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.
Fixed monthly rate. Predictable invoice.
Flat monthly rate for the whole delivery unit, anchored to throughput, not hours. No change orders for routine work.
First pod on a new strategic relationship.
Default rate for a mature pod engagement.
New accounts, complex scope, or engagements needing additional Orchestrator/Architect load.
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?
Start with a single-outcome trial, or set up a 30-minute conversation about whether the pod model fits your engagement. We won't bring a deck.