Optimization
Turn Content Genome findings into specific, typed recommendations — then deploy them through an approval-gated, verifiable system that leaves an immutable record.
From content understanding to action
The Optimization Engine is where observation becomes intervention. Once the Content Genome has characterized a client's site — identifying what's present, what's structured, and what's missing — the engine applies a set of deterministic rules to generate typed recommendations. Each recommendation proposes a specific, exact DOM-level change: not 'improve your structured data' but 'add LocalBusiness schema to your homepage with these specific fields at this location.'
What the engine can currently recommend
Six recommendation types are live in the current Optimization Engine: missing LocalBusiness schema, missing FAQ block, thin content, missing meta description, multiple H1s, and missing service area block. Each rule is deterministic — the same content genome produces the same set of recommendations on each run. New recommendation types will be added as the engine grows; all capability additions are logged in the changelog.
The approval gate: how changes get deployed
No recommendation deploys automatically. Every intervention passes through a risk-aware approval workflow: the recommendation is packaged and signed, preflight checks run against the live site, and the agency reviews and approves before anything changes. Once approved, the intervention is deployed via the Standard Script — a lightweight browser tag that applies constrained, typed-DSL primitives to the DOM at runtime. After deployment, GeoScript independently verifies that the change landed as intended.
Standard Script: how GeoScript touches a site
The Standard Script is a fail-open browser tag installed on a client's site. It fetches a signed deployment manifest from GeoScript and applies typed primitive operations — the DSL explicitly rejects inline scripts, event handlers, and javascript: URLs. Changes are bounded, auditable, and rollbackable. If the manifest fetch fails, the page loads normally with no intervention applied.
How this connects forward and back
Optimization lives in the middle of the loop. It consumes Website Intelligence (the Content Genome) on one side, and its outputs flow into Reporting and outcome measurement on the other. After each deployment, GeoScript re-crawls the site, re-runs AI visibility observations, and attributes changes in visibility to specific interventions. The loop is closed through Reporting.
What Optimization is not
Explicit limitations (§127)
- ✕GeoScript does not automatically deploy any change — every intervention requires agency approval before it reaches a client's site.
- ✕GeoScript does not guarantee that a deployed change will increase AI visibility — it proposes changes based on structured content evidence; outcomes are measured separately.
- ✕The Content Studio (human-review surface for editing recommendations before deployment) is planned but not yet available.
- ✕GeoScript does not modify server-side content, CMS databases, or backend systems — Standard Script operates at the browser DOM layer only.
- ✕GeoScript does not claim causal attribution for visibility changes — it measures before-and-after observations and reports the difference; causal inference is explicitly noted as imperfect.
Related
Other pillars
Monitor how AI engines respond to the queries that matter for your clients — capturing mentions, recommendations, and citations across a prompt universe built around each business.
Find businesses with measurable AI visibility gaps, score each opportunity, track your pipeline, and deliver evidence-backed pitches — before a competitor does.
GeoScript crawls a client's public website, extracts a structured content evidence base, and builds the Content Genome that powers every recommendation.
Understand which AI crawlers and agents are reaching a client's site, what they're doing, and why — with verified identity and declared purpose.
Immutable, period-scoped reports that capture AI visibility change, deployments made, and measured outcomes — shareable with clients and presentable under agency branding.
Every observation, recommendation, and outcome lives in a structured, tenant-isolated, immutable-where-it-matters data architecture — the evidence base the entire platform reasons over.