is strong, and the appetite for AI is real.

Three days. That's what one sales proposal used to cost this team — chase the client, dig up the data, wrestle it into slides. Now? Done before lunch. The AI didn't get smarter. They just let OpenMax read their own data and run inside the dashboards they'd already built. A Singapore e-commerce team across three markets — and it just works. So be honest: is your AI stuck on the model, or have you just never let it near your real data?

Nobody's assistant "learned AI" from watching a webinar. So on Aug 27 in Singapore we're skipping the demo. You sit down and do two things yourself: build a week's worth of marketing content, and work through a real list of sales leads. Every draft the agent makes, you approve. Aug 27, 2–4pm · SCCIOB Computer Lab, Singapore.

Agents don't just forget because they're weak.​ They forget because we keep dumping every chat, rule, and task into context.​ Zylos is the in-house agent framework we built at OpenMax to clean that up. After 20K , it compresses memory, keeps what the agent still needs, and cuts token spend by 60%.​ At some point, shouldn’t agents learn what to keep and what to let go?