A delivery unit designed to perform.
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 scoping by the milestone. Good for teams that want delivery as a managed service.
“A pod feels different by week two. Less coordination, more shipping. Once a team has worked this way they don't go back to scoping projects by the milestone.”
- An AI-native single unit: one Orchestrator/Architect, two Builders, one Product Owner/Consultant.
- Two parallel workstreams, load-balanced. 1–5 day ship cycles.
- Fixed monthly rate. Predictable invoice. No hourly billing.
- Full-stack shipping every cycle. Frontend, backend, tests, all in one vertical slice.
- Transparent 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. Same shape at every scope.
One unit. Three roles.
Built to run without project-management overhead on your side. Roles named for what they do.
Orchestrator/Architect
Owns system design and what ships. Decomposes work for AI leverage, judges output across both workstreams, and ships code alongside the Builders. Heavy engagement for the first ~90 days, then steady-state once patterns settle. Sets the pod's ceiling on speed and quality.
Two Builders
Full-stack by default. A single Builder 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 Builders hold both workstreams, so when priorities shift there is no ramp-up.
Product Owner / Consultant
Your interface to the pod. Turns priorities into specs, runs acceptance, manages release comms. Absorbs the project-management overhead that would otherwise fall on your side. 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 on a tight spec-to-ship cycle. Two workstreams in parallel, load-balanced. When one waits, the other moves.
Spec
Priorities come in from you; the PO writes them up as specs with clear acceptance criteria. A short brief, a Loom, a conversation captured as a note. Specs land sized so the next step starts within hours.
Decompose
The Orchestrator/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 Builder runs the end-to-end loop: code with AI agents, write and run tests, iterate. The Orchestrator/Architect stays close, reviewing as it ships, unblocking interpretation, and holding architecture coherence across slices.
Check
Every commit runs through automated quality gates: PMD ruleset, AI code-review agents, unit and integration tests. The Orchestrator/Architect reviews architecture; the PO accepts against the spec. Quality lives inside the loop.
Ship
Slices land in 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.
Four reasons the pod produces what a much larger team does.
Full-stack by design
One Builder 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 small, focused team loses ~7% to coordination and ceremony. A traditional capacity team loses ~15–25% (10–20% PM allocation per Hypersense's 2025 effort-allocation study; ~25% knowledge-worker productivity drains per APQC). The pod's effective throughput climbs before any AI leverage enters the math.
Sized for your feedback bandwidth
Most stakeholders can process feedback on ~2 parallel streams. Throwing more capacity at the problem produces idle capacity instead of output. The pod runs two workstreams, load-balanced, matching the rate you can absorb and direct work.
AI leverage, end to end
AI leverage runs the whole loop: 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. Faster delivery, same price.
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
Want to see a pod in action?
Set up a 30-minute conversation about whether the pod model fits your engagement. We won't bring a deck.