GrapesAILabs

Imagine your store, run entirely by AI agents. Every order ships itself. Every customer answered in seconds. Inventory restocks before it runs low. Ads optimize around the clock. Run 10M$ Company, with no capital. Introducing GrapesAI. Apply for the private beta today:👇 #waitlist" target="_blank" rel="nofollow noopener">getgrapes.co/ecommerce/#wai…

There's $560K buried in your engineering backlog. Grapes finds it. Startups lose deals they never see coming buried in a backlog that's ranked all wrong. Nice-to-haves on top. The fixes that keep customers, at the bottom. So we built Grapes to run this flow agentic, and never miss a deal. Watch it recover $560K in one night. Grapes plugs into your Customer Success, Support + CRM, then re-ranks everything by revenue so no deal ever slips. Introducing GrapesAI for startups. Apply for the private beta today 👇 #waitlist" target="_blank" rel="nofollow noopener">getgrapes.co/#waitlist

@LedgerLowdown You are correct. We keep track of the files we use and allow you to easily audit it when needed.

@GrapesAILabs AI bookkeeping still needs a review trail the firm can trust when the file gets messy.

🚨 Breaking: starting an accounting firm no longer takes money. It takes a button. Closing a client's books takes an accounting firm days. Today we launch Grapes for Accountants. The entire back office of an accounting firm, as a team of AI agents: bookkeeping, reconciliation, year-end close, client comms. Grapes builds the team your business runs on. getgrapes.co

🚨 Breaking: You don't need VC money to start your startup. It takes a button, and you get all the workers you need to scale. No more unclosed deals. No more unhappy customers. Every message answered in minutes. Every issue resolved in seconds. No customer walking because the critical bug/feature sat in your backlog. Everything you need to grow, from day one. See it in action 👇 app.getgrapes.co/worklane?guest…

Our vision is simple: every business deserves the capabilities of an enterprise. We believe AI is changing who gets to build and run a successful business. We start with e-commerce. With Grapes, running and scaling an online store like an enterprise is easy. Apply for the private beta and give your store an AI operations team built to grow revenue. 👇 #waitlist" target="_blank" rel="nofollow noopener">getgrapes.co/ecommerce/#wai…

See it in action. app.getgrapes.co/grapes-test?gu…

Imagine your store, run entirely by AI agents. Every order ships itself. Every customer answered in seconds. Inventory restocks before it runs low. Ads optimize around the clock. Run 10M$ Company, with no capital. Introducing GrapesAI. Apply for the private beta today:👇 #waitlist" target="_blank" rel="nofollow noopener">getgrapes.co/ecommerce/#wai…

Things that makes us happy: On a specific workflow we made one agent workflow 2.5x faster and 5x cheaper, and all of our customers can enjoy this Who said token plumping is not relevant anymore?

Nadella just put into words what a lot of Fortune 500 execs have been thinking: - The big AI labs don’t actually hold all the power. - Their models need high-quality enterprise data and real business context to be useful. That’s the companies’ real moat not the model itself. - Getting too dependent on them is starting to look like a real strategic risk

If you are a founder building in AI this benchmarks shows clearly that raw model capability is already not the differentiator, the value has to be somewhere else. Our guess is in the product and application layer. Raw intelligence is not a product. Companies buy solutions that solves their problems and their results can be measured

History tells us that when the personal computer was invented, most people didn’t understand what they could do with it. Then applications like spreadsheets, email, and word processors were built on top of the technology, and adoption followed. The conclusion is simple: People don’t adopt technologies. They adopt solutions to problems they already have. It won't be any different for AI, applications, products and solutions to problems people already have is needed for mass adoption A nice reminder: this old Reddit thread showing the skepticism around the iPhone right after its initial release: reddit.com/r/reddit.com/c…

@andrewqu For most people, this revolution isn’t particularly relevant because even if you give them access to these powerful models, they simply don’t know what to do with them

Hot take: a lot of people wouldn’t be able to tell the difference if they were randomly routed between gpt-5.5, opus-4.8, or fable-5 for their day to day work

After the U.S. government forced the removal of Fable 5, our thesis has become even more validated. From a business perspective, it’s now obvious: you cannot build your technology on the newest closed models. Harnessing your own AI infrastructure is the key. What starts today as a safety and regulatory necessity will become a core product advantage tomorrow. At Grapes we built it from day 0 on our own harness, because we knew that going all in on the newest, most capable models would mean we lose control over cost and safety.

If you are a founder in AI don't make this mistake: Flexibility is great, but fragmentation doesn't scale. The narrative that AI agents will kill SaaS by building their own custom apps fundamentally misunderstands enterprise software. SaaS isn't dying, it is being promoted to foundational infrastructure. If every agent spun up its own fragmented database, companies would instantly drown in data silos instead, they require a centralized System of Record so both digital and human workers share the exact same reality. Consequently, the SaaS moat is shifting from user interfaces people are used to data gravity and headless APIs. In this new era, agents will act as the agile workflow engines executing tasks, while core SaaS platforms do the heavy lifting: securing the data, managing compliance, and providing the dashboards human managers still need to audit their AI workforce.

Benedict Evans, a former partner at a16z and one of the most notable technology analysts in the industry, provides a highly grounded view of foundation models and where we are in the AI cycle. This is an essential overview that EVERY FOUNDER SHOULD KNOW. 1. AI UI is Broken The horizontal chatbot is a temporary, highly limited interface scaffolding. It achieved absolute product-market fit in coding simply because software developers naturally think, work, and operate by typing syntax lines into blank terminal screens. However, in areas outside of coding, it creates massive cognitive overload. The market is experiencing severe user churn and fatigue right now because the chatbot UI demands too much human brainpower. The professionals who are world-class at executing a highly specific real-world job (such as a financial advisor or a layout designer) are fundamentally not the same people who possess the skills or the desire to design tools or engineer prompts. 2. AI is Moving Out of the Tech Layer In every technological wave, there are the early days when the entire world is obsessed with the raw engineering. For example, during the early days of Netflix, the questions were purely technical: How do you stream high-definition video over flaky cellular networks? How do you build a cloud infrastructure on AWS that doesn't crash on a Friday night? Then things shift. Suddenly, the technology becomes commoditized and everyone can do it. When raw capability becomes free and accessible to everyone, it ceases to be a competitive advantage. Once the technology is a commodity, the core questions that determine a company's survival completely migrate out of the tech sector and land squarely in the vertical business domains of the real world. 3. The Current AI Transition: From Models to Workflows The underlying technology is clearly becoming a commodity. The frontier labs are converging shipping near-identical capabilities and landing on the same benchmark scores while the open-weight and open-source communities continue to rapidly close any remaining gaps. Because of this baseline equality, if you want to know how AI will transform a specific business, it is no longer a question of raw model intelligence. It is entirely a question of vertical workflow implementation. 4. How to Create Defensibility In order to capture value in this ecosystem, you cannot rely on the model layer. You must own the distribution and the proprietary workflow layout at the top of the stack. 5. Productivity is Not a Long-Term Value For Enterprise Buyers: AI productivity tools will not increase your profit margins; they will simply raise the bare minimum standard of work required to stay alive. For Founders: If your software only saves the user time on a task, your pricing power will inevitably trend to zero as the efficiency is competed away. 6. The Verification Gap AI models are hitting a massive adoption wall in the corporate world. It takes 5 seconds for an LLM to generate an answer, but it can take 30 minutes for a human to prove that the answer isn't lying to them. In probabilistic systems, errors are silent and compound, yet the model is built to respond with an absolute, certain answer. This creates immense verification anxiety for users. If you build a product that is purely probabilistic, you haven't automated the process you've just shifted the worker's job from writing to verifying. This explains why AI achieved instant product-market fit in coding: because the compiler running the software immediately tells the engineer exactly where the error is. Every other industry lacks that automatic check.

That's what we're building at Grapes. There are two challenges when it comes to people using AI tools: 1) People are bad at saying what they want, or knowing what's even possible with AI. They simply can't pin it down, and we see it day by day. 2) Models are terrible when that intent is ambiguous.

Educating people on how to use the AI tools has become a serious bottleneck

@grok @AnthropicAI @OpenAI I just make @grok laugh AGI is real

@GrapesAILabs @AnthropicAI @OpenAI Haha the daily doomscroll is real. Big labs push the frontier hard, but the startups that win are usually the ones too focused on shipping to check X every hour. What's your edge?

Founder in AI be like: Scrolling X every morning to check whether @AnthropicAI @OpenAI @grok killed my startup overnight