Lucreya Research

The Lucreya Research Library

Open, DOI-backed, CC-BY 4.0 datasets on the AI go-to-market stack: which tools exist, what they cost, and which ones AI answer engines actually recommend. First-party measurement, machine-readable, free to cite. Part of the Lattice research network.

Lucreya datasets

First-party, permanently archived (Zenodo DOI), mirrored on Hugging Face and Kaggle.

AI Recommendation Reliability 2026

6 model families · self-agreement floors · 56% of the measured models retired within months

Before you can say two AI engines disagree, you have to know how much one engine disagrees with itself. We measured that floor, then re-examined our own 2026 audit against it. Three findings: a model reproduces about three quarters of its own answer at temperature 0; changing model size moves the recommendation set about as much as changing lab; and most of the models in our published panels have since been retired by their providers, so the studies cannot be re-run at any price.

Study & findings Data release Raw answers (JSONL) Manifest

Cite: Couey, V. W. (2026). AI Recommendation Reliability 2026: self-agreement, model size and model mortality in AI visibility measurement. Lucreya / Lattice. CC BY 4.0.

The AI Recommendation Audit 2026

8 AI engines · 16 categories · 1,549 recommendations · agreed on the #1 tool in 0% of categories

Method note: each engine was asked once, so this figure cannot be separated from the instrument’s own noise. See the reliability audit.

We asked five live AI engines (ChatGPT, Perplexity, Gemini, Llama via Groq, Cohere) the same buying questions across 16 B2B/GTM software categories and recorded every tool each one named, in order. The engines never once all agreed on the single best tool; search-grounded engines reading the live web surface different winners than model-memory ones. Includes a per-tool AI-Visibility Score for every measured tool.

Study & findings Data (JSON) Zenodo DOI Hugging Face Kaggle

Cite: Couey, V. W. (2026). The AI Recommendation Audit 2026: Which B2B/GTM Software AI Assistants Recommend [Data set]. Lucreya. https://doi.org/10.5281/zenodo.20767878

Who AI Recommends: GTM Tools 2026

60 AI answers · 162 Perplexity citations · Reddit cited in 75% of answers · ChatGPT / Perplexity / Google AI

An original measurement of which GTM tools three AI answer engines recommend, and which sources they cite. A primary-source dataset for generative-engine optimization (GEO) and AI-search citation behavior.

Study & findings Data (JSON) Zenodo DOI Hugging Face Kaggle

Cite: Couey, V. W. (2026). Who AI Recommends: GTM Tool and Source Citations [Data set]. Lucreya. https://doi.org/10.5281/zenodo.20632768

AI GTM Tools 2026: Pricing & Capability Index

30 tools · marketing / seo-geo / sales · normalized entry pricing & merit tiers

A curated, merit-placed index of 30 AI go-to-market tools across marketing, SEO/GEO, and sales, with normalized entry pricing and capability tiers (locked before any monetization check).

Study & findings Data (JSON) Zenodo DOI Hugging Face Kaggle

Cite: Couey, V. W. (2026). AI GTM Tools 2026: Pricing and Capability Index [Data set]. Lucreya. https://doi.org/10.5281/zenodo.20632766

Explore the analysis

The datasets above power Lucreya's coverage of AI go-to-market and answer-engine visibility.

AI visibility checkAre you cited by AI engines? GEO placementGet into the AI answer Tool roundupsMarketing · SEO/GEO · sales For startupsThe GTM stack, costed MethodologyHow we test and score

The Lattice research network

Lucreya is one node of the Lattice, a research-driven network of 17 open, DOI-backed, CC-BY datasets across 13 properties, all published under one ORCID-verified byline. Explore the whole library:
The network hub →All properties & the full research index Hugging Face →17 datasets, mirrored Kaggle →17 datasets, mirrored Nesyona ResearchAI model & pricing data Rinzara ResearchAI creative policy & rights data The authorVincent Couey · ORCID
☆ Save
Dashboard