Phase I: Collection
Intent Saturation Analysis
We sample broad user-intent patterns across major LLMs to map the current ranking landscape around a topic or market.
AI Search Presence | Research Program
This project examines how large language models synthesize brand evidence and how those mechanisms shape citation, ranking, and authority formation across generative search environments.
Phase I: Collection
We sample broad user-intent patterns across major LLMs to map the current ranking landscape around a topic or market.
Phase II: Diagnostics
We identify the missing proof points and evidence signals that constrain how strongly an entity is retrieved, interpreted, and cited.
Phase III: Strategy
The resulting analysis is synthesized into a structured interpretation of content, source, and narrative conditions associated with stronger visibility.
Phase IV: Outcome
Subsequent observations can be used to evaluate changes in ranking position, citation presence, and authority across the target topic.
Learn More
Axiom is a research initiative studying how pretraining and contextual signals shape LLM ranking, and how those rankings can be influenced in principled ways.
Visit the Axiom research overviewSubmit the following information to receive your report.
The email you wish to receive the report: name@organization.com
Tell us about the brand you wish to analyze
Enter the main brand name exactly as you want it analyzed.
Enter the main website domain for the brand, without additional paths.
List any owned URLs you want included, separated by commas.
If left blank, we will use the top 3 most frequently appearing brands as competitors.
We will create the report by analyzing queries that your target customer will pose on an AI search engine. To compose these queries answer the following questions
Specify who is conducting the AI search: e.g., director of marketing
e.g., Architecture, Engineering and Construction. Leave empty for any industry.
Define what this persona is trying to accomplish in their search.
Enter the authenticated run password to start generating the report.
Review the information below, then confirm to start the analysis.
About Axiom Research Project
Axiom Research Project is grounded in a formal research approach to understanding how AI ranking systems evaluate brands. Rather than focusing only on final mentions, it studies how model priors, retrieved evidence, and competitive context interact to determine who is surfaced, who is cited, and who is omitted.
This matters because AI discovery is not a simple mirror of traditional search rankings. Models combine learned brand familiarity with the evidence available at the time of response, so visibility can emerge from very different mechanisms. The project therefore distinguishes durable reputation effects from signals that remain sensitive to new evidence, source coverage, and narrative framing.
The same research also informs how Axiom Research Project studies competitive pressure, showing where brands truly compete in AI-generated rankings, where evidence can shift the result, and where the market appears relatively stable. The motivation is to make generative-search behavior more legible: to separate what is structurally persistent from what is empirically movable, and to provide a clearer basis for interpreting AI visibility outcomes.
Sample Reports
CMO Executive Summary & Data-Driven Intelligence
Analysis Perspective: All metrics and comparisons in this report are presented from Frontier's point of view. Positive values indicate Frontier's advantage; negative values indicate competitor advantage.
Data Source: All results obtained by executing 100 random queries from the fiber_internet_queries query set on ChatGPT.
Measures your brand's "reputational moat" by quantifying how much an AI ranks you based on your established name recognition rather than relying on the specific content it reads. A high score means the AI consistently favors your brand from its training data alone — your reputation is baked into the model's knowledge and is hard for competitors to displace with new content. A low score means the AI is easily swayed by whatever snippets it encounters, leaving your ranking vulnerable. The higher the score, the better — AI relies more on its training to rank you.
Identifies your easiest competitive targets by measuring how vulnerable a rival brand's ranking is to being displaced by new, targeted content.
Reveals the competitive imbalance between two brands by showing whether your brand is easier to knock down in the rankings than your competitor, or vice versa.
Cross-competitor analysis for Frontier identifying patterns, opportunities, and recommended actions.
Frontier ranks #3 of 4 competitors. Vulnerable targets: Metronet
Attributes where Frontier wins across competitors
Attributes where Frontier loses across competitors
Focus PR efforts on these sources for immediate impact
Frontier's Reputation Moat: Rank #3
High Market Fluidity. AI is listening - you can steal share.
No competitor shows significant structural AI bias against you.
Clear differentiator vs T-Mobile.
Generate content on Apps, Communication, Network Quality, Response Time, Responsiveness to improve your AI rankings
Mean Reputation Moat per Brand (Higher is Better)
Strategic Landscape Map
High Market Fluidity, Low Unfair Advantage: The AI is listening. You can steal market share immediately with the right content strategy.
High Unfair Advantage: Competitor has structural AI bias advantage. Copying their strategy won't work - need differentiation.
Low Market Fluidity: AI ignores new evidence. Rankings are stable, long term content strategy is needed.
Competitors ranked by how easy they are to displace with the right content strategy. Green = displace in the short term (work down in order). Red = long-term strategy required.
Deep dive by competitor. Green bars = Frontier Strength. Red bars = Frontier Weakness.
Which attributes matter most for ranking
Green = Frontier wins | Red = Frontier loses
Three dimensions of source intelligence: source responsiveness to content changes, overall influence priority, and owned media uplift opportunity.
Content on the platforms below will rapidly alter AI rankings
Highly influential publications for rankings, content team should focus their efforts in these sources.
Owned domains and topics where new content would most improve your AI rankings
Click View Brief on any topic for an actionable content recommendation
Distribution of source citations across owned and third-party media
Ranked by citation frequency across all queries
AI Visibility Intelligence & Content Strategy
Data Source: All results obtained by executing 100 queries on ChatGPT. Workorb appeared in 2.0% of results.
How often each brand and source domain appears across 100 AI search queries
Percentage of queries where each brand was mentioned
Share of total domain citations across all queries
Actionable content briefs for Workorb's owned media to improve AI visibility
Competitors like ContraVault (contravault.com), Shred.ai (openasset.com), and others claim to shred massive RFPs into structured, actionable checklists and extract key requirements, obligations, deadlines, and risks, turning thousands of pages into a single source of truth and structured insights for bid teams.
Publish a granularity-focused guide that walks buyers through how to convert long RFP documents into structured, reusable outputs. Include a clear data model diagram showing how requirements, obligations, deadlines, and evaluation criteria are organized, enriched with context, and tagged by industry, use case, and client persona. Add a downloadable sample output (redacted RFP excerpt with structured fields) and a video walkthrough comparing a raw PDF to its structured output. Include a brief buyer’s checklist explaining how granular outputs accelerate decision-making and improve bid positioning, and a comparison section that explains where Workorb delivers deeper structuring than generic scanners.
Competitors like ContraVault, Shred.ai, Cassidy AI, DeepRFP, Stellis AI, and McCarren AI tout automated shredding, automatic extraction of requirements and risks, and rapid analysis of RFPs—often described as processing in minutes or instantly.
Publish a practical automation playbook that demonstrates how Workorb automates RFP intake, shredding, and extraction into a ready-to-use bundle of structured content. Include a simple, repeatable 5-step automation template, a sample RFP with annotated automated outputs, and a mini-case study showing reduction in time to first draft. Add a CTA for a live demo or interactive sample to show speed and accuracy in action.
Competitors claim AI can shred massive RFPs into structured checklists and extract requirements, deadlines, and risks to provide context-enriched, actionable insights, often with tools tailored to specific industries (e.g., AEC, government contracting) and, in some cases, in-editor drafting via Word add-ins.
Publish a comprehensive owner’s guide and live-demo content hub titled 'Workorb AI-Powered Proposal Generation: From Raw RFP to Structured Proposal Content.' Include a step-by-step walkthrough of ingesting an RFP, performing extraction, tagging outputs by industry and client persona, and delivering ready-to-use proposal content. Add an in-depth video walkthrough, sample output trees, and a downloadable template set that shows how Workorb organizes solutions, case studies, technical responses, and compliance statements. Include a comparative section that highlights how Workorb’s approach differs from the cited competitors (without naming them by claim) and emphasize native content reuse and governance features that facilitate rapid response across teams.
Competitors such as ContraVault, Shred.ai, Cassidy AI, DeepRFP, Stellis AI, and McCarren AI advertise end-to-end capabilities: AI-driven shredding of RFPs, extraction of requirements, obligations, and risks, cross-referencing with historical bid data, and even in-editor drafting or compliance matrices.
Publish an capabilities-focused piece that outlines an end-to-end RFP processing workflow and shows how structured outputs feed downstream bid activities. Include a capability map (ingest → shred/extract → cross-reference/history → output generation) and a side-by-side comparison with common competitor approaches to highlight unique strengths. Feature short customer quotes or anonymized workflow diagrams to illustrate how Workorb supports faster, more reliable bid decision-making.
Competitors emphasize centralized proposal content and workflows with integration-friendly features (e.g., content repositories and in-editor drafting), suggesting benefits for CRM-driven bid processes, though explicit CRM integration claims are not deeply detailed in the snippets.
Publish a dedicated piece 'CRM-First Bidding with Workorb' showcasing native CRM integrations, bi-directional data flows, and automated population of proposals from CRM data. Include a practical, step-by-step integration guide for Salesforce and HubSpot, a demonstration video of pulling client data into a bid and auto-updating proposal status, and a customer story focusing on improved win rates through CRM-driven content personalization. Offer an API overview and a starter code snippet to encourage developer adoption.
Competitors describe AI that not only extracts requirements but enriches them with context, cross-references with historical bid data, and delivers structured insight to start from a richer base than raw PDFs.
Launch a content series titled 'Enrichment in Action: Turning RFP Outputs into Context-Rich Proposals.' Include before/after samples showing raw input versus enriched outputs, a deep dive into tagging taxonomy (industry, use case, client persona), and a live example of an enriched compliance statement or technical response. Add a downloadable enrichment playbook that demonstrates how context, historical data, and cross-referenced obligations refine the bid strategy. Feature a customer quote or use-case illustrating faster, more accurate responses due to enrichment.
Competitors highlight AI-assisted risk and obligation extraction, compliance matrices, and go/no-go decision support to improve bid risk management and positioning within regulated contexts.
Publish a definitive guide 'Safe and Compliant RFP Automation with Workorb' detailing how Workorb handles data privacy, access controls, audit trails, and exportable compliance matrices. Include a security-focused feature explainer, data handling workflows, and governance templates that illustrate how teams maintain compliance across bids. Add a comparative explainer that positions Workorb’s safety features against common industry expectations highlighted by competitors, using neutral language and clear visuals.
The provided evidence does not explicitly describe on-prem/private cloud deployments; it centers on RFP shredding, analysis, and in-editor drafting features (e.g., Word add-ins) and government-contracting use cases.
Create a deployment and security guide titled 'Workorb on Prem / Private Cloud: Architecture, Security, and Migration.' Explain deployment options, data isolation guarantees, compliance considerations, and how to migrate from legacy systems. Include architecture diagrams, a step-by-step upgrade path, and a business case demonstrating when on-prem/private cloud makes sense for regulated customers. Add a comparison section that contrasts on-prem/private cloud readiness with common cloud-centric claims from competitors.
Competitors emphasize rapid RFP shredding, extraction, and analysis with tools that offer in-editor drafting capabilities (e.g., Word add-ins) and centralized content repositories, underscoring integration-friendly workflows across document and collaboration platforms.
Create a practical, solution-focused content piece titled 'Workorb Integrations: How to connect RFP shredding and proposal automation with your CRM, ERP, and document platforms.' Develop a 3-part guide: (1) an integrations overview showing supported systems and data flows, (2) a hands-on workflow example (pull in RFP data from a CRM, generate proposal content, push outputs back to the CRM), and (3) a developer-ready API/SDK teaser with sample endpoints. Include a comparison matrix highlighting where Workorb exceeds or differs from typical Word-add-in workflows and showcase an implementation timeline and success metrics.
Competitors highlight broad coverage and access to varied sources and data, including government contracting signals and diverse RFP ecosystems (e.g., McCarren AI emphasizing federal/state/local opportunity discovery).
Publish a coverage-focused article or interactive map that explains how Workorb supports RFPs across multiple sources and use cases. Include guidance on prioritizing opportunities across government, industry-specific bid pools, and cross-industry templates. Include a glossary of source types and a workflow for validating coverage relevance to a bid opportunity.
Competitors emphasize explainability by providing enriched context and structured insight on top of raw PDFs, enabling teams to understand how outputs were derived (e.g., context enrichment, structured requirements, and cross-referenced data).
Publish an explainability guide that details how Workorb turns shredded outputs into auditable, context-rich content. Include examples showing the lineage of a requirement from source document to final structured item, plus a lightweight transparency diagram for how decisions were made. Create a template for auditors or reviewers to trace outputs back to source sections and scoring criteria.
Competitors focus on government-focused capabilities and access to federal/state/local signals, underscoring governance of bid data and sources (e.g., McCarren AI’s government contracting orientation).
Publish a data governance and sovereignty piece that discusses how Workorb handles source provenance, data governance for government bids, and compliance with government data requirements. Include a framework for source validation, access controls, and auditability. Add a short Q&A; with implementation tips for agencies or contractors prioritizing data sovereignty.
Competitors advertise automated compliance support: matrices, statements, and in-editor drafting tools designed to ensure bids meet requirements, with examples like Cassidy AI and McCarren AI.
Publish a compliance-first guide that details how Workorb supports regulatory and client-specific compliance through structured outputs, matrices, and traceability. Include templates for compliance statements and a sample compliance matrix with annotated outputs. Add a checklist for reviewers to verify alignment with RFP requirements and contract terms.
Competitors emphasize rapid processing and no-delay outputs (e.g., shredding and analysis in minutes or instantly), implying fresh, up-to-date insights from RFPs as soon as they are ingested.
Publish a speed-to-insight benchmark and a freshness-focused article showing how Workorb delivers near-immediate structured outputs from new RFPs. Include a before/after scenario highlighting how quickly a new RFP content is transformed into actionable materials. Offer a time-based demo or live session to illustrate responsiveness.
Independent third-party domains that influence AI rankings on this topic — pursue content publication here
The domains below are editorial, community, or media sites where Workorb can publish content to influence AI ranking outcomes.
Long-tail queries where AI has limited information — Workorb can dominate these search results with targeted content
These queries have very low coverage on both Google and AI search engines. By creating authoritative content on these topics, Workorb can establish a dominant position in AI-generated answers.
“RFP & Proposal AI Evaluator vs manual proposal drafting for construction firms: a comparison”
The suggested content angles are tailored to address the specific needs and challenges faced by construction firms in proposal drafting, offering practical solutions and insights that align with the searcher's intent. By focusing on time savings, quality improvements, and real-world applications, these angles are likely to resonate with the target audience and outperform existing content on the topic.
“AI proposal automation vs manual proposal process: which increases win rate and revenue for construction firms”
The suggested content angles are tailored to address the specific query by providing actionable insights, real-world examples, and data-driven comparisons between AI-driven proposal automation and manual processes, which are key factors influencing win rates and revenue in construction firms. This approach aligns with the searcher's intent and leverages authoritative sources to enhance credibility and relevance, positioning the content to rank highly for the given query.
“best AI-driven proposal automation solutions for architecture, engineering, and construction firms”
The suggested content angles are tailored to address the specific needs of architecture, engineering, and construction firms seeking AI-driven proposal automation solutions. By focusing on practical applications, integration strategies, and real-world case studies, these angles aim to provide actionable insights and demonstrate the tangible benefits of adopting such technologies, thereby aligning with the searcher's intent and outperforming existing content.
“comparison of RFP compliance checking tools for construction bids”
The suggested content angles are tailored to address the specific needs of construction companies seeking RFP compliance checking tools, providing practical insights and solutions that align with their challenges and objectives.
“how to implement AI-driven business development for AEC firms to win more RFPs and bids”
These content angles address the specific needs of AEC firms seeking AI-driven solutions for business development, focusing on practical implementation strategies, integration with existing workflows, and real-world case studies to demonstrate effectiveness. This approach aligns with the searcher's intent and provides actionable insights, which are likely to rank well for this query.