No layers. No noise.
No sales team. No account managers. No junior devs learning on your project. When you hire Kaydenlabs, you're talking to the people who write the code.
Kaydenlabs
AI Engineering Studio
We've shipped 13+ AI products across fintech, real estate, social media, healthcare, estate administration, astrology, and tax automation. We built Hermes — a multi-agent AI system with four autonomous personas that manages revenue operations across eight products simultaneously. What drives us is the gap between AI demos and production reality. Most AI looks impressive in a pitch. We build the version that works at 3 AM with real data and edge cases.
Expertise
AI / ML
- LLM Integration (Claude, Gemini, OpenAI)
- Multi-Agent Systems & Orchestration
- RAG Pipelines & Vector Search
- ML Model Training & Evaluation
- AI Eval Frameworks
- Prompt Engineering & Tool-Calling
Frontend
- React 18/19 & Next.js 15/16
- React Native & Expo
- Tailwind CSS & Design Systems
- Framer Motion Animations
- PWA Development
- Responsive & Mobile-First Design
Backend
- Node.js & Python
- Supabase (PostgreSQL, Auth, RLS, Edge Functions)
- Stripe (Subscriptions, Payments, Webhooks)
- REST & Real-Time APIs
- Cron Jobs & Background Processing
- PDF Generation & OCR
Infrastructure
- Vercel Deployment & Analytics
- VPS Management (PM2, SSH)
- CI/CD Pipelines
- Monitoring (Sentry, PostHog, Umami)
- Security (AES-256 Encryption, HMAC Auth)
- Playwright & Automated Testing
The Story
Kaydenlabs started with a pattern we kept seeing: companies spending six figures on AI projects that never made it past the proof-of-concept stage. The demos were impressive. The slide decks were polished. And when it came time to put the AI in front of real users with real data, everything fell apart.
The problem was never the AI. It was that the teams building it had never shipped AI into production before. They knew how to make a model work in a notebook. They didn't know how to make it work when a user misspells their name, uploads a corrupted PDF, or asks a question the training data never covered.
We've shipped AI into production 13 times. We know where it breaks. We know what users actually do with it (as opposed to what they say they'll do). And we know that the hardest part isn't getting AI to work — it's keeping it working. That's what Kaydenlabs is built around: not the demo, but the day after the demo, and every day after that.