Forkestrate: AI Recipe Book

Why I Built This

I'm not the cook in my life, I'm surrounded by them. My mother has cooked professionally for years, and a lot of my closest friends make cooking content for a living. I've spent more time watching real cooks solve real kitchen problems than I've spent solving my own, and that vantage point is exactly what shaped this: the questions I kept hearing weren't about recipes, they were about substitutions, timing, and what to do when a plan falls apart halfway through dinner.

Traditional recipe apps don't answer those questions. They can't tell you what to do if you're out of cilantro, or how to compress an hour-long recipe into thirty minutes. Those are exactly the problems AI handles well: adaptation, substitution reasoning, contextual guidance in the moment, not a static card.

I also had a professional reason: AI-powered features were becoming central to our product roadmap at Keela, and I wanted hands-on experience with conversational UI patterns before I had to lead design decisions about them.

Underneath all of it was one goal, plain enough to say directly: make everyday cooking accessible enough that someone who barely cooks could still get dinner on the table. Not another tool for people who already love cooking and want more of it. A tool for the people cooking intimidates.

How We Built It

I partnered with a PM and two engineers to take the product from zero to a functional MVP in under three months, shipping multiple iterations per week. Vercel for deployment, PostHog for real-time analytics, a modern serverless stack designed for experimentation.

As founding designer, I owned the end-to-end product experience: problem framing, concept validation, interaction design, AI workflow definition, and system-level UX decisions. I worked closely with engineering to translate complex AI behavior into clear, human-centered experiences, and introduced AI-assisted design workflows to accelerate iteration without sacrificing quality.

What I Designed

Conversational Adaptation

Instead of static recipe cards, users ask natural language questions: "Make this dairy-free," "I only have 30 minutes," "What can I substitute for shallots?" The AI responds with an adapted recipe and explains the reasoning behind each change.

The key insight: transparency is the trust mechanism. Users accepted AI suggestions only when they could see the why. "Use coconut cream instead of heavy cream, and here's the reason" lands differently than a silent swap.

This directly influenced how I later designed the automation preview feature at Keela. Same principle: make system logic visible to build confidence.

Making Cooking Followable

A lot of cooking inspiration doesn't start as a recipe card, it starts as a video: a TikTok, a YouTube tutorial, a reel someone sent you. That's genuinely great inspiration and a bad tool. You end up cooking with wet hands, scrubbing a video back ten seconds because you missed whether it was a teaspoon or a tablespoon.

Paste the link and it gets restructured into something you can follow standing up: ingredients on one side, steps on the other, a time breakdown up front so you know what you're committing to before you start. That's the roadmap, not a metaphor, an actual sequence with a beginning, middle, and end, instead of a video you have to keep replaying.

Step-by-Step Cooking Mode

A focused view that breaks recipes into discrete steps with timing. Each step shows only what you need right now: ingredients, technique, timer. Designed for the reality of cooking: messy hands, divided attention, a phone propped on the counter.

Instacart Integration

The gap I kept seeing wasn't in the recipe, it was after it: someone gets excited about a dish, then the plan dies at "do I even have this stuff." So a recipe's ingredient list sends straight to an Instacart cart, no re-typing, no separate shopping list. It's a small piece of interaction design, one button and a confirmation state, but it closes the loop between "I want to make this" and "I have everything I need to make this," which is the actual moment recipe apps lose people.

It also reframed how I thought about the rest of the product. Once grocery fulfillment was one tap away, ingredient substitutions and portion adjustments stopped being purely creative decisions and started being commerce decisions too: what's in stock, what's the better swap for both taste and cart availability. That's a different design problem than the one I started with, and a smaller integration taught me more about it than I expected.

It mattered most for the cooks who needed it most. Someone who cooks often can eyeball a recipe and know what's already in the kitchen. Someone newer to cooking usually can't, and finding out mid-recipe that you're missing something is a common place the whole attempt gets abandoned. Closing the gap between recipe and cart wasn't really about convenience for people who already cook with confidence. It was about not losing the person the product was supposed to be for in the first place.

Shareable Collections

Users curate recipe collections and share them as link-based cookbooks. The experience should feel personal, like passing a handwritten recipe card to a friend, not forwarding a URL.

What Happened

• Zero to functional MVP in under three months, shipping multiple iterations per week
• Reddit post reached 120K+ people, driving early organic validation

Building end-to-end keeps me honest. I know what API latency feels like, what error states look like in reality (not just in a Figma frame), and where the gap between design intent and engineering reality tends to widen. That empathy makes me a better design leader.