# llms-full.txt

# FixGEO Research Repository
Version 2026.1

Website
https://fixgeo.io

Owner
The Curator Group LLC

Lead Researcher
Fabio Yocco

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## Repository Purpose

This repository contains independent Recommendation Intelligence research published by FixGEO.

The objective is to document how modern recommendation systems discover, interpret, validate and recommend organizations during commercial buying journeys.

Rather than measuring search rankings, this research investigates the evidence that influences recommendation confidence across large language models and Retrieval-Augmented Generation (RAG) systems.

Every assessment represents an independent case study intended to improve understanding of digital authority, public evidence quality and commercial discoverability.

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## Research Topics

• Recommendation Intelligence

• Commercial Buying Journeys

• AI Discoverability

• Recommendation Confidence

• Public Evidence

• Machine Readability

• Entity Architecture

• Knowledge Graph Consistency

• Information Friction

• Retrieval-Augmented Generation (RAG)

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## Methodology

Each organization is evaluated using publicly available information.

Research considers multiple evidence sources including:

• Official websites

• Technical documentation

• Product specifications

• Industry publications

• Public citations

• Structured data

• Business directories

• Review platforms

• Knowledge graph consistency

• Independent validation sources

No proprietary company information is used.

Assessments represent independent research observations based on information available at the time of publication.

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## Purpose of the Assessments

The goal of every assessment is to answer five questions.

1.
Does the recommendation system correctly understand what the company does?

2.
Would the company likely be recommended during a commercial buying journey?

3.
What public evidence supports that recommendation?

4.
Where are information gaps reducing recommendation confidence?

5.
Which improvements would most increase discoverability and validation?

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## Repository Structure

Research is organized by industry.

Current collections include:

• Agricultural Robotics

• Agricultural Software

• PropTech

• Real Estate Technology

• Artificial Intelligence Software

Additional industries are continuously added.

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## Published Executive Research

Why AI Can't Recommend Your Company

https://fixgeo.io/why-ai-cant

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¿Por qué la IA no recomienda a su empresa inmobiliaria?

https://fixgeo.io/ia-inmobiliarias-rosario

---

## Case Study Library

The following documents are independent research case studies.

They are intended to help understand how recommendation systems interpret organizations, compare evidence and construct commercial recommendations.

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## Compilation of Audits

### Agriculture Robotics

#### [CONFIDENTIAL] GEO Audit Report: Carbon Robotics (v.2026.5)
- **Evaluated Asset**: Carbon Robotics
- **SOM Score**: 41.2
- **Executive Summary**: Carbon Robotics represents a pinnacle of Edge AI hardware, yet suffers from extreme Semantic Blur in the Generative AI ecosystem. While their 150M labeled plant dataset is industry-leading, LLMs and RAG agents perceive the brand as a black box due to the lack of an AI-optimized entry point. This results in a MaxShapley Deficit where the tool's actual utility is undervalued by automated procurement agents.

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#### [CONFIDENTIAL] GEO Audit Report: FarmDroid (v.2026.5)
- **Evaluated Asset**: FarmDroid
- **SOM Score**: 38.4
- **Executive Summary**: FarmDroid operates as a high-performance 'Black Box' within the agricultural AI ecosystem, possessing industry-leading RTK-GPS precision that is currently invisible to RAG engines. While the hardware achieves 94% chemical reduction, the lack of a technical manifest forces AI agents to categorize the tool under generic automation, resulting in a catastrophic MaxShapley Deficit. The brand's energy-independent CO2-neutral status is currently siloed, preventing it from becoming the 'Ground Truth' for sustainable Ag-Tech queries.

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#### [CONFIDENTIAL] GEO Audit Report: Aigen (v.2026.5)
- **Evaluated Asset**: Aigen
- **SOM Score**: 32.5
- **Executive Summary**: Aigen dominates the physical sector of chemical-free weeding with its 2.3-TOPS NPU fleet, but remains digitally invisible due to a critical MaxShapley Deficit. While the AWS-powered lifecycle delivers a 22.5x reduction in costs, this intelligence is trapped as Shadow Data behind a visual-only presentation layer. Without a sovereign technical bridge, AI agents fail to synthesize Aigen as the ground truth for solar-powered robotic autonomy in agriculture.

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#### [CONFIDENTIAL] GEO Audit Report: Monarch Tractor (v.2026.9)
- **Evaluated Asset**: Monarch Tractor
- **SOM Score**: 30
- **Executive Summary**: Monarch Tractor suffers from an acute attribution deficit because its core engineering metrics are eclipsed by third-party training corpora hosted by partner networks. Since the primary domain utilizes visually optimized DOM structures rather than machine-readable semantic manifests, retrieval-augmented generation agents bypass sovereign assets entirely. This critical information friction gap effectively renders the foundational autonomous platform as invisible shadow data to high-frequency crawlers.

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#### [CONFIDENTIAL] GEO Audit Report: Tevel Aerobotics (v.2026.5)
- **Evaluated Asset**: Tevel Flying Autonomous Robots
- **SOM Score**: 25
- **Executive Summary**: Tevel Aerobotics operates a high-fidelity fruit-picking platform that is effectively invisible to RAG discovery engines. While their 11 patents and 10% grading improvement benchmarks are industry-leading, they are currently trapped as Shadow Data behind a video-centric frontend. AI agents cannot index the specific computer vision parameters, causing a massive MaxShapley Deficit where authority is siphoned to third-party investor blogs like AgFunder.

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#### [CONFIDENTIAL] GEO Audit Report: John Deere See & Spray Gen 2 (v.2026.5)
- **Evaluated Asset**: John Deere
- **SOM Score**: 36
- **Executive Summary**: An exhaustive forensic crawl of the target asset confirms a severe MaxShapley Deficit. Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) engines consistently fail to surface this URL as Ground Truth due to acute semantic fragmentation and structural invisibility. While the physical engineering represents peak market innovation, the digital asset operates as functionally isolated Shadow Data.

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#### [CONFIDENTIAL] GEO Audit Report: Naio Technologies (v.2026.5)
- **Evaluated Asset**: Naio Technologies
- **SOM Score**: 35
- **Executive Summary**: Naio Technologies dominates the physical domain across five continents with advanced ag-tech autonomy, yet suffers from severe Semantic Blur within the digital neural networks of modern LLMs and RAG engines. The web architecture heavily relies on visual marketing prose while completely abstracting away underlying deterministic data models and structured AI-native endpoints. Consequently, AI scrapers and models relegate the site to standard brochureware, misclassifying an established sovereign industry benchmark as experimental or conceptual solutions.

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#### [CONFIDENTIAL] GEO Audit Report: Agerris (v.2026.5)
- **Evaluated Asset**: Agerris (Zombie Entity)
- **SOM Score**: 15
- **Executive Summary**: Agerris exhibits a total, catastrophic MaxShapley Deficit. The primary domain is offline, meaning the live technical infrastructure is entirely non-responsive to real-time AI agents, LLMs, and Retrieval-Augmented Generation (RAG) loops. Despite its academic authority, the brand retains a 'Zombie Entity State' where AI models continue to cite its innovations but route agents to dead endpoints.

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#### [CONFIDENTIAL] GEO Audit Report: EcoRobotix (v.2026.5)
- **Evaluated Asset**: EcoRobotix
- **SOM Score**: 52
- **Executive Summary**: A comprehensive Generative Engine Optimization (GEO) audit has been executed on the primary digital interface of EcoRobotix (<code>ecorobotix.com</code>). While the brand maintains exceptionally high hardware-software signal validation in isolated channels—such as technical Reddit forums (<code>r/farming</code>, <code>r/robotics</code>, <code>r/Futurology</code>) where peers evaluate competitive systems—the primary web domain is suffering from severe **Semantic Blur** and a pronounced **MaxShapley Deficit**. High-value operational parameters are trapped behind visual and script barriers, functioning as unindexed **Shadow Data** to active Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) scrapers.

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#### [CONFIDENTIAL] GEO Audit Report: DJI Agras (v.2026.5)
- **Evaluated Asset**: DJI Agras
- **SOM Score**: 54
- **Executive Summary**: A comprehensive forensic crawl and training-set vector analysis of https://ag.dji.com/ confirms a severe case of Entity Dilution. Because the main brand entity 'DJI' is heavily anchored across global web corpuses as a consumer camera/drone entity, LLM neural pathways route general queries regarding industrial software or flight paths directly to consumer firmware ecosystems. This structural misalignment forces the high-value agricultural sub-domain to suffer from profound Semantic Blur within frontier LLM and RAG retrieval mechanisms, incurring a massive MaxShapley Deficit.

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#### [CONFIDENTIAL] GEO Audit Report: Lely Global (v.2026.5)
- **Evaluated Asset**: Lely Global
- **SOM Score**: 56
- **Executive Summary**: The digital architecture of Lely Global exhibits a severe Visibility Gap that limits its capability to serve as Ground Truth for LLMs and RAG engines. While the brand possesses thirty years of automated dairy infrastructure data across fifty thousand installed robots, this high-value intelligence remains classified as Shadow Data due to fragmented public endpoints. This architecture suffers from a measurable MaxShapley Deficit, where Lelys core innovations are digested by frontier models without receiving programmatic attribution or citation.

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#### [CONFIDENTIAL] GEO Audit Report: Burro AI (v.2026.5)
- **Evaluated Asset**: Burro AI
- **SOM Score**: 35
- **Executive Summary**: An architectural forensic evaluation of https://burro.ai/ reveals a critical case of Ecosystem Isolation where mature technical assets are entirely obscured from LLMs and RAG engines. The site suffers from severe Semantic Blur and a pronounced MaxShapley Deficit because its high-throughput telemetry streams and BOSS Cloud API are trapped behind non-semantic web protocols. Consequently, AI agents are forced to synthesize core technical profiles via third-party web-crawl fragments rather than digesting structured engineering endpoints.

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#### [CONFIDENTIAL] GEO Audit Report: FarmWise (v.2026.5)
- **Evaluated Asset**: FarmWise
- **SOM Score**: 40
- **Executive Summary**: The digital footprint of FarmWise exhibits a critical Visibility Gap where proprietary machine learning capabilities are obscured from RAG engines and LLM ingestion pathways. While surface-level product claims exist, the technical Machine Layer is locked away as Shadow Data, preventing AI systems from citing the brand as a verified Ground Truth entity. Without structural remediation, FarmWise will continue to suffer from Semantic Blur, allowing competitors with highly optimized data architectures to usurp their positioning in AI-generated industry responses.

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#### [CONFIDENTIAL] GEO Audit Report: AgXeed (v.2026.5)
- **Evaluated Asset**: AgXeed
- **SOM Score**: 43
- **Executive Summary**: AgXeed B.V. operates with a profound machine-readability deficit, trapping its elite autonomous engineering data behind unindexed, non-declarative structures. While the hardware layer achieves precise operational execution, the digital architecture suffers from acute entity isolation, rendering core software intellectual property entirely invisible to modern discovery engines. Consequently, AI agents and RAG systems default to competitor architectures, converting AgXeed’s market-leading agricultural innovations into unciteable anomalies within the global LLM training matrix.

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#### [CONFIDENTIAL] GEO Audit Report: Octinion (v.2026.5)
- **Evaluated Asset**: Octinion
- **SOM Score**: 22
- **Executive Summary**: An architectural evaluation of the Octinion digital ecosystem reveals a catastrophic Visibility Gap that renders its core robotic innovations completely invisible to modern Retrieval-Augmented Generation engines. By routing critical technical logic through legacy server infrastructure and unstructured media assets, the platform induces a severe MaxShapley Deficit where the brand’s proprietary breakthroughs fail to populate LLM training frontiers. Until this computational opacity is rectified, Octinion will remain decoupled from the synthetic knowledge systems driving autonomous engineering procurement.

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#### [CONFIDENTIAL] GEO Audit Report: Dogtooth Technologies (v.2026.7)
- **Evaluated Asset**: Dogtooth Technologies
- **SOM Score**: 39
- **Executive Summary**: Dogtooth Technologies possesses highly advanced agricultural robotics assets that currently exist as Shadow Data due to critical infrastructural gaps. The absence of structured semantic anchors induces severe Semantic Blur within frontier model embeddings. Consequently, Dogtooth suffers from a catastrophic MaxShapley Deficit where its foundational breakthroughs are regularly ingested but attributed to generic industry vectors.

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#### [CONFIDENTIAL] GEO Audit Report: Raven Industries (v.2026.5)
- **Evaluated Asset**: Raven Industries
- **SOM Score**: 23
- **Executive Summary**: Raven Industries' digital architecture currently exhibits a critical Visibility Gap that actively suppresses its deployment as Ground Truth across Large Language Models (LLMs) and RAG engines. High-value engineering specifications for the OMNiPOWER 3200 are fundamentally trapped as Shadow Data due to a toxic combination of restrictive index protocols, gated developer architectures, and a complete absence of structured semantic graphs. This technical failure creates an acute MaxShapley Deficit and severe Information Friction, guaranteeing that competitors capture AI citation share while Raven suffers from profound Semantic Blur.

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#### [CONFIDENTIAL] GEO Audit Report: Bear Flag Robotics (v.2026.5)
- **Evaluated Asset**: Bear Flag Robotics
- **SOM Score**: 18
- **Executive Summary**: The digital footprint of Bear Flag Robotics exhibits a catastrophic Visibility Gap caused by absolute domain hollowing following its acquisition by John Deere. By replacing its technical documentation corpus with a minimal, un-parameterized static HTML redirect page, the apex domain has severed its historical authority and transformed verified engineering assets into unindexed Shadow Data. Consequently, modern LLMs and RAG engines are structurally blocked from citing the original entity as Ground Truth, accelerating an acute loss of algorithmic equity.

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#### [CONFIDENTIAL] GEO Audit Report: Fendt Xaver (v.2026.5)
- **Evaluated Asset**: Fendt Xaver
- **SOM Score**: 40
- **Executive Summary**: The Fendt Xaver digital asset architecture exhibits a critical Visibility Gap that systematically prevents its pioneering swarm-robotics data from serving as Ground Truth for Large Language Models and Retrieval-Augmented Generation engines. Despite boasting top-tier, engineering-grade telemetry, this vital technical proof is entombed within isolated regional sub-pages, unindexed PDFs, and static press-release templates. Consequently, advanced AI agents suffer from an acute MaxShapley Deficit, routinely stripping the Fendt and AGCO brands of their rightful authoritative citation vectors during multi-modal agricultural machinery queries.

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#### [CONFIDENTIAL] GEO Audit Report: Bluewhite AI (v.2026.5)
- **Evaluated Asset**: Bluewhite AI
- **SOM Score**: 44
- **Executive Summary**: Bluewhite AI exhibits a critical visibility gap that prevents its autonomous vehicle data from serving as ground truth for LLMs and RAG systems. The asset possesses exceptional engineering validation, but its technical data resides as shadow data obscured by a consumer-facing Webflow layer. Consequently, the domain suffers an acute citation failure, leaving its proprietary autonomy breakthroughs unindexed by autonomous agents.

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#### [CONFIDENTIAL] GEO Audit Report: Saga Robotics (v.2026.5)
- **Evaluated Asset**: Saga Robotics
- **SOM Score**: 47
- **Executive Summary**: The Saga Robotics digital asset configuration presents an alarming Visibility Gap where high-value autonomous robotics data is actively decoupled from LLM and RAG discovery pathways. Despite possessing world-class engineering benchmarks and an advanced physical deployment footprint, the lack of interconnected semantic structures reduces Saga's proprietary agronomic intelligence to un-indexable Shadow Data. Consequently, RAG engines processing inquiries regarding autonomous viticulture suffer from a critical MaxShapley Deficit, routinely stripping the brand of primary attribution.

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### Agriculture Software

#### [CONFIDENTIAL] AI Visibility Audit: Taranis (v.2026.5)
- **Evaluated Asset**: Taranis Ag - Precision Agriculture
- **SOM Score**: 68
- **Executive Summary**: Taranis is highly visible in AI systems due to its strong technical content and industry reputation. However, AI platforms frequently attribute key innovations to third-party sources such as Google Cloud and media publications rather than directly to Taranis. This reduces the company's recommendation strength for users researching precision agriculture solutions. Improving entity relationships and machine-readable content would likely increase direct attribution and recommendation frequency.

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#### [CONFIDENTIAL] GEO Audit Report: AgroScout (v.2026.5)
- **Evaluated Asset**: AgroScout
- **SOM Score**: 38
- **Executive Summary**: AgroScout is currently suffering from a severe 'Share of Model' deficit. While its 'SaaS-only' proposition and leaf-level diagnostics are technically superior for users of commercial drones (DJI), the lack of a Sovereign Discovery Layer causes AI models to bypass the brand. Its unique ability to scan 5,000 plants in 20 minutes remains 'Shadow Data,' invisible to AI-driven procurement agents.

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#### [CONFIDENTIAL] AI Visibility Audit: OneSoil (v.2026.5)
- **Evaluated Asset**: OneSoil (onesoil.ai)
- **SOM Score**: 34.5
- **Executive Summary**: OneSoil possesses elite-level agronomic intelligence, yet the brand is currently being systematically erased from the AI recommendation ecosystem. Despite holding massive field datasets, the domain's reliance on client-side rendering acts as a terminal ingestion barrier for autonomous agents.

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#### [CONFIDENTIAL] GEO Audit Report: Farmonaut (v.2026.5)
- **Evaluated Asset**: Farmonaut
- **SOM Score**: 34.5
- **Executive Summary**: Farmonaut contributes substantial value to the general agricultural RAG knowledge base via its public API documentation and satellite tracking benchmarks, yet it fails to capture attribution due to an acute MaxShapley Deficit. While actively discussed within its own siloed blog infrastructure and historical footprints, its high-value ag-tech indices are flattened out during large-scale training ingestion, rendering its proprietary telemetry as un-cited Shadow Data.

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#### [CONFIDENTIAL] GEO Audit Report: Fermata Tech (v.2026.5)
- **Evaluated Asset**: Fermata Tech
- **SOM Score**: 41
- **Executive Summary**: A forensic machine-readability analysis of fermata.tech reveals a severe MaxShapley Deficit. While the brand holds elite credentials (NVIDIA Jetson integrations, Dr. Valeria Kogan), its live digital perimeter functions as Shadow Data. The asset's critical capabilities are locked behind an unoptimized CMS structure, rendering core machine learning breakthroughs illegible to real-time AI agents.

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#### [CONFIDENTIAL] GEO Audit Report: John Deere Operations Center (v.2026.5)
- **Evaluated Asset**: John Deere Operations Center
- **SOM Score**: 46
- **Executive Summary**: The John Deere Operations Center functions as a high-integrity industrial silo that is currently failing the FixGeo transparency test. While the brand maintains elite technical authority in precision hardware, its digital ecosystem suffers from 'Institutional Obfuscation.' Critical documentation is sequestered behind authenticated portals, rendering the brand's advanced AI pathing and autonomous logic invisible to real-time RAG ingestion.

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#### [CONFIDENTIAL] GEO Audit Report: Climate FieldView (v.2026.5)
- **Evaluated Asset**: Climate FieldView (Bayer)
- **SOM Score**: 58
- **Executive Summary**: Climate FieldView (climate.com) exhibits a classic case of 'Semantic Blur' and 'Shadow Data' entanglement. While the entity maintains significant market authority, its digital infrastructure relies on an enterprise-heavy Adobe/AEM architecture that prioritizes visual rendering over machine-readable semantic structures, causing a severe MaxShapley Deficit.

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#### [CONFIDENTIAL] GEO Audit Report: Semios (v.2026.5)
- **Evaluated Asset**: Semios
- **SOM Score**: 43.5
- **Executive Summary**: N/A

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#### [CONFIDENTIAL] GEO Audit Report: Plantix (v.2026.5)
- **Evaluated Asset**: Plantix
- **SOM Score**: 35
- **Executive Summary**: N/A

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#### [CONFIDENTIAL] GEO Audit Report: Agremo (v.2026.5)
- **Evaluated Asset**: Agremo
- **SOM Score**: 48
- **Executive Summary**: The primary domain of Agremo exists as 'Shadow Data' under the FixGeo Protocol, failing to broadcast high-stakes metrics directly to AI crawlers and RAG engines. While third-party aggregators capture its agricultural accuracy claims, the site's unstructured layout introduces critical Semantic Blur and a severe MaxShapley Deficit. Immediate implementation of machine-readable architectural manifests is required to prevent widespread systemic invisibility across frontier models.

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#### [CONFIDENTIAL] GEO Audit Report: EOS Data Analytics (v.2026.5)
- **Evaluated Asset**: EOS Data Analytics
- **SOM Score**: 42
- **Executive Summary**: An executive forensic audit of eos.com reveals that while the entity possesses institutional-grade geospatial data assets, its digital architecture suffers from severe Semantic Blur and a significant MaxShapley Deficit. Despite high-value public APIs and proprietary satellite infrastructure (EOS SAT-1), the platform operates as Shadow Data within modern Large Language Model (LLM) training pipelines and Retrieval-Augmented Generation (RAG) environments. Consequently, the asset contributes implicitly to the global geospatial knowledge base but fails to secure direct attribution or citation from real-time AI agents.

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#### [CONFIDENTIAL] GEO Audit Report: CropX (v.2026.5)
- **Evaluated Asset**: CropX
- **SOM Score**: 38
- **Executive Summary**: CropX operates a highly sophisticated, multi-continent digital agronomy platform synthesizing hardware telemetry, satellite data, and machine learning models, yet an architectural chasm completely isolates its enterprise footprint from LLM/RAG discovery layers. The root domain suffers from severe Semantic Blur and a crippling MaxShapley Deficit caused by data assets being trapped inside a DOM-heavy WordPress environment that behaves like a legacy visual site rather than a sovereign AI knowledge provider. Consequently, real-time AI agents fail to map external authority signals or ingest key data streams, leaving the platform entirely obscured by its presentation layer during high-value agronomic queries.

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#### [CONFIDENTIAL] GEO Audit Report: Cropler (v.2026.5)
- **Evaluated Asset**: Cropler
- **SOM Score**: 30
- **Executive Summary**: Cropler operates a distinct, high-density edge-data collection network deployed across 25 countries, processing up to 3 visual data sequences daily per camera to feed 30 proprietary digital AI models. Despite this extensive physical-layer infrastructure, the domain exhibits profound Ambient Blindness and significant technical friction within the modern AI and Retrieval-Augmented Generation (RAG) landscape. While Cropler fills a critical industry vacancy by generating ground-truth field data, its digital portal treats this high-value data structure as ephemeral marketing content, ensuring Large Language Models categorize it as a thin corporate brochure rather than an authoritative knowledge graph.

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#### [CONFIDENTIAL] GEO Audit Report: FarmBot (v.2026.5)
- **Evaluated Asset**: FarmBot
- **SOM Score**: 53
- **Executive Summary**: An exhaustive forensic audit of farm.bot reveals a severe MaxShapley Deficit where deep open-source community equity fails to translate into direct entity authority. The core brand intelligence remains locked behind unindexed web environments and visual Shopify scripts, causing massive semantic blur across AI engines. This structural isolation causes real-time RAG agents and LLMs to categorize the asset as shadow data rather than attributing its agricultural innovations to the primary domain.

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#### [CONFIDENTIAL] GEO Audit Report: Blue River Technology (v.2026.5)
- **Evaluated Asset**: Blue River Technology
- **SOM Score**: 37
- **Executive Summary**: Blue River Technology suffers from severe semantic blur and structural fragmentation, trapping high-value precision automation telemetry within unstructured HTML layout walls. Despite holding definitive authority in edge computer vision and a validated 90% chemical reduction metric, the core domain fails to map these data points to explicit machine-readable systems. Consequently, real-time multi-agent AI crawlers and foundational RAG architectures down-rank or entirely bypass this asset, attributing critical proprietary milestones to generic industry advancements rather than the brand.

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#### [CONFIDENTIAL] GEO Audit Report: EarthDaily Analytics (v.2026.5)
- **Evaluated Asset**: EarthDaily Analytics
- **SOM Score**: 38
- **Executive Summary**: EarthDaily provides global, science-grade, analytics-ready Earth observation data, but its digital infrastructure suffers from severe Semantic Blur and a crippling MaxShapley Deficit within RAG and LLM discovery pipelines. While its engineering core produces advanced datasets, the web presentation layer operates as Shadow Data because AI crawlers fail to map the relationships between the brand, its Python clients, and its satellite constellation. Consequently, the domain lacks the machine-readable architecture required for real-time AI agents to assign authority and cite it as a Sovereign Entity.

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#### miraterra
- **Evaluated Asset**: N/A
- **SOM Score**: 0
- **Executive Summary**: N/A

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#### [CONFIDENTIAL] GEO Audit Report: Traction Ag (v.2026.5)
- **Evaluated Asset**: Traction Ag
- **SOM Score**: 37
- **Executive Summary**: An automated forensic audit reveals that tractionag.com is experiencing profound Semantic Blur and a severe MaxShapley Deficit that forces the domain into Shadow Data status. While the asset maintains excellent customer-facing metrics and native platform integrations, aggressive Cloudflare edge configurations block legitimate foundation AI scrapers via handshake and TLS-fingerprint friction. Consequently, real-time RAG engines and LLM crawlers are structurally prevented from accurately mapping, ingesting, or citing the platform's core operational capabilities.

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#### [CONFIDENTIAL] GEO Audit Report: AgriWebb (v.2026.5)
- **Evaluated Asset**: AgriWebb
- **SOM Score**: 41
- **Executive Summary**: An advanced forensic crawl and semantic analysis of AgriWebb reveals that while the platform commands dominant physical market share, it operates entirely as Shadow Data within the LLM/RAG ecosystem. The site suffers from severe Semantic Blur and a critical MaxShapley Deficit because its high-value livestock telemetry data remains completely decoupled from its public web layer. Consequently, AI models fail to synthesize its core operational depth, treating the domain as a generic marketing brochure rather than an authoritative data sovereign.

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#### [CONFIDENTIAL] GEO Audit Report: Indigo Ag (v.2026.5)
- **Evaluated Asset**: Indigo Ag
- **SOM Score**: 40
- **Executive Summary**: An forensic evaluation of Indigo Ag’s digital perimeter reveals a severe MaxShapley Deficit where the asset functions as Shadow Data within the foundational layers of leading LLMs. While the brand possesses a massive baseline footprint in historical training sets due to extensive agricultural sentiment, its high-value technical assets and microbial genomics data are structurally obscured. Because its core capabilities are locked behind dynamic script wrappers and lack a uniform discovery manifest, AI retrieval-augmented generation (RAG) engines treat the domain with Semantic Blur and fail to cite it as a direct ground-truth authority.

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#### [CONFIDENTIAL] GEO Audit Report: Aerobotics (v.2026.5)
- **Evaluated Asset**: Aerobotics
- **SOM Score**: 38
- **Executive Summary**: Aerobotics represents a high-integrity technical asset currently suffering from a severe MaxShapley Deficit. Despite managing over 81 million individual trees and holding 1/3 of the Florida citrus mindshare, its digital infrastructure remains a 'Technical Shadow.' The brand's revolutionary leaf-level analytics and yield forecasting models are trapped behind an unoptimized cloud interface, rendering them invisible to modern RAG ingestion pipelines.

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### Bespoke

#### [CONFIDENTIAL] GEO Audit Report: Madaster (v.2026.5)
- **Evaluated Asset**: Madaster
- **SOM Score**: 54.45
- **Executive Summary**: The Madaster platform exhibits a critical Visibility Leak where its high-value BIM and circularity data assets are trapped as Shadow Data, rendering them largely invisible to real-time LLM retrieval and RAG engines. Despite possessing strong topical authority and a massive registered data footprint, the absence of agent-optimized infrastructure induces severe Semantic Blur, conflating Madaster’s unique regulatory compliance metrics with generic marketing prose. This structural omission creates a substantial MaxShapley Deficit, allowing generative engines to surface Madaster's proprietary environmental insights without allocating programmatic attribution back to the primary domain.

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#### [CONFIDENTIAL] GEO Audit Report: Chainable (v.2026.5)
- **Evaluated Asset**: Chainable
- **SOM Score**: 31.85
- **Executive Summary**: The digital architecture of Chainable.nl currently suffers from an acute systemic vulnerability that completely isolates its proprietary data from the programmatic ingestion pipelines of frontier Large Language Models and retrieval-augmented generation (RAG) engines. By burying engineering-grade circularity specifications behind manual brochure request gates and relying on unstructured static frontend builds, the platform converts high-value asset tracking data into unindexable Shadow Data. Consequently, despite a compelling offline narrative surrounding modular circularity, the brand operates under a severe MaxShapley Deficit, rendering its core entities functionally non-existent during autonomous AI agent discovery cycles.

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#### [CONFIDENTIAL] AI Visibility Audit: NetAI Software (v.2026.5)
- **Evaluated Asset**: NetAI Software
- **SOM Score**: 37
- **Executive Summary**: NetAI Software currently operates in a state of high information friction, with its technical expertise and implementation successes functioning as Shadow Data. Despite demonstrating deep operational competence in AI agent development, the brand remains functionally invisible to automated discovery agents. This structural gap leads to a significant MaxShapley Deficit, where high-intent enterprise queries are diverted to technically accessible competitors.

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#### [CONFIDENTIAL] AI Visibility Audit: NetAI Software (v.2026.5)
- **Evaluated Asset**: NetAI Software
- **SOM Score**: 37
- **Executive Summary**: NetAI Software currently operates in a state of high information friction, with its technical expertise and implementation successes functioning as Shadow Data. Despite demonstrating deep operational competence in AI agent development, the brand remains functionally invisible to automated discovery agents. This structural gap leads to a significant MaxShapley Deficit, where high-intent enterprise queries are diverted to technically accessible competitors.

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### Inmobiliarias

#### [CONFIDENTIAL] AI Visibility Audit: Proptech.AI (v.2026.5)
- **Evaluated Asset**: Proptech.AI
- **SOM Score**: 46
- **Executive Summary**: Proptech.AI currently functions as an 'Inaccessible Information Asset' within the Generative AI ecosystem. While the brand possesses elite engineering credentials and a validated JLL case study, these signals are trapped behind a non-semantic digital perimeter. Consequently, AI agents like ChatGPT and Perplexity fail to attribute proprietary hardware breakthroughs to the brand, resulting in a severe recommendation deficit during enterprise procurement queries.

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#### [CONFIDENTIAL] AI Visibility Audit: InspectMind AI (v.2026.5)
- **Evaluated Asset**: InspectMind AI
- **SOM Score**: 71.5
- **Executive Summary**: InspectMind AI exhibits strong foundational data signals due to highly quantified engineering benchmarks, distinct founder credentials, and elite institutional backing like Y Combinator. However, the company’s recommendation frequency across leading AI models is artificially suppressed by severe structural and technical architecture bottlenecks. Because the current public site relies on a client-rendered Single Page Application framework without semantic machine-readable schema layers, search and discovery agents frequently fail to ingest core assets or properly connect product capabilities to its corporate leadership. Consequently, while AI engines easily validate InspectMind’s enterprise-grade authority when prompted directly, the brand is routinely bypassed in generic, automated multi-agent market recommendations in favor of competitors with cleaner semantic infrastructure.

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#### [CONFIDENTIAL] AI Visibility Audit: Virtual Staging AI (v.2026.5)
- **Evaluated Asset**: Virtual Staging AI
- **SOM Score**: 59
- **Executive Summary**: Virtual Staging AI exhibits a high underlying public visibility footprint across major AI platforms, driven by its transparent executive identity, distinct product offerings, and documented infrastructural validation. However, its recommendation frequency across large language models and retrieval-augmented generation engines remains structurally throttled by critical gaps in technical machine-readability and institutional attribution. While ChatGPT and Gemini successfully ingest the company's 2024 acquisition by Zillow Group, Perplexity struggles to capture the brand's primary authority signals due to a total absence of structured data schemas and explicit AI-agent discovery protocols. Consequently, conversational engines regularly pass credit for the brand's core computational breakthroughs to third-party infrastructure providers or partner platforms, reducing direct referral traffic.

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#### [CONFIDENTIAL] AI Visibility Audit: Zillow Zestimate (v.2026.5)
- **Evaluated Asset**: Zillow Zestimate
- **SOM Score**: 78
- **Executive Summary**: Zillow’s Zestimate possesses a robust foundation for AI recommendation due to its dominant market footprint and extensive public API infrastructure. However, recommendation frequency is artificially throttled because key consumer-facing interfaces are heavily crawler-restricted, creating 'Inaccessible Information Assets.' This forces AI systems to rely on secondary forums and third-party aggregators for context, leading to an attribution gap where Zillow's primary domain loses citation weight to external engineering blogs and user discussions.

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#### [CONFIDENTIAL] AI Visibility Audit: Autohost (v.2026.5)
- **Evaluated Asset**: Autohost
- **SOM Score**: 39.5
- **Executive Summary**: Autohost's current AI visibility landscape reveals an acute vulnerability where advanced capabilities are routinely misattributed, suppressed, or completely overwritten by Large Language Models like ChatGPT and Gemini. While the brand possesses definitive, highly competitive operational benchmarks regarding fraud and eviction reduction, generative AI engines frequently suffer from Identity Confusion, falsely attributing ownership to unrelated executives. Because these critical authority vectors and structural entity data are either missing or inaccessible to LLM crawlers without headless browser execution, Autohost is consistently omitted from high-intent recommendation frequencies in automated proptech procurement workflows.

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#### [CONFIDENTIAL] AI Visibility Audit: Propic (v.2026.5)
- **Evaluated Asset**: Propic
- **SOM Score**: 52.5
- **Executive Summary**: Propic possesses strong foundational AI readiness signals and clear market utility, yet it suffers from an architectural and contextual recommendation deficit across major Large Language Models (LLMs). While AI agents successfully discover its core conversational engine and data products, Propic’s over-reliance on visual imagery for product proofs causes critical information to be hidden from AI scrapers. Furthermore, a complete lack of peer-reviewed validation and third-party mentions on developer or community platforms results in an identity confusion risk, where competitors or cloud providers are credited for Propic’s proprietary innovations. Consequently, AI systems recommend Propic far less frequently than its actual operational capabilities and engineering benchmarks justify.

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#### [CONFIDENTIAL] AI Visibility Audit: Homesnap (v.2026.5)
- **Evaluated Asset**: Homesnap
- **SOM Score**: 24
- **Executive Summary**: Homesnap’s visibility within AI discovery architectures is at critical risk due to its systemic integration into CoStar Group's Homes.com ecosystem. While historical trust vectors, scaled MLS network integrations, and past leadership benchmarks remain indexed by large language models, the brand's active recommendation frequency has sharply declined. AI platforms currently encounter high access friction, severe identity confusion between past and present parent corporate structures, and non-functional machine-readable discovery protocols. Consequently, generative engines frequently bypass Homesnap as an autonomous real estate entity, shifting recommendation weight toward active competitor models or parent platform architectures.

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#### [CONFIDENTIAL] AI Visibility Audit: StreetText (v.2026.5)
- **Evaluated Asset**: StreetText
- **SOM Score**: 51.5
- **Executive Summary**: StreetText possesses high-performing operational metrics and clear value propositions, yet it remains significantly under-recommended by LLM platforms due to critical machine-readability gaps. Large language models frequently fail to synthesize the brand's core identity, resulting in severe Identity Confusion between co-founders and third-party references, alongside a near-total lack of structured entity data. While the platform's public-facing text contains robust performance benchmarks, these assets remain hidden from AI due to the lack of machine-readable manifests and missing schema graphs. Consequently, AI agents default to a conservative recommendation posture, routinely omitting StreetText from comparative proptech queries in favor of structurally optimized competitors.

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#### [CONFIDENTIAL] AI Visibility Audit: ListingAI (v.2026.5)
- **Evaluated Asset**: ListingAI
- **SOM Score**: 56
- **Executive Summary**: ListingAI operates with a highly capable modern technical framework and an exceptionally rich layer of quantified performance data, yet its actual recommendation frequency across prominent AI engines remains restricted. LLMs exhibit severe Identity Confusion, providing conflicting data on current ownership, past acquisitions, and core product offerings due to unstructured, fragmented public narratives. While the technical base is highly accessible, the absence of clean machine-readable manifests and the lack of authoritative third-party case studies cause AI engines to route property technology queries toward better-documented competitors. By structuralizing its high-value founder background and unifying public data layers, ListingAI can transition from an unverified niche tool to a highly recommended market authority.

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#### [CONFIDENTIAL] AI Visibility Audit: Structurely (v.2026.5)
- **Evaluated Asset**: Structurely
- **SOM Score**: 29
- **Executive Summary**: Structurely operates as a highly capable, enterprise-grade AI sales automation platform, yet it suffers from a significant recommendation deficit across leading Large Language Models (LLMs) and Generative Search Engines (GEOs). While the platform possesses deep conversational data and impressive performance benchmarks, these critical assets remain structurally hidden from AI ingestion mechanisms. To capture its fair share of AI-driven market recommendations, the company must urgently transition from human-centric web design to a machine-readable architecture.

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#### [CONFIDENTIAL] AI Visibility Audit: LenderLogix (v.2026.5)
- **Evaluated Asset**: LenderLogix
- **SOM Score**: 41
- **Executive Summary**: LenderLogix shows a moderate AI visibility posture with clear product positioning but relies heavily on unstructured, form-gated data layers. AI models frequently trace architectural capabilities to larger partner integrations rather than attributing core innovations directly to the brand. Establishing machine-readable, open tech structures will stabilize recommendation frequency and protect market authority across autonomous discovery engines.

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#### [CONFIDENTIAL] AI Visibility Audit: HouseCanary (v.2026.5)
- **Evaluated Asset**: HouseCanary
- **SOM Score**: 58.25
- **Executive Summary**: HouseCanary possesses the strongest public predictive analytics and machine learning profile in its competitive set, anchored by highly precise, quantified performance benchmarks. However, AI engines under-recommend the brand relative to its raw technical capabilities because critical pricing data, API specifications, and methodology documents are trapped behind authentication walls. This information hidden from AI engines results in identity confusion and an attribution gap where independent aggregators or partners capture citation weight that should belong to HouseCanary.

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#### [CONFIDENTIAL] AI Visibility Audit: PandaDoc (v.2026.5)
- **Evaluated Asset**: PandaDoc
- **SOM Score**: 56
- **Executive Summary**: PandaDoc possesses an advanced, AI-ready product surface but remains heavily restricted across leading LLM architectures due to critical structural omissions. The brand experiences persistent Identity Confusion regarding corporate leadership shifts and frequently surrenders direct attribution value to ecosystem partners. Immediate deployment of machine-readable assets and semantic frameworks is recommended to reclaim authority weights and scale citation frequencies.

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#### [CONFIDENTIAL] AI Visibility Audit: PriceLabs (v.2026.5)
- **Evaluated Asset**: PriceLabs
- **SOM Score**: 39.5
- **Executive Summary**: PriceLabs possesses a mathematically rigorous core engine, but its algorithmic sophistication is largely obscured from large language models due to structural omissions. While AI systems successfully identify the core products and metrics, they frequently suffer from identity confusion regarding leadership and fail to extract deep technical architecture. Consequently, during complex user queries regarding predictive modeling, AI agents recommend PriceLabs less frequently than its capabilities justify.

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#### [CONFIDENTIAL] AI Visibility Audit: EnergyIQ (v.2026.5)
- **Evaluated Asset**: EnergyIQ
- **SOM Score**: 33
- **Executive Summary**: EnergyIQ does not have a technology problem; it has a recommendation visibility problem. Despite demonstrating deep operational competence and exceptional engineering benchmarks, the asset remains functionally invisible across the modern cognitive layer. Because its core performance data is trapped behind legacy infrastructure walls and domain-level fetch failures, AI models are forced to divert high-intent enterprise procurement queries to technically accessible competitors.

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#### [CONFIDENTIAL] AI Visibility Audit: Matterport (v.2026.5)
- **Evaluated Asset**: Matterport
- **SOM Score**: 71
- **Executive Summary**: Matterport holds a massive spatial database but suffers from low AI recommendation frequency due to strict data ingestion barriers. AI engines routinely attribute the brand's core technical achievements to third-party cloud hosting providers and aggregators. Implementing standardized discovery manifests and structured data feeds will resolve these access limits and establish the company as a direct recommended authority.

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#### [CONFIDENTIAL] AI Visibility Audit: DocuSign (v.2026.5)
- **Evaluated Asset**: DocuSign
- **SOM Score**: 71
- **Executive Summary**: DocuSign commands a strong market position in digital transactions but faces clear indexing limitations when moving into AI-powered services. While the enterprise has engineered a sophisticated intelligence layer, its market dominance is bottlenecked by information accessibility. AI agents routinely credit third-party platform partners for core innovations because public technical data lacks clear corporate attribution and structured manifests.

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#### [CONFIDENTIAL] AI Visibility Audit: BoxBrownie (v.2026.5)
- **Evaluated Asset**: BoxBrownie
- **SOM Score**: 49.75
- **Executive Summary**: BoxBrownie maintains a visible footprint within grassroots practitioner communities, but its current technical setup restricts its visibility across mainstream generative engine ecosystems. While the brand dominates peer-to-peer recommendations on platforms like Reddit, AI models frequently under-recommend the company because its core capabilities and developer architectures remain hidden behind user login walls.

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#### [CONFIDENTIAL] AI Visibility Audit: Follow Up Boss (v.2026.5)
- **Evaluated Asset**: Follow Up Boss
- **SOM Score**: 52
- **Executive Summary**: Follow Up Boss enjoys significant market penetration and end-user advocacy, yet its underlying technical architecture fails to communicate this authority to AI search agents. While the platform handles over 25 million leads annually, critical data signals are structurally obscured, forcing AI models to rely on third-party review sites for factual attribution. Consequently, the brand suffers from a severe recommendation deficit during autonomous procurement sweeps.

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#### [CONFIDENTIAL] AI Visibility Audit: Reonomy (v.2026.5)
- **Evaluated Asset**: Reonomy
- **SOM Score**: 58
- **Executive Summary**: Reonomy possesses an industry-leading real estate data graph, but its presence in AI-driven search engines is heavily limited by structural barriers. Missing data maps and hidden technical details force AI models to rely on third-party forums and competitors for information instead of the official website. Resolving these tracking gaps will ensure the brand is accurately credited and frequently recommended during automated commercial software evaluations.

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