Case study
Prompt control, for people who don't code
A content management system for organizing, versioning, and managing AI prompts across your projects.
Done-For-You
AI Feature Delivery
A turnkey service designed to help SaaS engineering leaders ship production-ready AI features in 8 weeks without derailing their core roadmap.
SaaS products and tools we've built...
Here's what we've shipped...
Three SaaS products that we've taken from idea to launch— two with AI at their core, that taught us how to ship AI features that actually work in production
Case study
Turn your prompts into tools with Daisy Chain
An AI workflow platform that transforms complex prompt chains into reusable, shareable tools without requiring code.
Case study
Over 2 million Tweets published with Chirr App
A Twitter scheduling tool that publishes long-form content as Twitter threads.
Date-fns is also the second most popular JavaScript utility library in the world, according to the 2024 State of JS report.
The problem
Why building AI features in-house falls short.
You're watching competitors roll out AI features while your team is stuck in development hell.
Maybe you've already tried building AI in-house. Your engineers spent months wrestling with prompt engineering, evaluation frameworks, and edge cases. What seemed simple in a demo became unreliable in production. Quality was inconsistent. Hallucinations slipped through. The timeline ballooned from 6 weeks to 6 months, and you're still not confident shipping it.
- Competitive Pressure. Or maybe you haven't started yet, but you're losing deals. Prospects are choosing competitors with "AI-powered" features. Your sales team is asking when you'll have something to show. Your board is asking why you're behind.
- Resource Drain. The problem isn't that your team isn't capable—it's that building production-ready AI features requires specialized knowledge your team shouldn't have to learn through expensive trial-and-error. Hiring an AI engineer takes months and costs $200k+ annually. Agencies deliver prototypes that break under real-world use. Your existing team is already underwater with the core product roadmap.
- Widening Gap. Every week you wait, the gap widens. Every lost deal stings a little more. And the worst part? You know AI features aren't rocket science—plenty of companies are shipping them. But the gap between a working demo and production-ready code feels impossibly wide.
A better solution for founders and engineering leaders who need to ship fast.
We build custom AI features in TypeScript with evaluation and quality assurance built-in from day one, delivering production-ready code in 8 weeks.
What makes this different: our MCP-first validation approach. Instead of building everything and hoping it works, we develop MCP (Model Context Protocol) adapters that let you test the AI functionality directly in Claude or ChatGPT while we're still building. You're hands-on with the feature's core behavior by week 2-3, catching edge cases and refining outputs before a single line of UI code is written.
This is the difference between 8 weeks with confidence and 6+ months of trial-and-error.
You get production-ready TypeScript code, comprehensive test suites, evaluation frameworks, complete documentation, and team training. Your engineers understand how it works, how to maintain it, and how to measure quality over time. No black boxes. No vendor lock-in. Just solid code your team can own and extend.
Who is this for?
This service is for founders and engineering leaders at Series A-B B2B SaaS companies who need to ship competitive AI features without hiring a specialized AI team or derailing their existing roadmap.
You're likely:
- Losing deals to competitors with AI capabilities
- Under pressure from sales or board to ship AI features
- Concerned about quality and reliability based on what you've seen from in-house attempts or agency work
- Looking for a partner who understands production engineering, not just AI demos
- Working with TypeScript/JavaScript codebases and want to keep everything in the same stack
So how does this work? - We work in fixed 8-week sprints to ship on a predictable timeline.
You tell us your vision, we shape the scope to fit what can be done in 8 weeks. — which means you get a complete, production-ready version of your product in exactly 8 weeks.
- Weeks 1-2: Discovery & Architecture. We map out your requirements, define success criteria, and design the technical architecture. By the end of week 2, you'll have a clear spec and we'll begin building the MCP adapter that lets you test the AI functionality directly.
- Weeks 3-6: Development & Testing. You're hands-on with the MCP adapter by week 3, testing the AI behavior in Claude or ChatGPT while we build the production code. We refine prompts, catch edge cases, and develop comprehensive evaluation frameworks—all before writing UI code.
- Weeks 7-8: Production Integration & Handover. We integrate the feature into your codebase, complete final testing, and hand over everything: production-ready code, test suites, evaluation reports, full documentation, and team training so your engineers can maintain and extend it.
- What You Get. Production-ready source code, comprehensive test suites, AI evaluation reports with custom grading criteria, full documentation, and deployment configs. You own everything we build.
How much will it cost?
Answer 4 quick questions to get an estimate of how much your product will cost to build in one 8-week sprint.
Frequently asked questions
How much will it cost?
Answer 4 quick questions to get an estimate of how much your product will cost to build in one 8-week sprint.