Hi HN, I’m Namanyay from Gigacatalyst (link: https://gigacatalyst.com/). Gigacatalyst allows sales, CS, and users to build one-off features, so your SaaS can support long-tail customer workflows and engineers aren’t pulled away from the roadmap.

When you sell software to large businesses, you realize that each customer needs their own workflow and features. Traditionally, this either means long engineering roadmaps or the customers end up using workarounds.

But what if everyone could build their critical missing features just by talking to an AI? That’s what we do at Gigacatalyst. We provide an AI customization layer for your customers, CS team, and sales team to build these missing critical workflows without needing any engineers at all. Think Lovable, but built on top of YOUR platform.

We connect to your product's APIs, learn your data model and design system, and let non-technical users build governed apps via natural language - inside your product, under your brand.

Here’s what it looks like in action: https://www.youtube.com/watch?v=_taSpSphH6E

One of our customers, a Series B company, saw their users (not engineers - managers, ops people, facility directors) build critical workflows like:

- Parts stockout prevention: A maintenance manager typed "show me which parts will run out in the next 2 weeks based on usage over the last 90 days, accounting for vendor lead times." The app tracks consumption velocity, forecasts stockouts, and alerts before it's too late. He says it's prevented ~$500K in emergency downtime.

- Invoice OCR from phone photos: Technicians kept losing paper invoices. The prompt: "upload a photo of the invoice, extract vendor name, date, amount, and line items, then match it to the purchase order and flag discrepancies." Now techs snap a photo on-site to automatically add to the system of record.

- Restaurant emergency triage: A pizza chain's facilities manager was drowning in maintenance requests. He built a priority matrix: "walk-in freezer not cooling" auto-routes as CRITICAL, "dining room light flickering" goes to LOW. He's now able to manage backlogs with the correct priority.

How Gigacatalyst works under the hood:

1. Agentic API discovery: Our agents go through your app and parse your endpoints, query params, request/response shapes, and sample data to build the base layer.

2. Generation and Validation: When a user describes what they want our AI generates an app. We set up multiple validation steps, including static checks, runtime error analysis, and LLM-as-a-judge.

3. Sandboxing and Compilation: We wrote our own compilation and sandboxing framework to get the fastest speeds and lowest costs. This means that users can interact with the built app in seconds.

4. Proxy layer: We create a proxy layer for all APIs to handle auth, tenant isolation, and rate limiting. Everything the agent has access to is controlled, logged, observed, and version controlled.

After 2000+ daily users, 900+ apps built, and 70% 30-day retention, today we're opening a public demo.

Try it: https://app.gigacatalyst.com/ - enter your SaaS product's API URL (or just the homepage) and start prompting.

If you're serving a variety of use cases, you probably deal with a lot of custom requests and Gigacatalyst will save you time and increase your bottom line. Book a meeting at https://gigacatalyst.com/#contact and I'll help your team and customers build new functionality on top of your platform.

I've been reading Hacker News since I was 12 years old. I'm proud to launch for all of you and I want to hear your feedback on my product and comments!

I agree that is part of the future of AI, you describe what kind of UI you need and the app generates a combination of UI components to match your needs.

I expect this to be a simple NLP mapping via local/cloud AI model to a JSON/DSL that describes the interface. Naturally, this won't work well for complex UI that are context aware but will do well for most simple apps/dashboards.

My question is: How do you plan to make this a business?

This is such an important step forward as we start to understand the 2nd order implications of AI and how it will change UIs in the future.

We used to have to allow + review 3rd party plugins for software so that people could customize it, but when the cost of development is near-0, we can simply hand over the development reigns to customers.

i love this because it seems like you've turned vibecoding up to 11 unleashing the non-technicals in your org to ship vibecode slop straight to prod. it's an idea so obviously terrible to most engineers that maybe it's actually really really smart. much bolder than yet another AI-driven dashboarding tool or smart notebook.

to address the elephant in the room... how do you think about technical debt incurred by users who likely do not understand the underlying data models, consider auth, etc?