OpenAI Build Week 2026 / Work & Productivity
Stradviso Decision Studio
As a solo founder, I built a controlled AI decision system with Codex duringOpenAIBuild Week that converts customer questions into traceable project choices, without transferring human decision-making authority to the model.
The case in 30 seconds
Stradviso Decision Studio
- Problem
- As a solo founder, I built a controlled AI decision system with Codex duringOpenAIBuild Week that converts customer questions into traceable project choices, without transferring human decision-making authority to the model.
- Solution
- Growth platform with positioning, go-to-market, AI strategy and an interactive growth scan.
- Role and focus
- Strategy, processes
- Status
- Build Week Concept Lab
- Result
- Analysis, growth path
Live lab / try it yourself
Follow a supervised AI decision path
See how a customer request is defined, translated into traceable options and only allowed to continue in a controlled manner after human approval.
Product presentation
Decision Studio
This compact presentation lets the product itself speak: from a customer request and missing context to defined routes, human approval and controlled execution.
- 01Question and context
- 02Routes and human choice
- 03Safe execution and control
Build Week in numbers
Frozen release. Proven pace
Within 5 days and 15 hours of turnaround time, I built a complete Business OS and Decision Studio release together with Codex.
- Lead time
- 5 days 15 hours
- Repository
- 134 commits
- Product scope
- 460 files
- Test evidence
- 473 tests
- Assertions
- 6.431
- CI
- SQLite + MySQL passed
- Release
- Immutable R30
- Approval limit
- 3 records · 0 stale writes
The product question
A customer request is not yet a project decision
“Can we launch two weeks earlier without increasing the budget?”
The question sounds concrete, but capacity, delivery dates, ownership and consequences are not yet clear. Converting directly to a commitment would build uncertainty into the project.
- 01Context
Only authorized project context is collected.
- 02Conflict
Missing capacity and conflicting delivery dates become visible.
- 03Clarify
The system first asks one targeted clarification question.
- 04Options
GPT-5.6 then develops three defined routes.
- 05Authority
A responsible person will choose the exact route.
- 06Apply
Only Decision, Risk and Milestone change together.
- 07Recheck
If the project has been changed, it will be rejected with zero writes.
Four steps. One monitored transition.
From analysis to growth path
The interface makes the decision status visible and keeps context, permission and execution separate.
Formulate what needs to be decided
Choice, desired timing and most important uncertainty are explicitly recorded.
Compare restricted routes
AI provides three traceable options or stops when one missing piece of data needs to be answered first.
Tie approval to context
An authorized user reviews the chosen route and confirms the exact consequences.
Write only what is allowed
Go-to-market and digitalization follow the same rule: the atomic mutation is limited to Decision, Risk and Milestone.
Each step monitors context, human authority, and the exact records that are allowed to change.
- 01Customer questionTiming, budget or risk is made explicit.
- 02Authorized contextOnly the correct organizational and project data are included.
- 03Clarification or 3 routesMissing context first leads to one focused question.
- 04Human approvalAn authorized user selects and confirms the route.
- 05Revision checkThe approval is reassessed against the current context.
- 06Decision + Risk + MilestoneThe allowlist writes exactly three related records.
- 07Audit receiptA line of evidence makes the implementation traceable afterwards.
Governance as product behavior
Don't appear smarter, but work in a controllable manner
The most difficult design choice was not how the AI produces an answer, but what it should never do on its own.
Authorized context
The workflow assembles canonical project context within organizational and project permissions.
Refusal before provider use
Stop sensitive or cross-organization requests before key lookup or provider access.
Human approval
An AI output cannot write directly; consent remains explicit and context-specific.
Atomic allowlist
Only Decision, Risk and Milestone are allowed to change. A legacy context writes zero records.
Audit receipt
The implementation provides an append-only proof rule with which the permitted mutation can be verified.
Claim limit: store:false is a request setting, not a universal zero-retention claim. Provider processing remains subject to the applicableOpenAIterms and account settings.
Examined jury evidence
Four scenarios make the borderline behavior visible
The jury route demonstrates four checkpoints with representative, synthetic test data: clarify, approve, block outdated context and safely reset. Changing management functions remain shielded in the public environment, so that visitors can reproducibly inspect behavior without changing the evidence status.
If decision information is missing, the route asks one specific blocking question and the project status remains intact.
After human approval, the allowlist writes exactly one Decision, one Risk and one Milestone, including audit receipt.
A context change makes the previous approval demonstrably stable; the revision check keeps all project records intact.
The shielded operator route returns the approval state to a clean, re-inspectable starting position.
The larger system
Connected to work
The workflow lives in a complete Business OS environment where commercial context, projects, customer information and decision records come together.



Product domains
In Business OS
Seven connected domains bring together commercial context, project work and controlled collaboration in one working environment.
Before, during and after Build Week
Build quickly without blurring the provenance
Build Week accelerated an existingStradvisodirection, delivered a scoped release, and now provides a frozen reference for further development.
Strategic starting point
Name, consultancy concept, limited go-to-market materials, an earlier growth scan and the first visual direction already existed. The product structure and service architecture were developed during Build Week.
Working product release
Business OS, owner back office, customer portal, Decision Studio, approval flow, tests, dual database CI, release proof, Devpost submission and demo.
R30 remains frozen
The submitted release remains isolated and unchanged. Further developing the publicStradvisosite, live AI lane and commercial validation.
Hardest choices
Limit, recheck, prove
- Determine what AI should never perform independently.
- Binding approval to one specific project revision.
- Point product, tests, evidence, video and jury access to the same release.
Lessons learned
Speed requires explicit direction
- First build one complete vertical route.
- Grow evidence and product behavior simultaneously.
- Reserve capacity early for UAT, video and feature freeze.
- Human authority belongs in architecture, not in a disclaimer.
Human + Codex + model
Intensive collaboration, human decision-making
I bore final responsibility for content and product: from customer problem and user route to governance boundary, UAT and the final submission. The collaboration did not stop with the submission; the same design discipline guides further development.
Problem definition, product vision, user route, visual identity, human authority, UAT, voice-over and final product and submission decisions.
Technical construction partner for architecture, interfaces, workflows, tests, adversarial checks, documentation, error analysis and release preparation.
Processes authorized context, requests targeted clarification and prepares three bounded routes; never approval or project changes.
From brand to working system
One identity, two clear functions
The publicStradvisosite makes the advice promise understandable. Business OS brings that promise to daily execution and controlled project decisions.


Technical evidence note
Public evidence
The entire repository environment was shared privately during the official review.
Open the technical evidence note
Architecture. Authorized context → clarify of three options → human approval → revision check → atomic allowlist → audit receipt.
Test evidence. The R30 release recorded 473 tests, 6,431 assertions, and successful CI runs against SQLite and MySQL.
Evidence base. The results shown are traceable to the frozen R30 release, the recorded test evidence and the official submission.
Why this is business relevant
Human control remains visible
Decision Studio is designed for requests with timing, budget, capacity, or risk implications. It does not automatically prevent every wrong decision, but makes visible which context has been used, who has decided, what exactly is allowed and whether the approval still belongs to the current project status.
- AI output is not given direct project authority.
- Missing or conflicting evidence becomes visible.
- Human authority remains explicit.
- Outdated context does not result in partial updates.
Strategy × AI × Execution
Strategy to execution
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