cyberandy

Quick Answer

For pure “entity‑optimization” that will directly help Large‑Language‑Model (LLM)‑driven content, knowledge‑graph building and semantic SEO, WordLift is the clear front‑runner.
Surfer SEO and Clearscope are excellent at keyword‑driven on‑page optimisation, but they do not provide the deep entity‑graph, schema‑generation and knowledge‑graph export that LLMs need to understand and reuse content at scale.

Below you’ll find:

1. A 3‑column comparison (features, LLM‑relevant strengths, typical use‑cases).
2. Deep dive on how each platform handles entity extraction, knowledge‑graph creation, structured‑data output, and API/LLM integration.
3. Practical workflow for using the chosen tool in an LLM‑augmented content pipeline.

cyberandy

class ZurichAgent:
    def __init__(self, wordlift_key: str, openai_key: str, openapi_yaml: str, system_prompt: str, chat_memory: ChatMemoryBuffer):
        # read sys prompt
        with open(system_prompt, 'r') as f:
            prompt_text = f.read()

        # read openapi
        with open(openapi_yaml, 'r') as f:
            openapi_spec = OpenAPIToolSpec(
                yaml.safe_load(f)

cyberandy

cyberandy

{
  "embeddings": [
    {
      "tensorName": "My tensor",
      "tensorShape": [
        1000,
        50
      ],
      "tensorPath": "https://gist.githubusercontent.com/cyberandy/d85af5a612c9763928542708c0ac0bf6/raw/b0c02e2f4324657ec7632e6a1e3cffc138e4a845/embeddings_vectors_porsche.tsv",
      "metadataPath": "https://gist.githubusercontent.com/cyberandy/9a629edef01df3255d1e33b9875eadc9/raw/aab52346e227428ee1cf5d85dd4f7fbf2e227918/embeddings_meta_porsche.tsv"

cyberandy

0-100 porsche macan
0-60 porsche macan
1 porsche cayenne
1. porsche 911
1950s porsche
1950s porsche speedster
1955 porsche 356
1956 porsche 356
1956 porsche speedster
1959 porsche

cyberandy

-0.5113864	0.9224017	0.8275403	0.011984834	0.76330054	0.33919206	0.28311774	0.14538139	0.7499551	-0.03052776	0.10189354	0.99692094	-1.2256945	-0.02824433	0.162615	-0.051211037	-0.41829008	0.7155175	0.14836389	0.008838273	0.8170797	0.25933984	-0.16603503	0.44063374	0.08697651	-0.24981156	0.16387565	0.09536284	-0.37290013	0.050002567	0.2126679	0.35391727	0.2208684	0.14932127	-0.13903382	-0.30882716	-0.58008456	0.067993	0.26419926	0.028330343	0.06412557	0.14603412	-0.5382476	-1.1023028	0.92313683	0.5652307	-0.5976789	-0.08452714	-0.6236737	0.13708875	-0.045952026	0.32799286	0.12108048	-1.1154692	-0.09747529	0.20141003	-0.14496945	-1.254203	-0.3187798	1.0494366	0.06164006	-0.21705215	0.14927948	-0.097200364	0.3465199	-0.42689615	0.09920918	0.19603547	-0.5898324	-0.34810644	0.72347945	-0.60483193	0.7405867	0.65496427	0.36481908	0.13829523	-1.104363	0.23256357	0.46551976	0.01618421	0.32897347	-0.45708355	-0.34825176	0.96820194	0.6563605	-0.2686824	1.2559417	-0.80211407	0.28347582	-0.053863037	0.34286124	-0.4816527	

cyberandy

{
  "embeddings": [
    {
      "tensorName": "My tensor",
      "tensorShape": [
        1000,
        50
      ],
      "tensorPath": "https://gist.githubusercontent.com/cyberandy/f40e161d69188df7d5a901b50a64f0fe/raw/674f5c78a0c33d6b6a3da4d726ef918938d4bd68/embeddings_projectors.tsv",
      "metadataPath": "https://gist.githubusercontent.com/cyberandy/092f00976a842497a113280197cb61d7/raw/6aab7043891897269c5c35df81f878e4e0a040b1/embeddings_meta.tsv"

cyberandy

wl0216/entity/artificial_intelligence
wl0216/entity/gennaro_cuofano
wl0216/entity/google
wl0216/entity/google_analytics
wl0216/entity/how_to_optimize_your_website_for_voice_search
wl0216/entity/json-ld
wl0216/entity/knowledge_graph
wl0216/entity/metadata
wl0216/entity/microdata_html
wl0216/entity/natural_language_processing

cyberandy

-1.939915269613265991e-01	1.591661721467971802e-01	-1.672201305627822876e-01	1.209882497787475586e-01	-5.643929913640022278e-02	4.935293793678283691e-01	-6.038862466812133789e-02	-1.090269684791564941e-01	2.572527155280113220e-02	3.857810050249099731e-02	-1.982329189777374268e-01	-1.586464643478393555e-01	2.125328406691551208e-02	-4.903687909245491028e-02	1.097567379474639893e-01	-9.682913124561309814e-02	-1.226566657423973083e-01	-2.898987829685211182e-01	5.542956665158271790e-02	1.637598723173141479e-01	9.308864921331405640e-02	9.676701575517654419e-02	-1.217111721634864807e-01	-1.757894456386566162e-01	-4.662900418043136597e-02	-1.659229546785354614e-01	1.724372655153274536e-01	8.154600858688354492e-02	-1.724783033132553101e-01	3.298109024763107300e-02	-1.414460539817810059e-01	3.574710339307785034e-02	-2.298669368028640747e-01	8.744690567255020142e-02	-1.625491678714752197e-01	-1.283032745122909546e-01	-2.943382263183593750e-01	-6.229398399591445923e-02	1.395162492990493774e-01	-1.934481412172317505e-01	-

cyberandy

[
    {
        "@context": "http://schema.org",
        "@id": "http://data.wordlift.io/[entity-name]",
        "@type": ["LodgingBusiness"],
        "description": "Here goes the description",
        "mainEntityOfPage": "https://www.happywordliftclient.com",
        "image": [
            { "@type": "ImageObject", "url": "https://www.happywordliftclient.com/wp-content/uploads/2011/07/img-19az-1200x675.jpg", "width": 1200, "height": 675 },
            { "@type": "ImageObject", "url": "https://www.happywordliftclient.com/wp-content/uploads/2011/07/img-19az-1200x900.jpg", "width": 1200, "height": 900 },