You inherited (or recognized) the moment when four pressures landed on the same OKR:
- A new CDO or CTO mandate to modernize digital while introducing AI-assisted engineering across the practice.
- An ADA Title III lawsuit, OCR letter, or DOJ-investigation-grade RFP landing on the same calendar as a design-system rebuild.
- Procurement / CISO / privacy-officer pressure: “we are betting on AI-native dev and we cannot have it embarrass us in an OCR inquiry, a malpractice claim, or a board-level audit.”
- An existing HELiX or A11y client scaling up to add HELiXiR and REA after the first deployment proved the model.
When all four are live, none of the smaller engagement shapes work. An accessibility audit alone leaves you with a roadmap and no governance to implement against. A design-system audit alone leaves you with a roadmap and no agent surface that knows about the components. An MCP server alone leaves you with a tool catalog and no audit trail. A REA install alone leaves you with policy and no product-engineering work to govern.
You need the four installed as one operating model — shared MSA, shared sequencing, shared audit substrate, shared phase boundaries. Not four engagements stitched together. One engagement with four SOW lines under one combined cap with a 20% combination discount.
These four layers are usually bought separately — an accessibility audit, a design-system engagement, an AI-governance install, and an MCP-server build, each from a different vendor, each with its own sequencing and its own artifacts. The Full Stack couples all four under one MSA, one sequence, and one audit substrate. The value is in the coupling, not in any single layer.
The engagement delivers HELiXiR-shaped documentation across all four product layers. HELiX components ship with markdown-first specifications and machine-readable Custom Element Manifest data, queryable by your agents through HELiXiR’s progressive-discovery MCP surface. REA’s policy file, hook chain, and subagent roster are themselves agent-readable. A11y findings are structured to be both human-readable (the auditor’s narrative) and agent-consumable (CSV + Linear-importable JSON + CEM-resolved file:line citations). The output is an operating model your team operates and your agents can read — not four separate reports filed and forgotten.