RetrieveAI is a scoped-first Retrieval Intelligence Engine designed to measure AI visibility, entity strength, prompt coverage, and commerce-readiness inside generative systems.
Real screens from the RetrieveAI prototype — validating the retrieval scoring architecture, scope management, and commerce intelligence layers across the full audit pipeline.
Composite scoring dashboard showing AI Visibility Score, Entity Strength, and Retrieval Coverage as normalized 0–100 metrics with trend overlays. Audit job status and recent simulation run history are surfaced at the top level.
Score delta visualization across audit runs over time. Surfaces volatility in LLM retrieval behavior per entity. Identifies which scoring dimensions are stable across simulation runs and which are prompt-sensitive — enabling targeted remediation.
Crawl depth controls, URL type classification, and scope mode assignment interface. Shows how the system maps site architecture into auditable surfaces before any prompt simulation begins. Handles single_page through full_site mode transitions.
Purchase-intent signal density scoring, product catalog intelligence assessment, and AI-assisted commerce surface analysis. Evaluates how effectively structured product data performs in post-search generative discovery environments like conversational shopping.
Six precision-engineered scoring modules that audit, quantify, and surface how generative AI systems understand and retrieve a brand at inference time.
Measures how prominently a brand surfaces across LLM inference paths. Combines entity recall rate, prompt match frequency, and representation density into a normalized composite using deterministic simulation runs.
Evaluates semantic depth and disambiguation clarity of a brand entity across structured data coverage, knowledge graph co-occurrence signals, and contextual representation quality in LLM-accessible content.
Maps all retrievable surfaces — product pages, category clusters, FAQ nodes — and verifies each is correctly accessible, contextually coherent, and structured for AI retrieval pipelines and vector index ingestion.
Validates schema markup completeness, JSON-LD fidelity, and data structure correctness. Ensures pages communicate unambiguously to AI crawlers, embedding generators, and RAG context construction pipelines.
Runs deterministic, temperature-zero LLM prompt simulations against scoped content using real-world query patterns. Identifies retrieval gaps, hallucination entry points, and entity coverage blind spots across each audit surface.
Audits product catalog intelligence, purchase-intent signal density, and AI-assisted commerce surface quality. Scores how effectively a brand converts within post-search generative discovery environments.
Every audit begins with intelligent scope definition — matching audit depth to the specific use case, controlling API budget consumption, and enabling progressive intelligence layering across pipeline stages. No blind full-site sweeps.
Automated crawl mapping via sitemap parsing, Playwright-driven link extraction, and DataForSEO graph enrichment. Produces a typed, classified URL inventory before any LLM work is dispatched.
Semantically related pages are bundled into retrieval contexts before prompt dispatch — reducing token waste, improving simulation fidelity, and enabling cross-URL entity coherence scoring within the same job.
Four scope modes each carry distinct page caps, prompt universe sizes, scoring weight distributions, and API cost envelopes. Scope selection is the first architectural decision in every audit configuration.
Scores are computed in ordered layers — shallow visibility scores first, deep simulation and recommendation generation last. No LLM work is dispatched until prior pipeline stages pass validation and caching.
Eight deterministic stages transform raw URLs into actionable retrieval scores, entity models, and structured recommendations — each stage dependent on validated output from the previous.
Chosen for production reliability, AI workload compatibility, and vector retrieval performance — not convenience defaults.
When a user asks an LLM to recommend a product, compare services, or explain a concept, the model retrieves from its training context and any available RAG surfaces. SEO rank is irrelevant inside that retrieval.
Traditional analytics measure what happens after a click. SEO tools measure position in a ranked list. Neither instrument the prior question: does the model retrieve your brand at all?
RetrieveAI was built to close that instrumentation gap — with scoped audits, deterministic prompt simulation, and entity-level scoring that gives engineers a measurable model of brand retrievability inside generative systems.
Generative AI interfaces are becoming the primary touchpoint for product discovery and comparison. Brand retrieval inside LLMs is now a measurable, improvable business surface.
Search ranking and LLM retrieval are architecturally distinct. Optimizing for PageRank does not optimize for token-level entity representation inside a language model's inference context.
Without temperature-zero, seed-controlled dispatch, score deltas between audit runs are indistinguishable from model variance. Determinism is what makes regression tracking scientifically valid.
Most retrieval problems are localized to specific intent surfaces. Scoped audits produce higher signal-to-noise ratios, lower API overhead, and faster iteration cycles than site-wide crawls.
Reliability primitives — circuit breakers, semaphore concurrency, timeout budgets, cache versioning — were designed in from day one. Defensive engineering is a discipline, not an afterthought.
Scope is the first architectural decision in every audit. Each mode defines a distinct envelope of page caps, prompt universe size, scoring weight distribution, entity reinforcement depth, and API cost control.
Surgical audit for one URL. Maximum signal concentration for a single intent surface.
Semantically related page group. Best fidelity-to-cost ratio across all scope modes.
Full category-level audit including sub-pages, facets, and filter surfaces.
Complete site-wide retrieval intelligence with full concurrency and budget controls active.
| Scope Mode | Entity Disambiguation | Context Bundling | Cross-URL Scoring | Semaphore Concurrency | Commerce Layer |
|---|---|---|---|---|---|
| single_page | Partial | — | — | — | — |
| context_cluster | ✓ | ✓ | ✓ | — | ✓ |
| category | ✓ | ✓ | ✓ | Partial | ✓ |
| full_site | ✓ | ✓ | ✓ | ✓ | ✓ |
Architecture and design decisions explained clearly.
No. SEO tools optimize for search engine rankings — crawl coverage, backlink authority, keyword density. RetrieveAI audits how LLMs retrieve and represent a brand inside generative inference. These are architecturally distinct problems requiring different instrumentation and different remediation paths.
Ranking measures position in a sorted result list. Retrieval measures inclusion in a model's consideration set. A brand that never appears in an LLM-generated response has zero retrieval coverage — regardless of search rank. RetrieveAI measures retrieval directly using scoped prompt simulation, not search index proxies.
Full-site sweeps are expensive in LLM API calls, Playwright compute time, and result noise. Most retrieval problems are localized to specific intent surfaces. Scoped audits produce higher-fidelity, lower-noise signals faster and at lower cost per actionable insight generated.
Without temperature-zero, seed-controlled dispatch, score changes between audit runs could reflect model stochasticity rather than content changes. Determinism makes the scoring system scientifically valid — a delta in AI Visibility Score means the content changed, not the random seed. This is a foundational architectural requirement.
Active development progress across all pipeline modules. Features are being built, tested, and validated against real audit runs.
Sitemap parsing, Playwright-based link extraction, URL classification and deduplication. Produces a typed URL inventory as the first pipeline stage input, handling redirects and canonical resolution.
Semantic clustering of URL sets into coherent retrieval contexts. Bundles related pages before prompt dispatch to reduce token overhead, improve simulation fidelity, and enable cross-URL entity coherence scoring.
Composite scoring system combining entity recall rate, prompt match frequency, and representation density into a normalized 0–100 visibility score per URL and context cluster.
Deterministic, temperature-zero LLM prompt dispatcher. Generates real-world query patterns per scope, dispatches with seed control, and parses structured retrieval results for downstream scoring stages.
Purchase-intent signal density scoring and AI-assisted product catalog intelligence. v1 ships basic commerce surface detection; v2 adds generative shopping simulation and structured product data schema validation.
End-to-end simulation of purchase-intent prompt flows through generative AI interfaces. Models how AI-assisted commerce surfaces retrieve, recommend, and convert product entities from the retrieval layer.
RetrieveAI is an active portfolio prototype. If you're working on retrieval infrastructure, AI visibility tooling, or post-search commerce systems — this architecture may be relevant to your work.
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