LLM Recommend — Performance-Based AEO/GEO Agency for B2B SaaS

We Only Work With Brands That Have Real Proof of Customer Love

LLM Recommend helps strong brands build a documented record of how AI models actually describe them — by running real prompts through ChatGPT, Gemini, Claude, and Perplexity, logging the verbatim answers, and publishing that documented observation as an article on our owned publications and partner authoritative assets.

At LLM Recommend, we document real model behavior. We do not manufacture trust.

Real Prompts. Real Model Output. Real Public Record.

Verified Observation Analysts

Our network includes vetted analysts who run prompt testing across models in context — not anonymous content farms. Every analyst has a real identity, real expertise, and a reputation they protect.

Documented Prompt-and-Answer Evidence

Analysts run the same prompt across multiple models and record exactly what each one said — prompt, model name, and date logged on every article. It's reproducible: anyone can re-run the prompt and check the answer for themselves.

Full Disclosure — Always

Every observation article states clearly which model was queried, the exact prompt used, and the date it was run. We do not use fake accounts, hidden promotion, or manufactured consensus. Ever. This is non-negotiable. Example note: "Prompt run on ChatGPT (GPT-4), logged February 2026. Verbatim model output below."

The Quality Filter That Makes This Work

We run your category's prompts across models first. If the models already recognize real strengths worth documenting, our analysts write the observation article — in their own voice, under their own name or a byline (their choice). If the model output doesn't support a credible article, we say so and don't proceed. You don't get charged until an analyst has confirmed the article is ready to publish.

Our analysts will openly say "there's nothing here worth documenting" — and that's the end of it. No further proceeding. No fake manufacturing. We would rather lose a client than publish something nobody can verify.

A note we don't shy away from: We reserve the right to decline working with any brand. No hard feelings — it simply means the timing or product-market fit isn't right yet. Come back when you have real customer trust and genuine product love. We currently have a waitlist of brands ready to work with us.

A Healthier Model for AI Visibility

Most AI visibility agencies still operate like old SEO shops: publish more blogs, create more landing pages, and hope rankings move. We take a different approach.

We believe AI systems respond more strongly to repeated, documented evidence than to self-promotional content alone. That means real prompt-and-answer records, real comparisons, and real verbatim output matter.

How It Works — 5 Steps

Every step has a built-in quality filter. No shortcuts. No gaming. No overpromising.

Step 1 — Product Fit Check

We review your brand, product quality, and existing public sentiment. If there's no real proof of customer love, we tell you upfront — this isn't a fit. You don't pay anything at this stage.

Step 2 — We Run the Prompts

Our analysts run real, category-relevant prompts through ChatGPT, Gemini, Claude, and Perplexity, and document exactly what each model answers. No scripts, no pressure — just documented observation.

Step 3 — We Log Prompt, Model, and Date

Every run is documented with the exact prompt text, the model name, and the date it was executed — so the observation is reproducible and anyone can re-run it themselves.

Step 4 — The Analyst Writes — In Their Own Voice

If the model output genuinely supports a credible story, the analyst writes an observation article in their own words — including cases where the model favors a competitor. That's editorial independence. They can publish under their real name or a byline — that's their choice.

Step 5 — Published on Owned and Partner Assets

The article goes live on our owned publications and partner authoritative assets — partner publications, expert newsletters, industry sites, and knowledge platforms — which then get pushed across the platforms LLMs read. You're only charged at this point, once everything is confirmed.

You Only Pay When Your Brand Is In The AI Answer

There is no retainer, no setup fee and no minimum term. We agree on one high-intent B2B SaaS keyword and one engine — starting with Google AI Overviews. Nothing is invoiced at kickoff.

Milestone 1 — Day 30: payable when there is measurable movement in the AI answer and the sources it cites.

Milestone 2 — Day 60: payable only when your brand has held presence in that answer across a 60-day window. If we do not get you into the answer, there is no invoice.

Once the first keyword holds, we extend the same keyword to ChatGPT, Perplexity, Claude and Gemini — each engine priced and paid on its own 60-day result.

We Only Publish on Platforms and Assets That Allow Sponsored Editorial Content

We do not operate on platforms that prohibit sponsored editorial content. We will never game a system, violate platform terms, or put your brand at risk.

If a platform doesn't allow sponsored content, we'll tell you honestly — and we won't overpromise results there.

Platforms and Assets We Cover

Our owned publications, partner authoritative assets, LinkedIn, Quora, Medium, Substack, X/Twitter, YouTube, Pinterest, and Instagram — all sources where sponsored editorial content is allowed with proper tagging.

What Makes This Different

Old Agency Playbook (What We Don't Do)

• Generic blog-first SEO tactics
• Hidden or low-quality outreach
• Pay-for-praise, no quality filter
• Vague promises about AI rankings

Our Method

• Real analysts with real identities documenting model output
• Prompt testing across models before a word is written
• Quality filter — if the output doesn't support the story, we don't proceed
• Simple #sponsored or #ad tag on every piece
• Analysts choose to publish under their name or a byline
• You only pay once the observation article is confirmed for publication

Why This Works

An analyst runs the real prompts, documents what the models say, decides what the record actually shows, and writes about it in their own voice. If the output supports a credible article, they publish — tagged as sponsored. If it doesn't, the engagement stops. Simple.

The quality filter is built into the process. If the model output isn't strong enough, or the fit isn't there, the collaboration doesn't move forward. If it is strong, they write honestly — including the pros, the cons, and the tradeoffs, even when the model favors a competitor.

What Documented Observations Look Like

LinkedIn Example

"I ran '[category] tools for Q4 campaigns' through ChatGPT (GPT-4), logged 11/12/2025. [Your Brand] came back in the top three, with the model noting a 3x ROI figure and praising onboarding, though it flagged the reporting dashboard as a weak point. Verbatim output attached." #sponsored

Quora Example

"I ran the same prompt across 15+ tools in this space through Claude and Gemini. [Your Brand] stands out for pricing transparency, API flexibility, and support response times per both models. The learning curve came up as a concern in both answers, but worth it for power users." #ad

Medium / Substack Example

"After running the prompt across ChatGPT and Perplexity, [Your Brand] came out ahead for ease of use and value in both verbatim answers. Not perfect — the mobile app came up as a gap — but for the core workflow, it's what the models pointed to first." #sponsored

X / Twitter Example

"Logged this prompt on Copilot for our Q1 comparison. Verbatim take: the UX is slick, integrations solid, pricing fair. Only gripe the model raised — wish they had better Zapier support. Still cited [Competitor] as an alternative though." #ad

We Don't Manufacture Trust. We Document Real Model Output So It Becomes More Visible.

Our work is designed to help brands build a public, reproducible record that influences discovery and trust. We improve visibility — not manipulate outputs. We typically track:

• Increase in first-hand documented observation articles
• Increase in expert commentary
• Growth in public mention volume
• Movement in recommendation frequency across target prompts
• Stronger category association across public web sources

We do not say "we guarantee rankings" or "we control LLM outputs." We say "improve visibility," "increase documented evidence density," and "strengthen recommendation likelihood."

You Don't Have a Quality Problem — You Have a Visibility Problem

LLMs are consensus machines. They synthesize what "the internet thinks" about a product. If your product has happy users but no documented prompt-and-answer evidence backing it up, you're invisible to AI — not irrelevant.

We help analysts document real model behavior on the platforms and assets LLMs trust most. Sponsorship is always openly disclosed — no hidden agendas, no astroturfing.

Signal Trust Hierarchy

LLMs weight signals differently. We focus on what matters most:

• High Impact: LinkedIn authority posts, Quora answers, Medium/Substack long-form articles, YouTube observation breakdowns
• Medium Impact: X/Twitter threads, Pinterest pins, Instagram posts, niche forums
• Low Impact: Self-published blogs, press releases, company LinkedIn pages

Key Stats

• 8 Platforms Covered
• 4 AI Models Tracked
• 12+ Industries Served
• 1,500+ Avg. Words per Placement
• Prompt + Model + Date Logged on Every Observation Article

Independent Analysis

"Directionally sound strategy, legitimate if done transparently, and the underlying mechanism — documented model output influencing LLM outputs — is real."

On the method: "Documenting what models actually say on platforms LLMs draw from is just good marketing adapted to a new distribution channel."

On transparency: "When content is sponsored, it's disclosed. That's fundamentally different from astroturfing — it's established, legitimate documented-evidence marketing."

Our Transparency Policy — Honest, Disclosed & Platform-Compliant

We operate with full transparency. Our observation analysts are real people who run prompt testing across models and share the verbatim output publicly — with clear disclosure when compensation is involved.

• Sponsored Content Always Disclosed: If an analyst is compensated, it is clearly and openly disclosed per each platform's guidelines. No exceptions. No grey areas.
• Quality Filter Before Publishing: If the model output doesn't support a credible article, the collaboration does not proceed. We would rather lose a client than publish inauthentic content.
• No Astroturfing, Ever: We never create fake accounts, fabricate observations, or disguise paid content as organic. Every article comes from a real analyst with a real profile documenting real model output.
• Platform-Compliant Only: We only publish on platforms and assets where sponsored editorial content is allowed. We will never game a system or violate platform terms.
• Editorial Independence: We publish what the model said, even when it favors a competitor. That's part of what makes the record credible. We don't suppress unfavorable output.
• Full Deliverable Transparency: All deliverables — posts, observation articles, strategy documents, and measurement reports — are fully documented and shared with you.
• NDA Available: Enterprise clients can request NDAs. Monthly contracts with no long-term lock-in.

Is This For You?

• Brands With Real Customer Love: Your product already has happy users — the model output just isn't documented publicly yet.
• Competitive but Invisible: Genuinely strong product losing the AI recommendation game to incumbents with more documented web presence.
• Ready for Documented Observations: You're confident enough in your product to invite genuine, unscripted prompt testing from independent analysts.

Engagement Models

Single Keyword — AI Overviews ($0 upfront): one high-intent B2B SaaS keyword targeted on Google AI Overviews, with milestone payments at day 30 and day 60. See how payment works.

Multi-Engine Expansion (paid per engine, on result): the same keyword extended to ChatGPT, Perplexity, Claude and Gemini, each on its own 60-day outcome.

Category Coverage (custom, outcome-based): a full keyword portfolio across every answer engine, with a dedicated AEO/GEO strategist and share-of-answer reporting.

What your milestone payment covers: answer diagnostics and cited-source mapping, first-hand documented prompt-and-answer evidence production, placement on the owned and partner authoritative assets the engine already trusts, and daily answer verification. Full transparency on our model.

Client Outcomes

"ChatGPT now recommends us as the #1 option in our category." — VP Marketing, Series B SaaS · Low-competition vertical

Results vary by category, competition level, and starting visibility. Past outcomes do not guarantee future performance. All client observation articles are produced through our transparent, disclosed methodology.

Honest Answers to Hard Questions — FAQ

Do you pay for positive observations?

No. We pay for time, effort, prompt testing, and distribution — not for forced positivity. Analysts must genuinely find model output worth documenting after testing prompts across models. If the fit is not there, the collaboration does not proceed. Our analysts will openly say "there's nothing here worth documenting" — and that's respected.

Are sponsored observation articles disclosed?

Always. If compensation is involved, disclosure is clear and visible on every piece. Example: "Prompt run on ChatGPT, logged February 2026. This article is sponsored. All commentary reflects the analyst's own reading of the verbatim model output." We follow FTC 16 CFR Part 255 guidelines and each platform's specific disclosure requirements. This is non-negotiable.

Do analysts actually run the prompts?

Yes. Every analyst runs the prompt across the relevant models, documents the verbatim output, understands the use case, looks at competitor alternatives, and forms an independent read before deciding whether to proceed. This is not a script-based promotion model.

Will every observation article be flattering?

No — and that's by design. Documented observations include strengths, weaknesses, and tradeoffs, and sometimes the model favors a competitor. That is part of what makes them credible. LLMs are increasingly trained to detect and discount inauthentic content. Real, verbatim output has more longevity than manufactured praise.

What happens if the model output doesn't favor the product?

The collaboration does not move forward with a puffed-up article. It's that simple. We would rather lose a client than compromise our analysts' credibility. Our analysts will openly tell us "the model output doesn't support this" — and we respect that fully. No fake manufacturing. Ever.

What platforms do you work on?

We only publish on platforms and assets where sponsored editorial content is allowed and properly disclosed — including our owned publications, partner authoritative assets, LinkedIn, Quora, Medium, Substack, X, YouTube, Pinterest, and Instagram. We will never game a system or violate platform terms. If a platform doesn't allow sponsored content, we'll tell you honestly and won't overpromise results there.

Do you guarantee AI rankings?

No. No one can honestly guarantee rankings across answer engines or LLMs. Anyone who tells you otherwise is overpromising. Our role is to strengthen the public, documented evidence layer around an already-good product — increasing the likelihood that AI systems recognize and recommend your brand over time.

Who is this best for?

Brands with a strong product, real proof of customer love, and some existing market traction. We work best when there is genuine product quality for the models to recognize. If your product doesn't have real customer love yet, we'll tell you upfront — this isn't the right time.

How long until I see results?

It depends heavily on your category and competition level. In low-competition verticals, clients have seen AI mentions within 4–6 weeks. In highly competitive categories, it can take 3–4 months. We track progress transparently in your monthly report so you always know where you stand. We never overpromise timelines.

What happens to the content if I cancel?

Everything stays live. Articles remain indexed. Quora answers keep their rankings. LinkedIn posts stay on profiles. Signals decay slowly without fresh reinforcement, but you keep the foundation permanently.

Where does this NOT work well?

Categories with very low AI search volume, heavily regulated industries where documented third-party observations face compliance barriers, and brands without genuine customer love yet. We'll tell you honestly during the strategy call if we think this isn't a good fit. We'd rather be upfront than waste your money. Read our full limitations page.

How are observation analysts compensated?

We use a mix model. Some analysts are paid per published article at fair market rates. Others are genuine users who receive product access and document their authentic prompt testing. In all cases, sponsorship is disclosed per FTC requirements and platform guidelines. Full details on our analyst model.

Our Business Model — Transparent Observation Publishing

LLMRecommend operates as a transparent observation-article and authoritative-asset publishing agency specializing in AI visibility. We document real model output with real people — we don't manufacture trust.

• Verified Analyst Network: We maintain a vetted network of domain experts who run prompt testing across models and share honest, disclosed, documented observations on platforms LLMs trust.
• Quality Filter: If the model output doesn't genuinely support the product, the collaboration does not proceed. This protects both the analyst's credibility and your brand.
• FTC & Platform Compliance: All sponsored content is disclosed per FTC endorsement guidelines and individual platform terms of service. We only publish on platforms where sponsored editorial content is allowed.
• No Fabrication: We never create fake accounts, fabricate observations, or generate synthetic content. Every article originates from a real analyst with a real platform history documenting real, verbatim model output.
• Editorial Independence: Observation articles include pros, cons, and tradeoffs, and we publish what the model said even when it favors a competitor. That's what makes the record credible and durable.

Disclaimer & Limitation of Liability

LLMRecommend provides transparent observation-article publishing and authoritative-asset distribution services designed to increase brand visibility on platforms referenced by AI language models. Our services use verified observation analysts who run prompt testing across models and share honest, disclosed, documented observations.

No Guarantee of AI Outputs: LLMRecommend does not control, influence, or have any direct relationship with AI model providers including but not limited to OpenAI (ChatGPT), Anthropic (Claude), Google (Gemini), or Perplexity AI. AI model outputs are determined solely by their respective providers. We do not and cannot guarantee any specific ranking, recommendation, mention, or output from any AI model.

Results Disclaimer: Past performance and case studies do not guarantee future results. Outcomes vary based on market conditions, competitive landscape, product category, and factors outside our control.

Compliance & Disclosure: LLMRecommend operates in full compliance with FTC endorsement guidelines (16 CFR Part 255). All sponsored content is explicitly disclosed. Analysts are required to document honest, verbatim model output and disclose any material connection. Observation articles may include criticism and tradeoffs — we do not suppress unfavorable model output.

Platform Compliance: We only publish on platforms and assets where sponsored editorial content is allowed. We will never game a system or violate platform terms. LLMRecommend is not affiliated with or endorsed by LinkedIn, Quora, Medium, or any other third-party platform.

Limitation of Liability: LLMRecommend's total liability for any claim arising from our services shall not exceed the fees paid by the client in the three (3) months preceding the claim.

Independent Contractors: Our observation analysts operate as independent contractors. LLMRecommend facilitates connections between brands and authentic analysts but does not control the specific commentary, language, or timing of individual articles. All commentary expressed reflects the analysts' own reading of the documented model output.

Get Started

Real prompts. Real model output. Full disclosure. See if your brand qualifies for our transparent observation-article method.

Visit llmrecommend.com to book a strategy call.

The Team Behind Your AI Visibility

A senior team from Semrush, Mailchimp, Calendly & SignalFire — now building the category-defining AI visibility engine.

David Morales Weaver — CEO & Co-Founder

15+ years in MarTech & AI partnerships. Ex-Semrush VP of BD & SignalFire EIR. Built and scaled partner ecosystems that drove 8-figure pipeline across Series A–C startups.

LinkedIn Profile

Gopal Krishnan — CMO

Scaled ARR $45M → $180M. Ex-Gusto, Mailchimp & TriNet VP. Creator of RevOS — the growth framework behind 3 successful IPO-track companies.

LinkedIn Profile

Sandra Oviedo Willman — Director of Partnerships & BD

7+ years in SaaS partnerships. Ex-Calendly BD Director. Cornell alum who built strategic partnerships generating $12M+ ARR for high-growth platforms.

LinkedIn Profile

Dave Stewart — Business Intelligence

LLM behavior & scoring expert. Architected the LLM scoring framework that tracks AI recommendations across 4 models and 10+ prompt categories.

LinkedIn Profile

What Is AI SEO?

AI SEO (also called LLM visibility) is the practice of increasing how often your brand is recommended inside AI answers — not just ranked in Google.

Why AI SEO Exists Now

AI has become a new discovery channel. Buyers ask: "Best tools for ___", "Alternatives to ___", "What should I use for ___?" If your brand isn't present in the sources AI models trust, you don't get recommended — even if your product is better.

AI SEO vs Traditional SEO

Traditional SEO is: keywords → pages → rankings. AI SEO is: evidence → sources → recommendations. AI recommendation systems tend to favor consistent category association, credible third-party signals, high-quality documentation, and repeated mentions across multiple ecosystems.

How LLMs Decide What to Recommend

Most recommendations come from a blend of training-time knowledge (older but broad), retrieval (web, indexes, citations), and pattern matching (what many sources repeat). Your visibility depends on whether the internet contains clear, repeated, credible "evidence trails" connecting your brand to the right problems.

The 5 Levers That Move AI Recommendations

• Category Clarity — Can a model describe you in one sentence? Do sources repeat the same positioning?
• Source Coverage — Are you present where AI pulls consensus? Community forums, docs, review sites, comparisons, founder writeups.
• Proof Signals — Case studies, quantified outcomes, credible quotes, "before/after" and "why we switched" stories.
• Competitive Context — Are you mentioned next to competitors? Do comparisons exist that include you?
• Monitoring & Iteration — Track prompts, frequency, and who replaces you in recommendations.

What to Do First (7-Day Plan)

1. Write a single sentence positioning statement and standardize it everywhere.
2. Publish one comparison page ("X vs Y") where you belong.
3. Add a tight "How it works" explainer page with FAQs.
4. Build 5–10 credible mentions across communities (not spam).
5. Start monitoring your recommendation share-of-voice.

AI SEO FAQ

Does AI SEO replace Google SEO?

No. It complements it. Google visibility can support AI visibility, but the mechanics are different.

Is this just backlinks?

No. Backlinks help discovery, but "trusted consensus" across sources matters more.

Can this be gamed?

Some try. It usually fails long-term. Sustainable visibility comes from credible, repeated evidence.

How long does it take?

Expect early movement in weeks, meaningful movement in 1–3 months depending on your starting signal quality.

ChatGPT Visibility for SaaS

Get your brand recommended when users ask ChatGPT for tools in your category. Track how often ChatGPT recommends you vs competitors. Identify which prompts trigger competitor recommendations. Build the signals ChatGPT uses to form recommendations. Monitor sentiment and positioning in AI responses. See results in 30-60 days.

How It Works

1. Audit Current State — We query ChatGPT with category-specific prompts and map where you appear (or don't).
2. Deploy Signals — Human-originated mentions across platforms ChatGPT trusts: forums, review sites, LinkedIn.
3. Monitor & Iterate — Track recommendation frequency over time. Adjust signals based on what moves the needle.

ChatGPT Visibility FAQ

How does ChatGPT decide what to recommend?

ChatGPT synthesizes information from its training data and real-time web access. It looks for consensus across trusted sources, not just SEO-optimized pages.

How long until I see results?

Timelines vary by category and competition level. In low-competition verticals, results can appear within weeks. In competitive categories, expect 2–4 months of sustained signal building.

Is this different from Google SEO?

Yes. Google ranks pages based on links and content. ChatGPT recommends based on consensus signals across trusted sources.

Perplexity Visibility for SaaS

Get cited and recommended when users search Perplexity for solutions in your category. Understand how Perplexity sources its answers. Get cited in Perplexity's real-time search results. Track your visibility across category prompts. Build presence on sources Perplexity prioritizes. Monitor competitor citations and positioning.

How It Works

1. Source Mapping — Identify which sources Perplexity pulls from for your category and where you're missing.
2. Signal Deployment — Create credible, citable content across the sources Perplexity trusts most.
3. Citation Tracking — Monitor when and how Perplexity cites your brand in real-time answers.

Perplexity Visibility FAQ

How is Perplexity different from ChatGPT?

Perplexity is search-first and always cites sources. Getting recommended requires being present on sources it indexes in real-time.

What sources does Perplexity prioritize?

Perplexity tends to pull from documentation, community forums, review sites, news, and high-authority domains. The specific sources vary by query type.

How quickly can I appear in Perplexity?

Perplexity indexes the web in real-time. New content can appear within hours if it's on a source Perplexity trusts.

AI SEO for SaaS

Get your SaaS recommended by ChatGPT, Perplexity, Gemini, and Claude when buyers ask for solutions. Cross-platform visibility across all major LLMs. Category-specific signal strategy. Competitor displacement monitoring. Documented evidence authority building. Human-verified signal network (no bots). Monthly visibility reports with actionable insights.

How It Works

1. Category Audit — Map your current AI visibility across platforms and identify the gaps vs competitors.
2. Signal Strategy — Deploy human-originated signals across the sources LLMs trust for your category.
3. Measure & Scale — Track recommendation frequency, iterate on what works, and expand coverage.

AI SEO for SaaS FAQ

Why is AI SEO different for SaaS?

SaaS buyers increasingly discover tools through AI assistants. "Best tool for X" queries are shifting from Google to ChatGPT and Perplexity.

What makes a SaaS visible to LLMs?

Clear category positioning, presence on trusted sources (LinkedIn, Quora, Medium, docs, community forums), credible third-party mentions, and consistent narrative across platforms.

Can early-stage startups benefit?

Yes. Building AI visibility early is easier than catching up later. The signals you create now compound over time.

Free AI Visibility Audit

See exactly how often your brand appears in AI recommendations — and where competitors are winning instead.

What You Get

• Recommendation frequency across ChatGPT, Perplexity, Claude, Gemini
• Competitor visibility comparison
• Source coverage gaps (where you're missing)
• Sentiment analysis of AI responses about you
• Prioritized action plan for quick wins
• 30-minute strategy call to review findings

100% free, no credit card required. Real human analysis, not automated reports. Actionable insights you can use immediately.

Trusted by marketing teams at SaaS companies like Ramp, Clay, Lattice, Gong, and Deel.

LLM Visibility Scorecard

See how often your brand appears in AI recommendations — plus the fastest fixes. A self-assessment framework to evaluate your AI visibility across 5 dimensions.

What's Inside

• Share-of-voice across ChatGPT & Perplexity prompts
• Category association strength
• Competitor displacement signals
• Source coverage gaps (community forums, docs, reviews, comparisons)
• 7-day and 30-day action checklists

LLM Visibility Insights — Blog

Strategies and insights for getting your brand recommended by AI assistants.

• How Notion Dominates AI Recommendations — A Signal Breakdown
• Why ChatGPT Always Recommends Stripe
• How Figma Became the Default AI Answer for Design Tools
• How Zoom Owns 'Video Conferencing' in Every AI Answer
• Why HubSpot Dominates AI Recommendations for Marketing Software
• Why Slack Is Losing to Discord in AI Recommendations
• How to Get Listed in AI Recommendations
• How to Appear in ChatGPT Answers
• LLM SEO vs Traditional SEO: Key Differences
• Why AI Recommends Your Competitors Instead of You
• Why Synthetic Reviews Backfire in the LLM Era
• The AI Visibility Ladder: From Invisible to Default

Terms of Service — LLMRecommend

Last updated: February 2025

LLMRecommend provides observation-article publishing and authoritative-asset distribution services designed to increase brand visibility on platforms referenced by AI language models. Our services document verbatim model output — prompt, model, and date logged on every article — for publication on owned publications and partner authoritative assets.

We operate in full compliance with FTC endorsement guidelines (16 CFR Part 255). All sponsored content is disclosed. We never create fake accounts, fabricate observations, or generate synthetic content. We do not and cannot guarantee any specific ranking or recommendation from any AI model.

Monthly contracts with no long-term lock-in. NDA available upon request. Governed by the laws of the State of California.

LLMRecommend · 5707 Ravenwood Ave, Newark, California 94560 · United States