Newbrain Ventures

The first version of an AI system is where the real work starts. Many companies treat an AI project like a delivery: build it, launch it, done. In our experience, launch day is when the learning begins. Real users show you what the system gets wrong, what's missing, and what nobody thought to ask for. That's why we work in short cycles: release something useful early, see how the team uses it day to day, and keep improving as the business changes. Each new version builds on what's already there instead of starting over. Version one is never the final version. Anyone who has built software knows that. If you're planning an AI project, plan for version two from day one: → Budget time for improvements after launch, not just for the build → Give users an easy way to flag what's not working → Have someone on the team who owns the system and its feedback → Measure how it's used, not just whether it works

Before we write any code, we ask one question. Most AI projects fail for a simple reason: they automate a task instead of understanding the work. The request usually sounds like "automate this report" or "summarize these calls." But the projects that last start with a different question: how does this team actually make decisions? So when we start with a new client, we don't open with code, or with a pitch about what AI can do for them. The honest answer is that at that point, we don't know yet. Neither does the client. We don't even know if AI is the right solution. We sit down with their team first. We try to understand what they're really trying to achieve, what a good outcome looks like for them, and what makes them say no. Sometimes the answer is AI. Sometimes it's a simpler change in the process. Either way, what we build is based on how the team thinks, not on a single task. That's the difference between an automation and a system.

Firms like Vantora are building tailored AI systems inside real operations, not selling another dashboard. That tells you something: companies want integrations that solve their actual problems, not generic tools bolted onto old workflows. For marketing and growth leaders, the brief should shift too. Stop searching for plug-and-play software. Start finding the messy, specific processes worth rebuilding with custom AI. That's where lasting advantage gets built, quietly, one integration at a time.

You tried giving your team a social media course on how and what to post, you tried ghostwriting posts for your top executives. You tried reminding your people to post and nothing seems to work. Here’s how we solved that for enterprise clients. Meet KOL: Your team sees industry and company news, they pick what they want and an Ai trained on their voice writes the post for them. They review and post straight to LinkedIn. 10min per week and they’re done. Positioning at scale.

This is a great read on why chaos testing is something we should be thinking about before deploying AI systems. The idea is: inject failure into the system to uncover weaknesses before users do. It’s an interesting approach, and with autonomous agents, the challenge becomes even greater. The example described in this article highlights a different kind of failure: not a bug, but an agent acting on a flawed assumption with full conviction. Intent-based chaos testing is designed to catch exactly this. It probes how autonomous agents go off-script before they hit production. If your team is running autonomous agents, test for the moments when they’re completely wrong, but absolutely sure they’re right.

One presenter ran his talk through AI audience simulations before going on stage. He spotted blind spots in his argument and tightened his message before facing a single real person. That's where AI shines for public speaking. Not writing your script, but pressure-testing it. You get sharp, targeted feedback in minutes instead of waiting for a live room to go quiet in the wrong places. Next time you're prepping a pitch or client call, feed your outline to an AI critiquer. You'll feel the weak spots before your audience does.

Google just shipped a tool that reshapes how AI agents handle Gmail and Drive. Their new gws command-line interface strips away the friction between AI agents and Workspace services. No more awkward workarounds or messy API calls. The tool auto-updates when Google releases new endpoints, which keeps your automations running smoothly as their platform evolves. If your business needs automation, we are hosting a free invite-only webinar next month that covers where to start, how to think through it and how to determine the type of automation you need. Comment Newbrain and we’ll send you the invite.

The research caught us off guard. When AI agents interrupt, push back, or even go silent during conversations, they actually solve complex problems better. Accuracy climbed from 68.7% to 79.2% just by letting them communicate messier. Real collaboration has always been chaotic. We interrupt. We disagree. We wrestle with ideas until something clicks. Now AI that mirrors this natural friction performs better too. If you're choosing AI tools for teamwork, maybe it’s time to be a tougher boss.

The AI startup landscape just got clearer with six distinct categories emerging. A lot of founders aren’t sure what they’re building, they haven’t decided what kind of AI company they actually want to create. This confusion bleeds into everything: their pitch decks, customer conversations, even their product roadmap. Only 4% of companies genuinely weave AI through their entire business. Pick your lane early. Your investors and customers will thank you for the clarity.

Companies adopting ChatGPT Enterprise and similar platforms are 46% more likely to invest in additional software. Tech stack intelligence now beats traditional signals for spotting active buyers. Watch who's implementing enterprise AI tools. Those companies are gearing up to spend, not just window shopping.