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OUR METHODOLOGY

Disciplined process. AI-scale execution.

Building a fundable hospitality company in months — not years — requires a methodology that lets AI scale execution while humans own direction. Here's how we work.

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Human judgment. AI-scale execution.

What humans own

  • Problem definition and product direction
  • Architecture decisions
  • Security review
  • Business logic validation
  • Quality gates between phases
  • Customer empathy and operational instinct

What AI accelerates

  • Documentation generation from live discussion
  • User story drafting
  • Technical specification scaffolding
  • Code scaffolding (CRUD, schemas, contracts)
  • Unit test and edge case generation
  • Refactoring suggestions

"AI without human gates is vibe code. Humans without AI scale is consultancy speed. We do neither."

Seven phases. One operating system.

Each phase has clear inputs, clear outputs, and a human quality gate before we move forward. No phase advances without explicit approval.

  1. 01

    Grooming session

    Capture complete business and technical context in a single structured session.

    Inputs
    Founder vision, market context, operational constraints, edge cases
    Outputs
    Full transcription + structured meeting notes

    Gate: Product lead validates that all logic was captured. No gaps in business intent.

    60–90 minutes · one session

    Most engineering teams start coding before they understand the business. We don't.

  2. 02

    Transcription processing

    Transform unstructured conversation into a validated business document.

    Inputs
    Raw meeting transcription
    Outputs
    AI-generated structured grooming summary: business context, functional areas, risks, assumptions, open questions

    Gate: Product lead validates for missing logic, misinterpretations, incorrect assumptions.

    30–60 minutes vs. 1–2 days of manual documentation writing

    Documentation is generated from live discussion, not invented later. Always fresh.

  3. 03

    Business stories

    Convert grooming summary into approved user stories with clear acceptance criteria.

    Inputs
    Transcription + structured grooming summary
    Outputs
    Epics, user stories, business logic, acceptance criteria (Given/When/Then), edge cases, definition of done

    Gate: Product lead and founder validate that business intent is preserved.

    50–70% faster than manual story writing

    Ambiguous requirements cause 35% of rework. We eliminate ambiguity before code.

  4. 04

    Technical specification

    Translate approved stories into a 70–80% ready technical specification.

    Inputs
    Approved business stories + original transcription
    Outputs
    Backend tasks (API, validation, services, data transformations), frontend tasks (UI states, error handling, UX notes, client-side validation), database layer (tables, fields, indexes, relationships, migrations), integrations (external APIs, events, webhooks), security, performance, logging

    Gate: Lead engineer and architect validate architecture compliance.

    Production-grade software needs production-grade specs. Vibe code skips this entirely.

  5. 05

    Task breakdown and estimation

    Convert technical spec into actionable sprint plan.

    Inputs
    Approved technical specification
    Outputs
    Development subtasks, QA subtasks, DevOps tasks, complexity estimates

    Gate: Engineering team reviews estimates, adjusts complexity, commits sprint capacity.

    Founders need predictable delivery. Estimates from AI, calibration from humans.

  6. 06

    Accelerated development

    Ship production-grade code at compressed timelines.

    AI accelerates
    Code scaffolding, CRUD operations, schema generation, unit test generation, API contracts, refactoring suggestions
    Developers own
    Architecture decisions, security review, performance optimization, code review

    Gate: Code review passes. No AI-generated code reaches production without human review.

    Feature implementation: 2 weeks → 1 week without quality loss

    AI-generated code has 1.7× more major issues than human-written when shipped without review. We don't ship without review.

  7. 07

    QA and validation

    Catch defects before production through systematic test coverage.

    AI generates
    Test cases, edge case scenarios, regression checklists, API testing scripts, automation test skeletons
    Humans own
    QA validates coverage, executes tests, signs off acceptance criteria

    Gate: QA lead validates that acceptance criteria are met.

    25–40% fewer defects vs. traditional development

    Production-grade hospitality software fails in customer-facing ways. We pressure-test before customers do.

No phase moves forward without a human gate.

This is the difference between AI-augmented engineering and vibe coding.

Phase Who validates What they validate
Grooming Product lead + engineering All business logic captured
Business stories Product lead + founder Business intent preserved
Technical spec Lead engineer + architect Architecture compliance
Implementation Engineering team Code review passed
QA QA lead Acceptance criteria met

Every phase has a named human who must approve before we advance. AI accelerates the work between gates. Humans own the gates themselves. This is what production-grade means.

What this gets you.

These are the time and quality compressions you should expect when working with us, compared to traditional studio engagements.

30–50% Faster feature implementation AI scaffolds, humans review and ship.
50% Less documentation overhead Specs are generated from live discussion, not written from scratch.
35% Less rework from unclear requirements Acceptance criteria are explicit before code begins.
25–40% Fewer defects reach production AI-generated test coverage + human QA gates.

Why hospitality founders need this discipline.

Hospitality software fails publicly. A bug in your point-of-sale takes down service. An outage in your booking system loses guests in real time. A workforce app that frustrates staff at the worst Friday-night moment damages the experience for every guest in the building.

Generic AI development methodology doesn't account for this. It optimizes for shipping fast. We optimize for shipping fast and not breaking the operational moment.

Every phase in our methodology has a hospitality-operator question hiding inside it: what happens at 7pm on Friday? What if the kitchen Wi-Fi drops? What does the staff actually do when the system says no? We co-build with founders who know these answers — and we encode them into specs before code begins.

What this is not.

We don't do this

  • Generate code with AI and skip review.
  • Ship MVPs that work in demo but fail at 100 users.
  • Treat documentation as optional or generated-after.
  • Promise speed without quality gates.
  • Let AI make architecture or security decisions.

We do this

  • Use AI to scaffold; humans review every change.
  • Build production-grade from the start; pressure-test before customers do.
  • Generate documentation as part of the work, not after.
  • Compress time through methodology, not by skipping steps.
  • AI scales execution; humans own all judgment.

"The code is the easy part. The hard parts are vertical knowledge, customer discovery, GTM in hospitality, fundraising narrative, and operational expertise from running ventures in this exact industry."

— TDX Studio

Ready to co-build?

If you're a hospitality operator with conviction and capital — let's talk.

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