For Enterprise Teams
The IFD Engagement Model
An IFD engagement is structured to deliver immediate value while building your team's capacity to sustain the practice independently. Four phases, each building on the previous, moving ownership progressively to your team.
Discovery
We begin by understanding your current state — not just your technology stack, but how your team works with it. We conduct an architecture review, assess your development workflow and AI tool usage, identify where architectural knowledge gets lost, and run initial altitude-gated design sessions on a representative slice of your system to demonstrate the IFD approach and surface intent that may not be captured anywhere today.
Discovery typically runs one to two weeks depending on system complexity. It produces a clear picture of where IFD delivers the most immediate value in your specific environment.
Methodology Adoption
With discovery complete, we establish the IFD foundation in your repository: a Diataxis-structured documentation directory, Design Decision Documents for the architectural choices surfaced in discovery, practice-level Skills configured for your environment, and a CLAUDE.md index that orients any AI assistant to the corpus on session start. The focus is your most critical systems first — a working IFD practice on a meaningful slice, not a comprehensive project that delays value.
By the end of this phase, AI sessions in your development environment navigate from documented intent rather than heuristics.
Skill Development
With the IFD foundation in place, we develop the project-level Skills that make it operational for your specific codebase. This involves extracting the conventions, naming patterns, and architectural rules your team follows, encoding them as Skills AI assistants consume during implementation, and validating against real tasks. Your senior developers and architects collaborate throughout to ensure Skills reflect your actual standards — not generic best practices. Skills are authored in the Claude Agent SDK format by default; if your primary AI assistant is Cursor, GitHub Copilot, or another tool, we ship equivalent Skill packages in the appropriate format.
Skills that don't reflect your team's actual conventions will not survive contact with production code — this phase is inherently collaborative.
Enablement
The final phase transfers full ownership of the IFD practice to your team. This includes facilitation training for altitude-gated design sessions, Skill authoring and DDD authoring practice with real decisions, and a sustainability review to confirm the practice is self-sustaining before the engagement concludes.
After enablement, your team has the methodology, the artifacts, and the capability to maintain and extend the IFD practice without ongoing external support.
Built for Your Team's Ownership
Each phase builds toward the same outcome: an IFD practice your team sustains independently.
The engagement is not designed to create a dependency. Each phase hands your team more ownership — by the end of enablement, XTIVIA's involvement is complete and your team is running the practice. The artifacts, templates, and Skills produced during the engagement remain in your repository, under your control, evolving with your codebase.
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