Most AI projects stall at the architecture meeting. Ours are in production by week eight.
Speed without shortcuts. Security without afterthoughts. This is the exact process behind every Stack Build engagement — documented, repeatable, and designed around the specific points where AI projects slow down.
Every Stack Build engagement follows the same four-phase structure. It exists because we have learned precisely where enterprise AI projects lose time — and we have engineered each of those bottlenecks out. The result is not luck. It is architecture.
Four phases. Every engagement.
No exceptions. No shortcuts. This is the structural reason we ship in eight weeks when others are still in planning.
Before a line of code is written, the build plan is locked. Data access is provisioned. Security architecture is defined. Story points are confirmed against technical reality — not scope assumptions. This is where most engagements are already two weeks behind us: they start building before they know what they are building.
- Production data access secured and environment provisioned
- Security and compliance architecture defined — data residency, access controls, audit trails
- Story points confirmed against actual technical constraints, not scope assumptions
- Integration dependencies mapped against your enterprise systems
Working software ships at the end of week four. Not a prototype. Not a wireframe. A functioning AI system running on your actual production data, deployed in your environment. Senior engineers own every sprint. No juniors on delivery, ever.
- Working AI system built on real production data
- Every sprint closes with deployed, testable software — not a Jira ticket
- Compliance controls embedded in the data layer from day one
- Weekly demo to a single stakeholder — no approval chains
Enterprise integrations are complex. This phase handles them — SAP, Salesforce, Microsoft, legacy systems — while a full security review runs in parallel. Not as a final gate. In parallel. Security findings get resolved before go-live, not after.
- Enterprise integrations completed and tested under production load
- Full security review running concurrent with delivery — not deferred to the end
- Performance tested against real usage patterns
- Documentation written by the engineers who built it
The system goes live in your environment. The IP transfers. The dependency on Stack10 ends — by design. You receive the codebase, architecture docs, and runbooks to operate and extend the system internally. No retainer required. No licensing fees. No handcuff.
- System deployed to your production environment
- Full IP transfer — codebase, architecture documentation, runbooks
- Handover session with your internal team: knowledge transfer, not just file transfer
- Optional: Stack Build continues into ongoing Product Development
It is not that other teams are slower. It is that they are structured for slowness.
Every bottleneck below is a structural property of how most AI engagements are run. The Stack Build Method was designed to remove them before a project starts.
Scope is defined after the project starts — often in week two or three, by which point assumptions are already baked into the estimate.
Story points are locked before day one. Every deliverable is agreed before a line of code is written. No scope surprises mid-engagement.
Junior engineers execute what seniors design — handoffs introduce errors, rework, and weeks of lost time at exactly the wrong moment.
Senior engineers only, from architecture to deployment. The person who designs the system is the person who ships it.
Security review is scheduled for the final sprint — findings at that stage can unravel weeks of delivered work and push go-live by months.
Security architecture is defined in week one and enforced in every sprint. Phase 3 runs a full concurrent review — no late-stage surprises.
Multiple stakeholder approval chains add weeks to every decision — competing priorities, slow email threads, unclear sign-off authority.
One stakeholder. Weekly demos. One decision point per sprint. The engagement is structured so that slowness has nowhere to hide.
Working code is delivered at the end — meaning real feedback arrives in week eight, when rework is most expensive and most disruptive.
Every sprint closes with deployed, testable software. Feedback arrives in week two — when it is still cheap and fast to act on.
Compliance is not a checklist. It is an architectural constraint.
Enterprises operating under APRA CPS 230, AML/CTF requirements, or internal governance frameworks do not just need working code — they need code that can withstand a regulatory audit the day after it is deployed. The Stack Build Method treats compliance as a first-sprint requirement, not a post-deployment task.
Data Residency by Default
Your data stays where it is required to. Environment architecture is scoped to your regulatory constraints before a single model is trained — not negotiated with IT security in week seven.
Audit Trails Built In
Every decision your AI system makes is logged and traceable. Designed into the data layer in Phase 1. Not added later when a compliance team raises a finding at go-live.
Access Controls from Day One
Role-based access, authentication, and least-privilege data access are part of the architecture design — not a post-deployment hardening task added under deadline pressure.
A PE-backed Australian healthcare group engaged Stack Build to ship four interconnected AI systems — an intelligence layer, a compliance agent, Xero reconciliation, and a procurement workflow. The result: 98% reduction in manual compliance processing within the first quarter. All four systems are APRA-compliant and running in production today — built and handed over in under ten weeks.
See the method applied to your problem.
A Discovery Call is sixty minutes. We will map your use case against the Stack Build Method and tell you exactly where your current approach is losing time — whether you engage us or not.