Build model
Fixed scope
build and run
AI delivery pod: production in 6 weeks
One use case, one KPI written down before any code, live on real records inside 30 days and measured against that number by week 6 — then the next use case gets scoped. The pod is a principal engineer plus Claude-based agents doing the repetitive build work, quoted as a fixed number against our published $6–20k band, never billed by the hour.
Timeline
2–8 weeks
Runtime
Monitored
Handover
Owned by your team
Delivery blueprint
Visual summary of what this build includes from quote to handover.
Scope
Written
Deliverables and acceptance checks are fixed before build.
Execution
Instrumented
Retries, logs, and alerts are configured for production.
Controls
Approved
High-risk actions are gated by named human approvers.
Transfer
Owned
Runbook and account ownership move to your team at launch.
The pilot works. It just never ships.
Most AI work stops one step short of the business. The demo answers correctly in a sandbox, everyone nods, and then it sits there — because nobody named an owner, nobody agreed which number it had to move, and it was never wired into the system where the work actually happens. MIT’s 2025 GenAI Divide study put the share of enterprise AI pilots with no measurable P&L impact at roughly 95%.
The second failure is capacity. Your engineers already have a roadmap, and “add AI” lands on top of it as something to squeeze in between releases. Six months later the pilot is still a pilot, two model generations have passed, and the internal case for spending more has quietly died.
What a pod actually is
A KPI agreed before any code
Week zero produces one sentence both sides sign: the number this build has to move, how it is measured, and what counts as done. It goes on a dashboard fed by Postgres on day one, so the week-6 result is a reading, not an opinion.
Grafana · Postgres · n8n
One principal engineer, no rotating bench
The person who scopes the work builds it and hands it over. Claude-based agents write the boilerplate, tests, and evals; the engineer owns every decision about data, guardrails, and what is allowed to go live.
Claude · n8n · Docker
Shipped into the real workflow
Launch means your team using it on live records — wired into HubSpot, QuickBooks, Slack, or whatever runs the process, with permissions, error handling, and a rollback path agreed before switch-on.
HubSpot · QuickBooks · Slack
ROI math
20 hrs/wk of manual process work × $40/hr × 52 = $41,600/yr recovered — typical pod use case $6–20k.
Hour counts are medians for ops teams this size, typical for this type of automation — the free audit replaces them with the real numbers from your process, and the KPI is set from those.
How this ships
Week 0 · 3 days
Audit
Use case picked from your process list, data access checked, KPI and acceptance test written, fixed quote issued — or a walk-away if the math doesn’t work
Week 1
Blueprint
Architecture and integration map, guardrails and approval gates, rollback plan, and the monthly tool-cost forecast — signed off before build starts
Weeks 2-4
Build
Working system in production on real records by day 30, with retries, alerting, and evals wired in from the first run
FAQ
What does an AI delivery pod cost?
Each use case is a fixed number quoted against the published bands: a single-workflow case runs $2–6k, a multi-system one $6–20k. No hourly billing, and no discovery invoice — the audit that produces the quote is free. After launch you pay tool and model costs, typically $40–180/mo, plus an optional care plan from $500/mo.
How is this different from an agency team?
You meet the engineer who builds it, and that doesn’t change mid-project. There is no account-manager layer, no juniors learning on your budget, and no bench to keep busy — which is also why we take on three builds a month and will tell you straight when the next slot opens.
What if the KPI doesn’t move?
Two things are guaranteed: the first automation is live in production within 30 days of kickoff or the build isn’t billed, and the KPI is measured and reported honestly — including when the reading disappoints. The business outcome itself isn’t guaranteed, because it depends on your volumes and your team. If the week-0 math says automation won’t pay off, we say so before you spend anything.
Which use case goes first?
The one with the most repetition, the cleanest data access, and an owner who wants it. The free audit inventories your recurring tasks and puts a payback number on each; the first sprint takes the top of that list, not the most interesting AI problem.
Get a fixed quote for a 6-week pod sprint
Fill this quick form and we’ll send a fixed written quote for this exact build.