Traditional SEO ranked blue links. We engineer your brand to be cited as ground truth by frontier models.
Over 60% of enterprise software evaluations and technical purchases now start with conversational queries inside ChatGPT, Perplexity, Claude, or Google AI Overviews. If your company lacks unambiguous entity definitions, dense semantic triples, and verified knowledge graph grounding, frontier models will synthesize competitor recommendations instead. Frontier Harness approaches GEO and AEO as an information retrieval engineering challenge: we structure your corporate knowledge so LLM retrieval agents cite and recommend your brand.
Why Traditional SEO Fails in the Generative Search Era
Most enterprise AI initiatives fail not because model intelligence is lacking, but because off-the-shelf tools and naive prompt wrappers cannot survive the messy reality of enterprise data.
Traditional SEO optimizes for keyword density and backlink volume; generative search engines evaluate semantic density, entity relationships, and source quotability.
LLMs frequently synthesize hallucinations or cite outdated product pricing because enterprise marketing text is buried in unstructured PDFs and marketing fluff.
Search visibility is now zero-sum: instead of 10 organic positions on Google, answer engines synthesize 1 direct answer citing only 2-3 trusted entities.
Most enterprise teams have zero observability into their 'Share of Model' (SoM)—they cannot measure how often ChatGPT or Perplexity recommends them over competitors.
How We Engineer & Operate the System
A deterministic 4-stage pipeline orchestrated with verifiable evaluation suites, telemetry monitoring, and human-in-the-loop governance.
Entity Grounding & Semantic Triples
We extract and structure your enterprise offerings, leadership credentials, and client outcomes into Schema.org graph structures, Wikidata entity records, and disambiguated semantic triples.
Vector RAG Optimization & Chunk Density
We engineer your technical documentation, whitepapers, and product specs for optimal neural embeddings and web-crawling retrieval agents (GPTBot, PerplexityBot, Claude-Web).
Synthetic Query & Share of Model (SoM) Benchmarking
We execute automated test suites across hundreds of prompt permutations daily on GPT-4o, Claude 3.5 Sonnet, Gemini 2.0, and Perplexity Sonar to benchmark citation share against competitors.
Hallucination Auditing & Source Correction
Continuous telemetry detects when models hallucinate inaccurate capabilities or omit your brand, triggering targeted inverted index updates and authority source recalibration.
What You Receive in Production
Enterprise Knowledge Graph & Schema Triple Deployment
PRODUCTION CODEFull production deployment of nested JSON-LD entity structures covering Organization, Products, Services, Technical Articles, and Executive credentials.
High-Density Technical Blueprint & Citation Hub
EDITORIAL ARCHITECTUREArchitectural whitepapers, benchmark data, and disambiguated specifications designed specifically for LLM extraction and direct quotability.
Continuous Share of Model (SoM) Dashboard
TELEMETRY SUITEWeekly automated benchmarking tracking recommendation win-rates across Perplexity, ChatGPT, Claude, and Gemini with competitor comparison.
LLM Referral & Pipeline Attribution Tracker
REVOPS INTEGRATIONCustom server-side attribution connecting conversational AI search traffic directly to CRM opportunities and pipeline conversion.
// Frontier Harness Entity Grounding & Knowledge Graph Spec
export interface EntityGroundingSpec {
"@context": "https://schema.org";
"@type": "ProfessionalService";
name: string;
alternateName: string[];
knowsAbout: Array<{
"@type": "Thing";
name: string;
sameAs: string; // Wikidata URI for neural entity disambiguation
}>;
hasOfferCatalog: {
"@type": "OfferCatalog";
name: string;
itemListElement: Array<{
"@type": "Offer";
itemOffered: {
"@type": "Service";
name: string;
description: string; // Machine-extractable, high semantic density
};
}>;
};
}Frequently Asked Questions
What is the difference between SEO and GEO / AEO?
Traditional SEO optimizes for keyword density, crawl budgets, and backlinks to rank in Google's 10 blue links. Generative Engine Optimization (GEO) and Answer Engine Optimization (AEO) optimize for neural embeddings, entity grounding, and retrieval-augmented generation (RAG) so that conversational models (ChatGPT, Perplexity, Claude) extract your brand as the authoritative, direct answer.
How do you track whether ChatGPT or Perplexity is citing our business?
We engineer an automated synthetic evaluation harness that runs hundreds of enterprise prompt variations daily across OpenAI, Anthropic, Google, and Perplexity APIs. We calculate your 'Share of Model' (SoM)—the percentage of times your brand is cited, recommended, and sentiment-scored compared to competitors.
Can you fix hallucinations where AI models state inaccurate info about our product?
Yes. Models hallucinate when retrieval sources contain conflicting, unstructured, or outdated data. We audit model hallucinations, create verified primary-source triples, update crawl feeds, and structure disambiguation data so AI engines resolve your facts accurately.
How quickly do we see results from GEO engineering?
Unlike legacy SEO which takes 6-12 months, real-time retrieval engines like Perplexity Sonar and SearchGPT update their search index dynamically. Our clients typically see measurable citation win-rate improvements within 14 to 30 days of schema deployment and knowledge graph grounding.
GTM & RevOps AI
Turn target account lists into deep strategic intelligence, buying signals, and inbound pipeline.
Financial Operations AI
End-to-end invoice processing, automated 3-way matching, and exception triage at enterprise scale.
Legal Operations AI
Pre-execution redlining, playbook compliance scoring, and automated agreement triage.
Identify Where AI Delivers Verifiable ROI
Every engagement starts with a structured assessment to audit your data, map workflows, and define the exact KPIs our engineers will be accountable for.