AI-Native Pod. Engineers Accelerated by AI.

Both are managed development teams that build your product end-to-end. The differences are structural. An AI-Native Pod is built around an AI toolchain (Cursor, Claude Code, Copilot) from day one. It's smaller — senior-heavy composition vs a typical mixed-seniority Dedicated Team — and operates with explicit delivery metrics tracking AI impact. A standard Dedicated Team uses AI tooling but isn't structurally designed around it. Choose AI-Native Pod when you want maximum velocity with smaller headcount and you trust senior judgment. Choose Dedicated Team when you need broader role coverage (PMs, designers, multiple specialists) and longer-term capacity stability.

Team Augmentation adds individual engineers to your existing team. You manage them, set their tasks, integrate them into your sprints. An AI-Native Pod is a managed unit — we run the pod, deliver outcomes, you set direction. Augmentation is "we send you engineers who happen to use Copilot." Pod is "we deliver your product with a senior-led AI-augmented team." Augmentation is faster to start with shorter commitments. Pod works on outcome-based scopes.

Standard toolchain: Cursor (development IDE with AI), Claude Code (multi-file refactoring and complex tasks), GitHub Copilot (inline suggestions). We can also integrate your existing AI tools if you have preferences or compliance requirements. For projects where it fits, we can layer in our proprietary AI products (KnowHub for knowledge access, SmartSearch for semantic search, InProduct for in-codebase context), available as separate engagements.

Three layers. First, AI-generated code is flagged in pull requests — senior engineers review before merge. Second, automated security scanning runs on every push (SAST, dependency scanning, secret detection). Third, we document AI usage in PRs for a full audit trail. AI accelerates execution; humans still own architecture, security, and edge case decisions. We treat AI like a fast junior engineer: useful, but never trusted unilaterally.

Industry research shows AI-augmented developers ship measurably faster on routine work: code scaffolding, test writing, documentation, refactoring. Complex architectural and product decisions don't accelerate the same way (and shouldn't). In practice, AI-Native Pods ship more per sprint than equivalent traditional teams, with comparable code quality. We report delivery metrics monthly so you see the impact in your numbers, not in our marketing claims.

Yes, if your use case fits. KnowHub (knowledge access), SmartSearch (semantic search), and InProduct (in-codebase context) are our products, available as separate engagements. They can be integrated into the pod's workflow if it accelerates delivery, but they're not bundled by default. We assess fit during the discovery call and propose them only if they add value for your specific use case.

Yes. This is the most common setup. AI-Native Pods often work alongside an in-house engineering team, owning specific workstreams (new product line, AI feature integration, performance refactor) while in-house engineers own the core platform. We integrate with your code review process, sprint cadence, and tooling. The pod is a delivery unit, not a parallel organization.

Build in-house when your core engineering culture already adopts AI tools, you have senior engineering leadership comfortable selecting and integrating tools (Cursor, Copilot, custom RAG systems), and your roadmap allows several months of internal experimentation. Choose an AI-Native Pod when you want operational AI workflow on day one, you need measurable productivity now, or you're piloting AI-augmented delivery as a contained experiment before rolling it out internally. Many clients use the pod to learn what works, then adopt similar workflows internally.

Yes. Startup House is ISO 27001 certified. Security and IP protection apply from day one. NDAs are standard. Data handling is compliant with GDPR. All code, documentation, and product artifacts belong to you — no vendor lock-in.