Hire AI Pods

Launch smarter and ship faster with AI-native product teams that handle everything from research and prototyping to full-scale deployment.

AI Pod team collaboration

Building AI capabilities from scratch takes months you don’t have. Hiring AI engineers one by one? Even slower. By the time you’ve assembled a team, your competitors have already shipped.

AI Pods are multidisciplinary teams of 3+ AI-native experts who know how to build with and for AI. No interviews, no hiring cycles, no ramp-up time. We assemble the pod based on your exact requirements, and they start delivering within days.

The difference between AI Pods and traditional hiring isn’t just speed—it’s momentum. When you hire individual AI engineers, you’re betting that they’ll gel as a team, establish workflows, and figure out production deployment together. Most don’t. AI Pods have already solved those problems. They’ve shipped AI products together, debugged model hallucinations at 2am together, and optimized inference costs together. You’re not paying for team formation; you’re paying for a team that already works.

 

AI Pod team collaboration

This matters most when you’re racing against a market window. Your competitors aren’t waiting for you to finish hiring. They’re shipping features, capturing users, and iterating based on real feedback. AI Pods compress the timeline from “we need AI capabilities” to “we’re in production” from quarters to weeks. You skip the false starts, the misaligned hires, and the expensive mistakes that come from assembling an AI team from scratch. You get experts who’ve made those mistakes on someone else’s dime and learned how to avoid them.

What’s in an AI Pod?

Every pod is custom-built for your project, but typically includes:

  • AI/ML Engineers — Model development, fine-tuning, prompt engineering, RAG systems
  • Full-Stack Developers — Application architecture, API integration, deployment infrastructure
  • Product Designers — AI UX patterns, interaction design, user research for AI products
  • Data Engineers — Pipeline architecture, data quality, vector databases, embedding systems
  • DevOps/MLOps — Model deployment, monitoring, scaling, CI/CD for AI workflows

We don’t force a fixed team structure. You tell us what you’re building, and we configure the pod to match your technical needs and project phase.

How It Works

  1. Tell us what you’re building — Share your AI project goals, timeline, and technical requirements in a 30-minute consultation.

  2. We configure your pod — Within 48 hours, we present a custom team composition with specific experts matched to your needs.

  3. Pod starts shipping — Your team is onboarded and writing code within 1 week. No hiring, no screening, no wasted time.

  4. Scale as needed — Add specialists for new features, adjust team composition as priorities shift, or scale down after launch.

Why AI Pods Beat Hiring

Speed to market — Most companies spend 3-6 months hiring an AI team. Pods start in days, not months.

Pre-integrated teams — These aren’t random contractors. Pods work together regularly and know how to ship AI products fast.

No hiring risk — Skip the uncertainty of hiring AI talent who might not work out. If the fit isn’t right, we adjust immediately.

Flexible engagement — Need a pod for a 3-month MVP sprint? Done. Want to keep them long-term? Also done. No pressure to commit beyond your current runway.

Depth of AI expertise — Every pod member has shipped real AI products. They’ve solved the problems you’re about to encounter.

Frequently Asked Questions

How many people are in a pod?

Minimum 3, but most pods range from 3-6 people depending on project complexity. We recommend starting with a core pod and scaling up as needed rather than over-building from day one.

What's the minimum commitment?

3 months. Most clients stay 6-12 months to go from prototype to production-ready product. You can adjust team composition throughout the engagement as priorities change.

What types of AI projects do pods handle?

RAG systems, chatbots, AI-powered SaaS features, recommendation engines, computer vision applications, custom LLM integrations, AI automation workflows, predictive analytics, and more. If it involves AI/ML in production, we've built it.

Who owns the code and IP?

You do. 100%. All work product, models, code, data pipelines, and IP belong to you. Every pod member signs NDAs and IP assignment agreements before touching your project.

How is the pod managed?

You work directly with the pod as if they're your in-house team. Daily standups, sprint planning, code reviews—everything happens in your workflow. We provide an account manager for escalations and team adjustments, but the pod operates as an extension of your team.

How is pricing structured?

Fixed monthly rate per pod, not per hour. Pricing depends on pod size and expertise level. We'll provide a quote after understanding your project requirements. No surprise billing, no hidden fees.