Table of Contents

• TL;DR

• Prologue

• Inspiration

• Vision and Mission

• Product

• Team

• The Future

• Join Our Waitlist

• Or Start Now

TL;DR

Prologue

It’s been a year since we formed our company. Our original mission was to answer one of the world’s most frequently asked questions: “What’s for dinner?” … so families could have an easier time connecting around the dinner table more often.

Our vision was to create something the whole family would have fun using - swiping likes & dislikes to create the perfect dinner “playlist”. Think Tinder meets Spotify. In addition, we wanted to automate the task of creating grocery lists, eliminate the need to go to the grocery store and build a supportive community that shared - recipes, tips, stories and causes.

Under the hood, we knew the only practical way to achieve this vision was to power it all with AI. So today, when you open up Dinnerfy, everything you see and experience in the app is curated by an assortment of AI-powered systems and models. Every food image is AI-generated. Every recipe was either created or curated by AI. The dinner feed recommendations and sequencing all use AI. In total, Dinnerfy has 3 different AI subsystems and it uses 3 LLMs from 3 different vendors.

We are so grateful to the thousands of people who’ve embraced Dinnerfy and we’re super excited to see this community continue to grow. If you’re interested in becoming a part of it, it’s easy and free to join .👇🏻

Inspiration

While Dinnerfy is a very simple app, it was surprisingly challenging to build. Collectively, our team has been building AI systems for nearly a decade. But as we sprinted to execute our product vision, it remained frustratingly time-consuming for our experienced Dev team to build the plumbing required to make Dinnerfy deliver the right content quickly and reliably.

Much of the work was foundational and would’ve been required regardless of the application we were building on top of it. Things like …

1. Connecting, vectorizing and indexing data sources

2. Connecting and testing different large language models

3. Developing prompt management middleware

4. Developing async request & response middleware

5. Coding the API needed to inject AI results into the app

6. Keeping up with and implementing a steady stream of updates

As we worked to make our application easier to maintain and optimize, an idea began to form.

We decided to find out. Our first call was to a CTO we knew who was running a SaaS business that made extensive use of AI. While the call ran 90 minutes, it was instantly clear that he wanted what we were describing even more than we did! Three weeks of validation netted us 5 paid subscriptions - for a product that didn’t even exist yet. 🤩

In addition to finding customers willing to pay us before we even had a product, we saw a few other promising signs:

• There was consensus on the problem to be solved

• People were spending a lot of time & money to solve the issue

• The platform would clearly be both integral and sticky (suggesting high lifetime value)

• Investor interest was strong

Needless to say, this early validation made our decision to pivot almost an afterthought. Much bigger than whether to pivot was “What should we call it?” and “How should we finance it?” Deciding on the name Lamatic.ai took us 2 days. Assembling a group of smart investors who believed in our vision and getting the money into the bank took 60 🌪️

Vision and Mission

GenAI is evolving incredibly quickly and there’s nothing to suggest it will slow down any time soon. Its adoption has been faster than other foundational technologies (like blockchain and VR) because of its broader applicability and faster payback. The volume of AI hype right now illustrates just how strongly the world believes that GenAI will deliver game-changing benefits for businesses across the spectrum of industries.

However, doing GenAI is not easy and keeping up may be even harder. At least 3 new models and around 400 published research papers are released each month. Each release might offer a critical enhancement to your application. Divert your attention for even a month and your application could fall behind or even break. Ensuring performance, quality, security and ongoing optimization should be at core of any company’s plan to create GenAI products.

You need a strategy for keeping these systems current and for continuously optimizing them. Big companies are told to create a dedicated “GenAI Platform Team” with this as its sole responsibility. But for smaller companies this isn’t just impractical, with a managed GenAI platform like Lamatic.ai available, it will also be dumb.

While there are other options available that can be used to address these challenges (like Langchain and lamaindex), they are designed to serve those that are already skilled GenAI developers. CTOs that abdicate responsibility for how this infrastructure gets built will find themselves saddled with an array of implementations and a ton of agility-killing technical debt.

Speed and agility are enhanced when foundational capabilities are integrated into the operating system. Much like the WordPress and Shopify ecosystems accelerated value delivery in website publishing and ecommerce, Lamatic.ai is doing the same for GenAI.

Product

Lamatic.ai is a fully managed end-to-end collaborative GenAI development platform. This means it delivers everything you need - both now and ongoing. Our powerfully simple platform gives you access to the latest AI capabilities by making them easy-to-use product features.

Lamatic.ai allows teams to add GenAI to products without changing the Product Roadmap. Launch features or entire apps in 5-10 days (instead of 3-4 months) while also eliminating development and ongoing maintenance costs. Its UI-based approach means configurations can be changed easily by business users - enabling much faster iteration and driving greater adoption. Lamatic.ai not only makes it easier and faster to build GenAI apps, it also ensures reliable world-class performance.

Team

Our team combines seasoned and complementary founders with young blooded engineers. Charles Whiteman, the business brains has scaled Divers Direct to the world’s largest SCUBA retailer and helped guide MotionPoint to a 70M exit. Aman Sharma, the tech half, has built and scaled 3 tech startups and an assortment of AI products used by millions. Our first two engineers, Naman and Vansh joined the team as interns on the Dinnerfy build and were key contributors to Lamatic.ai’s foundation. Arun and Maruthi joined later to turbo charge our nimble team that’s laser-focused on delivering Lamatic.ai’s product vision fast.

Shameless Plug - if you’re interested in joining our rockstar team, Apply Here.

The Future

As we commit to bringing you the world’s best GenAI integration platform, we know we’re going to need your help - to encourage us, to guide us, and to help us share the Lamatic.ai story with the world. Soon we’ll launch our redesigned website and we can’t wait for you to see it. In the meantime, here’s a sneak peak. We’d love to hear your feedback!

Soon after our new website, we’ll release the first public-facing version of Lamatic.ai. While it’ll be a major step toward the No Code interface we envision, there will be plenty left to do. Your feedback will be key in helping us sequence this work.

Join Our Waitlist

Join Waitlist - If you like where we’re headed and would like regular updates as this journey unfolds, please join our waitlist. 👇🏻

Or Start Now

Start Now - Do you have a current project and an urgent need to power your custom application with GenAI now? We can accommodate a limited number of pre-release onboardings. Let’s talk.