Our Design Partner Program: AI Infrastructure Startup Seeking Co-Creators

I want to be upfront about something: LoomStack is an early-stage AI infrastructure startup. We're pre-launch. The product is being built. And that's exactly why I'm writing this — to introduce our design partner program and who it's for.

Most companies announce design partner programs as a polite way of saying “we need beta testers.” That's not what this is. Beta testers validate something that already exists. We're looking for something different — engineering teams willing to co-create a new category of infrastructure with us. Teams who feel a specific pain deeply enough that they want to help shape the solution, not just evaluate one.

If you've read our writing on the coordination bottleneck, coordination debt in AI engineering, or orchestration infrastructure, you know the problem we're obsessed with. This post is about who we want to solve it with — and what that relationship looks like.

Why a Design Partner Program for AI Infrastructure (Not a Beta)

Category creation doesn't happen in isolation. Every infrastructure layer that became foundational — Git, Kubernetes, Datadog — was shaped by deep collaboration between builders and early users. Stripe spent two years with their first 50 customers, personally installing their API on users' laptops. Temporal called their first Cloud customers “design partners” explicitly, running production workloads with manual operations because the feedback loop was more valuable than waiting for automation.

“A true design partner program is not about early selling. It's about building with your customers for the long term.”

— Logan Randolph, Sierra AI

We're building AI-native SDLC orchestration — infrastructure that coordinates how AI agents, human engineers, and organizational processes work together across the software development lifecycle. This isn't a well-understood product category yet. There's no existing market we can copy. The teams who adopt this earliest will literally define what it means and how it works.

That's not a role for passive users. That's a role for partners.

What We're Building

I'll keep this brief because we've written extensively about the problem elsewhere. Here's the core thesis:

AI coding tools have made individual developers dramatically faster. But organizational throughput hasn't kept pace. Code output is up 3–5x, but delivery speed is flat — or worse. The bottleneck has shifted from writing code to coordinating everything around it: reviews, context management, architectural consistency, governance, and multi-tool orchestration.

LoomStack is the coordination layer. We sit between your AI tools and your engineering processes — orchestrating workflows, enforcing governance, distributing context, and making sure your team's collective AI usage creates compounding value instead of compounding chaos.

Think of it this way: Kubernetes didn't replace Docker. It made Docker useful at scale. We're doing the same thing for AI engineering tools — making Cursor, Copilot, Claude Code, and autonomous agents useful at organizational scale, not just individual scale.

Who We're Looking For

Not every team is the right fit for a design partnership. We're not looking for the biggest logos or the most funding. We're looking for teams where the pain is real, the culture is right, and the willingness to co-create is genuine.

The Ideal Partner Profile

The companies we're most excited about are those where someone has already tried to solve this problem internally — built custom .cursorrules distribution, created AI review guidelines, hired an “AI enablement” role — and hit the limits of what ad-hoc solutions can do. You understand the problem from lived experience, not theory.

“Earlyvangelists are a special breed of customers willing to take a risk on your startup's product. They can envision its potential to solve a critical and immediate problem — and they have the budget to purchase it.”

— Steve Blank, The Four Steps to the Epiphany

What Design Partners Get

Let me be concrete about what this relationship looks like from your side. This isn't a waitlist where you get access two months later. It's a genuine partnership:

Early access, before anyone else. You'll be using the platform while it's still being shaped. Features will be rough. Things will break. But you'll be months ahead of the market in understanding how to orchestrate AI engineering at scale.

Real roadmap influence. Not a suggestion box. Structured input into what gets built and in what order. Monthly roadmap sessions where your team's needs directly shape priority. When we build something because of your feedback, we'll tell you. When we decide not to, we'll explain why.

White-glove support. Dedicated engineering support. Direct access to me and the founding team. Shared Slack channel. If something breaks, we fix it together — often in real time. No support tickets, no escalation paths. You talk directly to the people building the product.

Founding-partner pricing. Design partners get pricing that reflects the risk they're taking by betting early. These rates lock in permanently — they don't expire after a promo period. The teams who helped build this should benefit from it indefinitely.

Custom integration work. We'll build integrations specific to your stack. Your CI/CD pipeline, your review tools, your specific AI tool configurations. Not generic connectors — solutions designed around how your team actually works.

What We Ask in Return

I want to be honest about what makes this different from just “getting early access.” Design partnerships require real commitment from both sides. Here's what we need from you:

Time and attention. A weekly 30-minute standup for 3–6 months. That's the core commitment. Some weeks we'll need more — onboarding, deeper product sessions, debugging together. But the baseline is 30 minutes a week of honest conversation about what's working and what isn't.

Access and transparency. We need to understand your actual workflows, not a sanitized version. That means access to your CI/CD systems, review processes, and deployment patterns. We need to see how your teams really work with AI tools — the messy reality, not the conference-talk version.

Candid feedback. “It's great!” is useless feedback. We need to hear what's confusing, what's broken, what you expected but didn't get, what you'd never use, and what's missing. The signal we're looking for is honesty, not politeness.

Willingness to share learnings. If the partnership works well, we'd love to tell that story together — a case study, a reference call, or a co-authored piece on what AI-native engineering coordination looks like in practice. This isn't required to participate, but it's part of what makes the best partnerships mutual.

A decision at the end. Design partnerships should end with a clear outcome: either you convert to a paying customer because the product delivers real value, or we part ways having learned something important. We don't want indefinite free access disguised as a partnership. That's not honest for either side.

How to Reach Out

If what I've described sounds like your team, I'd love to talk. We're selecting 5–8 design partners for our first cohort — small enough to go genuinely deep with each one, large enough to surface patterns that make the product better for everyone.

The best way to start a conversation is simple: reach out directly. Send me a message on LinkedIn or email abhishek@loomstack.co. Tell me about your team, what AI tools you're using, and what's breaking. No formal application process, no committee review. Just a conversation between engineers about a problem worth solving together.

A few things that help when reaching out:

• Your team size and how many are actively using AI coding tools
• Which tools you're using (Cursor, Copilot, Claude Code, agents, etc.)
• What coordination pain looks like for you specifically
• Any internal solutions you've already tried
• Who would champion this internally

Building AI Infrastructure for the Next Decade of Engineering

I'll close with why I think this matters beyond just our company.

Every major shift in how software gets built has produced a corresponding infrastructure layer. Version control. CI/CD. Container orchestration. Observability. Each one started as optional tooling for early movers and became non-negotiable infrastructure for everyone. Each one was shaped by deep collaboration between the teams who built it and the teams who used it first.

AI-native engineering is producing its own coordination crisis. As our research on multi-agent engineering patterns shows, the infrastructure layer that solves it will define how engineering organizations work for the next decade. That's what we're building — and we don't want to build it alone.

The best infrastructure isn't designed in a vacuum. It's forged in the gap between what teams need and what currently exists. Our design partners aren't early users. They're co-authors of what AI-native engineering coordination looks like in practice.

If you're feeling the pain we've described — if your teams are producing more code than ever but delivering at the same speed — if you've tried to solve this with internal tooling and hit the limits — we should talk.

Let's build this together.