AI Pods — Production-Ready Teams, Not Resumes
An AI Pod is a pre-assembled engineering team — solution architect, tech lead, PM, developers, QA, and a custom agentic layer — ready to ship to production from sprint one. No recruiting cycle, no ramp-up tax.
What you get
When it fits
- You need a team shipping in weeks, not a hiring pipeline running for months
- The work is well-bounded enough to scope but rich enough to need a real team, not a single contractor
- You want one accountable team with clear ownership, not a panel of agencies
- You're willing to pay for outcomes — and want a partner willing to be paid that way
When it doesn't
- You only need one engineer for one specific gap — staff augmentation is a better fit
- Scope is genuinely undefined and the work is pure research — discovery first, then a pod
- You need a managed service with a permanent SLA — pods are project teams, not call centers
Process
Day 1: discovery call. Days 2–4: scope and team-shape analysis. Days 5–7: pod assembled and onboarded. Week 2: first production delivery. Pods run on quarterly cycles with clear outcome metrics; you can scale up, scale down, or wind down between cycles. Exit includes full IP transfer, agent configuration, and RAG knowledge base — no lock-in.
Full delivery processPricing
Outcome-based pricing on most pods (no token counting, no surprise bills). Fixed-scope, fixed-price for well-bounded engagements. Low-risk entry points: QA Bot Pod (90 days), Documentation Pod (4 weeks), LLM Integration Sprint (2 weeks), Architecture Review (audit + remediation roadmap).
See engagement modelsCase studies
FinTech Mobile Banking Platform
Secure, AI-powered mobile banking serving 500K+ users with instant transfers and biometric authentication.
Multi-Vendor E-Commerce Platform
Scalable marketplace processing $10M+ monthly with AI recommendations and real-time inventory management.
AI-Powered Applicant Tracking System
Comprehensive ATS solution with AI-driven candidate matching, automated resume parsing, and real-time recruiter-candidate communication serving 10K+ monthly candidates.
FAQ
- What sizes do pods come in?
- Most pods run 4–7 core members depending on scope. A typical Build Pod is solution architect + tech lead + PM + 2–3 developers + QA, augmented with AI agents for code, test, and documentation. We'll size the pod to the work in discovery, not to a fixed seat count.
- What's a 'Custom AI Pod'?
- A pod with an additional agentic layer specific to your workflow — orchestration code, a RAG knowledge base built on your docs, and internal tooling that lives with the team. When the engagement ends, you keep the agents, the RAG store, and the configuration. No lock-in.
- How is this different from staff augmentation?
- Staff aug is one or more engineers integrated into your team and your management. A pod is a self-contained team with its own architect, PM, and QA — accountable for an outcome, not a list of tasks. We offer both, and we'll tell you which fits during the scoping call.
- Do you really transfer 100% of the IP?
- Yes — code, agent configuration, prompts, RAG knowledge bases, runbooks, and decision logs. Lock-in is a vendor strategy, not an engineering one. If we can't earn the next quarter's work on merit, we don't deserve it.
- What are the 'low-risk entry points'?
- Short, fixed-scope engagements designed to test fit before committing to a full pod: a 2-week LLM integration sprint, a 4-week documentation pod, a 90-day QA bot pod, or a one-shot architecture review with remediation roadmap. Most clients start with one of these.