Development
Delivery Models
2 senior operators and 5 AI agents own planning, design, build, and QA inside your repository. You stay the Product Owner and own the code from the first commit.
AI Pod is a dedicated software development team for products that already exist or are actively being built. It suits two types of buyers.
You run a live product or a small portfolio and need to keep it maintained and moving forward. You may not have an engineering team, or you rely on a fragile patchwork of freelancers.
AI Pod gives you a complete product team across PM, BA, design, development, and QA without hiring anyone, at a predictable flat cost you can scale or pause as revenue allows.
You have engineers, an established process, and more roadmap demand than your team can deliver. Or you are moving your SDLC toward AI and need to reduce the risk.
AI Pod plugs into your repository and CI as an extra delivery unit, increases throughput without adding headcount, and keeps ownership and production with your team.
Two senior operators own one side of delivery each and direct the AI agents working under them. A part-time Project Manager and on-demand DevOps support complete the pod.
Owns the architecture, build, and every line of code that ships. Directs the Dev Agent and QA Agent.
Owns requirements, product decisions, and feature definition. Directs the PM, BA, and Design Agents.
Tracks progress and reports to you, so you always know where things stand.
Steps in for infrastructure, deployment, and scaling when the work calls for it.
Adding an AI tool to a single stage only speeds up that stage and shifts the bottleneck to the next one. AI Pod runs the full cycle, from planning through release, so work keeps moving end to end instead of piling up at a single handoff.
Start with a technical session and a small pilot. Measure the results, then scale only if the model fits.
For a product company with an existing team and process, AI Pod works inside your repository. There is no parallel workflow and no handoff event. You provide business requirements, and the pod returns finished, accepted features ready to ship.
Product · Engineering · QA already in place, owning the core product.
Repository · CI · release process — your own environment.
Owns architecture, the build, and every line that ships.
Owns requirements, product decisions, and feature shape.
The pod works in your environment, so there is nothing to migrate from later.
Ownership and production stay with your team throughout the engagement.
You define scope and accept the work. The pod owns how it gets done.
Every task on your project runs through two loops before it’s considered done.
Start small and scale based on results. A short pilot proves the fit before any long-term commitment. Moving between stages doesn't create rework, because the codebase is documented from the first commit.
Start with a technical session and one well-bounded pilot on your product. We baseline the metrics, you see how the model fits, and you scale only if the results hold.
“Their team handled the challenge professionally and found appropriate workarounds.”
“The group goes above and beyond to accommodate our demands.”
“The overall service was exceptional.”
“Their communication, work ethic, and desire to give a positive outcome for their client are impressive.”
“They deliver what they promise, and I can’t say that about other companies I’ve worked with.”
Dedicated Team 2.0 is built for ongoing work on a product that already exists or is being built. If you're at a different stage, one of these fits better — and moving between them costs you nothing in rework, because the codebase is documented from the first commit.
A smaller idea that one senior engineer can carry across BA, design, development, and QA with AI agents on their own.
Explore →You want a brand-new product built from the ground up, with the vision but no software team of your own to build it.
Explore →Your plan, budget, and timeline are already set, and you just need our engineers working inside your own team.
Explore →No. AI Pod works in both situations. If you run a live product with no engineering team, or only a fragile patchwork of freelancers, the pod becomes your entire product team, spanning PM, BA, design, development, and QA.
If you already have engineers and an established SDLC, hire a dedicated team to plug into your repository and CI/CD pipeline as an additional delivery unit. Your team keeps ownership of the core product, while the pod takes on well-bounded work alongside them. The only role you always provide is a Product Owner.
You do, fully, from the first commit. AI Pod works inside your repository, not in a separate environment you have to migrate from later. There’s no handover event and no vendor lock-in.
The codebase is documented as it is built, so knowledge does not live in one person’s head. You own the code and IP outright, and you can bring the work in-house at any point.
This is earlier than this model was built for. Dedicated Team 2.0 is designed for ongoing work on a product that already exists or is actively being built, with a real roadmap, real bugs, and features to ship.
A raw, unvalidated idea needs discovery and validation first. If you are at that stage, we would start there instead. Moving between stages later doesn’t create rework because the codebase is documented from the first commit.
No. You stay the Product Owner. You decide what gets built, why it matters, what gets prioritized, and when work is accepted.
AI Pod owns how the work gets done: the architecture, build, quality, and delivery process. It does not take over product direction. This split is deliberate, because the model depends on a single, clear owner on your side to define the scope and sign off.
If you want to hand off product direction as well, this is not the right fit.
No. Staff augmentation gives you people by the hour and leaves process, coordination, and delivery risk on your side.
AI Pod is an owned-outcome team. 2 senior operators own delivery, direct the AI agents under them, and are accountable for what ships, all under your Product Owner. You are buying a working delivery unit and a standard workflow, not extra hands you have to manage yourself.
Before any pilot, we run a technical session, engineer-to-engineer. The goal is to map out how AI Pod will integrate with your repository, CI, release process, and ownership boundaries.
This removes the most common blocker: uncertainty around how an outside team will fit into an existing pipeline. You see the integration on paper first, then we prove it through a small, well-bounded pilot before anything scales. There’s no parallel process running on the side and no new handoff to manage later.
Production and your data stay with you. AI Pod is designed to work with synthetic data, keeping the pod outside your audit and compliance scope rather than expanding it.
When real access is required, we sign a BAA and operate within your controls. This matters most in healthtech and fintech, where PHI and PII cannot leave your environment.
The model is built so you can increase throughput without sacrificing control or expanding your compliance footprint.
Most pods can start within 2 weeks after the technical session, once repository access, CI permissions, and the pilot scope are agreed upon.
We start with one well-bounded pilot, baseline the metrics, and review the results before moving into ongoing work.
You can pause. The model runs on a flat monthly cadence, so you can scale up, scale down, or pause based on revenue, roadmap pressure, or team capacity. Because AI Pod works inside your repository and documents the codebase from the first commit, pausing doesn’t create a handover problem.
You see progress from 3 angles, so there’s never a guess about where things stand:
Yes. Compliance isn’t an afterthought, and your operations and legal team stay covered on three fronts:
The pod bills at one flat monthly rate, set before the work starts, so your spend is predictable from day one and doesn’t creep as the work goes on. Here’s what that rate covers and what moves it:
The pod is remote by default, but if a mix of remote and on-site suits how you work, we set it up that way. The operators can spend time at your office for kickoffs, planning, or stretches where being in the room speeds things up, then shift back to remote for focused delivery.
