The SpaceXTech AI Factory
A repeatable system that turns your backlog of AI ideas into governed, accepted products. Same path, same gates, same evidence every time, whoever does the building.
One path for every AI product
Discovery is time-boxed. The committed delivery clock starts only when the work order is accepted, and it pauses for any delay on your side that we log.
Discover & size
Frame the problem and score readiness, size and risk.
Work order acceptedProve value
Test the idea on real data before full spend.
Go / no-goDesign & build
Short sprints with working demos.
Design approvedIntegrate & test
Systems, model and security tests, then user acceptance.
Acceptance signedLaunch & hand over
Controlled release with monitoring in place.
Formal acceptanceWhat happens, what you get, who signs off
Activities
- Frame the problem and success measure with the business owner
- Profile the data and score how ready it is
- Run a security and AI risk check
- Score the six sizing measures
Outputs
A use-case card, readiness score, size band and written acceptance criteria.
Activities
- Used for medium and large products
- Build a thin slice on your real data
- Test the outcome hypothesis before the full build
- A "no-go" stops spend early, by design
Outputs
An evidence pack and a clear go or no-go recommendation.
Activities
- Requirements, design and data contracts
- Pipelines, features, models and agents, each with a written charter
- Two-week sprints with working demos
- All code lives in your repositories
Outputs
An approved design, working increments and reusable assets added to your library.
Activities
- Connect to your systems; operational systems are read-first
- Integration tests and data reconciliation
- Model, bias, explainability and security tests
- User acceptance and a rollback rehearsal
Outputs
Test and model-validation evidence, plus a signed acceptance record.
Activities
- Controlled production release
- Drift thresholds and retraining set up with operations
- Knowledge-transfer session with your team
- Reviews at 30 and 90 days
Outputs
A live product, a full handover pack and the start of the warranty period.
Every product ships with
Six measures set every product's size and time box
Size isn't a negotiation. Score each measure from 1 to 3 and the total sets the delivery band. Try it with an idea you have in mind.
Worked example: equipment maintenance alerts
Sensor and maintenance data from two systems, a new pipeline, a multi-feature model scoring in near real-time, no agent actions, and sign-off from IT and the operations team.
two systems
new pipeline
multi-feature ML
near real-time
none
IT + operations
≤ 16 weeks
Agent autonomy is set per product, and signed off by you
Every agent gets a level on the autonomy ladder before it's built. Moving up a level is a decision, never a drift.
Agents are tested before release for accuracy, prompt injection, data leakage, unsafe tool use and recovery from partial failure.
Teams planned at 80%, so committed dates hold
A 20% reserve absorbs the usual surprises, like data cleanup and slow access requests, without moving deadlines. Unused reserve goes to the next product.
- Start with a few standing teams and add more as demand grows
- Add a team when the reserve is used for two months running, or large products start to queue
- Prepare the next wave while the current one builds
Every team, every sprint
The reserve is the buffer. It's what keeps dates honest.
Capacity grows with demand
Your team can run it without us
The AI Factory is designed so your people take over, step by step, and prove it by doing the work themselves.
Operate
Your team monitors products, runs a scheduled retrain and redeploys in test.
Extend
They change a pipeline, update a model and size a new use case.
Own
They deliver a small product end to end and govern work from any supplier.