Which technologies were used in the Website Automation System architecture?

Reach, Zeo, Next.js, TypeScript, Tailwind CSS, Framer Motion

Representative Architecture Scenario

Website Automation System

A reference scenario covering architectural constraints, implementation choices, and the evidence required to validate outcomes in a real engagement.

Evidence level · representative

Derived from the typed scenario library and ecosystem metadata; performance statements are validation targets, not attributed client results.

Sources: src/content/caseStudies.ts · reviewed 2026-09-10

01 / The Problem

A consultancy website generated leads, but each inquiry required manual triage and inconsistent follow-up. Leadership needed a system that qualified demand without adding operational risk.

02 / Interactive Architecture

Hover components to explore layer responsibilities.

AIAS Triad Architecture Pipeline

Deterministic Flow

Active Layer: Zeo [Prong 02]Verified Deterministic Contract

Governed model execution, structured tool calling, and high-throughput workflow orchestration.

03 / Architecture Components

AIAS implemented a static-first Next.js architecture with a client-safe intake classifier, structured JSON export, and conversion paths tied to Reach campaigns and Zeo implementation tracks.
Static-first build with no backend lock-in
Deterministic intake routing with auditable rules
No hydration mismatches across marketing routes
Governance checkpoints for every transition from inquiry to engagement

04 / Implementation Highlights

Multi-step intake captures organization and scope signals
Deterministic classifier maps requests to advisory, co-build, managed, or enterprise paths
Structured payload export supports downstream execution in Settler
AI is constrained to recommendation copy and planning suggestions
Deterministic scorecard remains source of truth for engagement routing
Confidence-based escalation keeps human review in loop

05 / Automation Wins

Reach uses these case patterns for demand generation and qualification
Zeo consumes structured outputs for system build planning
Settler handles repeatable deployment and governance handoff
Lower customer acquisition cost
Higher lead-to-opportunity conversion
Cycle-time reduction in qualification

06 / Validation Plan

Reduced manual triage time through deterministic classification
Improved conversion quality by matching requests to clear engagement models
Maintained static-safe rendering and predictable build output

Aligned Engagement Paths

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02 / Interactive Architecture

Hover components to explore layer responsibilities.

AIAS Triad Architecture Pipeline

Deterministic Flow

Active Layer: Zeo [Prong 02]Verified Deterministic Contract

Governed model execution, structured tool calling, and high-throughput workflow orchestration.

03 / Architecture Components

AIAS implemented a static-first Next.js architecture with a client-safe intake classifier, structured JSON export, and conversion paths tied to Reach campaigns and Zeo implementation tracks.

Static-first build with no backend lock-in

Deterministic intake routing with auditable rules

No hydration mismatches across marketing routes

Governance checkpoints for every transition from inquiry to engagement

04 / Implementation Highlights

Multi-step intake captures organization and scope signals

Deterministic classifier maps requests to advisory, co-build, managed, or enterprise paths

Structured payload export supports downstream execution in Settler

AI is constrained to recommendation copy and planning suggestions

Deterministic scorecard remains source of truth for engagement routing

Confidence-based escalation keeps human review in loop