A delivery unit that ships every week.
A small AI-leveraged delivery unit at a fixed monthly rate. You set the direction; we handle decomposition, build, shipping, and comms.
A new way to buy delivery.
You onboard a POD instead of hiring by the hour or locking scope up front and managing change orders through the rest of the delivery. It's a good fit for teams that want delivery as a managed service.
“A POD feels different by week two. There's less coordination, the engineers define the work items, and the PO stays on the business side. AI runs the whole loop, with a human judging every step of it.”
- A single AI-native unit: one Architect/Orchestrator, two Developers, one Product Owner/Consultant.
- Two parallel workstreams. A deliverable in your hands every 1–5 days.
- Fixed monthly rate. No hourly billing.
- Full-stack every cycle: frontend, backend, and tests in one vertical slice.
- Reporting from day one: what shipped, what's in flight, what's slowing it down.
How a POD is shaped.
Two sides, business and delivery, wired together by one architect and a shared AI toolchain. The shape stays the same at every scope.
One unit. Three roles.
A POD is built to run without project-management overhead on your side, and the roles are named for what they do.
Architect/Orchestrator
Owns system design and what ships. Decomposes work for AI leverage, judges output across both workstreams, and ships code alongside the Developers. Heavy engagement up front, steady-state once patterns settle, spikes when the work requires it. Sets the POD's ceiling on speed and quality.
Two Developers
Full-stack by default. A single Developer ships frontend, backend, and tests in the same cycle, operating AI agents end-to-end as one loop: generate, review, test, ship. No specialist handoffs. Both Developers hold both workstreams, so when priorities shift there is no ramp-up.
Product Owner / Consultant
Turns priorities into specs, runs acceptance, and manages release comms. That overhead stays with us. You set direction at the roadmap level; the PO carries it into the POD.
Spec to ship, repeated many times a month.
A POD runs a tight spec-to-ship cycle with two workstreams in parallel. When one waits, the other moves.
Spec
Priorities come in from you: a short brief, a Loom, a conversation captured as a note. The PO defines the business requirements and, together with the Architect and the Developers, turns them into BRDs (Business Requirement Documents) and implementation specs with clear acceptance criteria.
Decompose
The Architect breaks each spec into vertical slices. Each slice cuts through frontend, backend, and tests. Slice size is tuned for AI leverage: small enough to build in one focused session, large enough to be meaningful on its own.
Build
A Developer runs the end-to-end loop, spec-driven: code with AI agents, write and run tests, iterate, using the AI tooling and skills Aquiva has invested in. The Architect stays close: reviews as it ships, settles interpretation questions, holds architecture coherence across slices.
Check
Every commit runs through automated quality gates: PMD ruleset, AI code-review agents, unit and integration tests. The Architect reviews architecture; the PO accepts against the spec.
Ship
Slices reach your environment on a 1–5 day cadence. Demos run weekly at minimum, more often when there's something to show. The POD ships outside the quarterly release train.
What a POD delivers, and why a small unit can outpace a larger one.
The four mechanisms behind it.
Full-stack by design
One Developer ships in a single cycle what three specialists used to: backend, frontend, tests. The handoffs and the sprint-per-layer waterfall disappear. So does most of the sequencing tax.
Low overhead
A POD runs on roughly 7% coordination and ceremony by design; a traditional capacity team loses 15–25%. Hypersense's 2025 effort-allocation study puts project management alone at 10–20% of team effort, and an APQC survey found knowledge workers lose about a quarter of their time to productivity drains. The POD's effective throughput climbs before any AI leverage enters the math.
Sized for your feedback bandwidth
About two parallel streams is what a stakeholder can genuinely review and steer; past that, added capacity sits idle instead of producing output. The POD runs two workstreams, matched to the rate you can absorb and direct work.
AI leverage, end to end
AI agents handle every stage of the build: code generation, test authoring, documentation extraction, refactoring, integration stubs, data migrations. We pass every efficiency through as more shippable work at the same fixed rate.
Where a POD fits, and where it doesn’t.
A POD works best under specific conditions. We name them upfront so you can judge fit before you sign.
- Fast decisions: scope approval and acceptance within 24 hours
- Async-by-default communication; Slack or equivalent works
- Evaluation by outcome ("did it ship and work"), not hours logged
- Clear definition of done: acceptance criteria can be written
- Build-heavy scope: features, migrations, integrations, automations
- Modern environment with API access, sandboxes, CI/CD
- AI tools allowed on the codebase
- Timesheet-based evaluation or sprint ceremonies imposed on the POD
- Security policies that forbid AI tools on the codebase (hard disqualifier)
- Non-technical gatekeepers required for every change
- Pure maintenance or break-fix with no shippable outcomes
- Deep cross-team dependencies the POD cannot ship without
- Fixed-price waterfall SOWs with penalty clauses or hourly bidding
- Ship-features-on-top scope with no cleanup budget on an unstable codebase
Common questions
What is the Aquiva AI POD?
A managed delivery unit at a fixed monthly rate. One Architect/Orchestrator, two Developers, and one Product Owner, all AI-leveraged and engineered to outpace a much larger team. You set the direction; the POD handles decomposition, build, shipping, and comms.
How is an Aquiva AI POD composed?
A POD has two sides, business and delivery, connected by one Architect and a shared AI toolchain. The business side sets priorities and accepts work. The delivery side (Architect/Orchestrator + Developers) runs a tight spec-to-ship cycle with two parallel workstreams.
What types of work fit the AI POD model?
PODs work best for ongoing product development, feature backlogs, and iterative delivery where you need sustained throughput at a predictable cost. They are the wrong shape for one-off migrations, compliance audits, or engagements that need more than 10 people.
See a POD in action.
Set up a 30-minute conversation about whether the POD model fits your engagement.