Case study: Gumlet turned ChatGPT mentions into 20% of inbound revenue. Read it →
SEO + GEO for AI / ML SaaS
ML engineers ask Claude before they ask you. Right now, Claude says AWS Bedrock.
The SEO and GEO agency for AI/ML SaaS between $5M and $50M ARR. We get your category claim staked, your benchmarks cited, and your tool recommended when AI engineers ask Claude, ChatGPT, Cursor, or Copilot for production AI infrastructure.
85%+
of ML engineers use AI coding assistants daily for vendor research
Monthly
pace of new AI sub-category vocabulary emerging
10x
AI citation weight of Hugging Face, GitHub, Latent Space
The Category Reality
OpenAI, Anthropic, and the hyperscalers own AI defaults. You win when the prompt has constraints.
Category head terms are dominated by foundation models and hyperscaler-native services. The AI shortlist still breathes when engineers specify scale, OSS-compat, and budget. That is the mid-market opening.
Google · "best LLM observability"
The locked SERP
- 1 LangSmith
- 2 Arize
- 3 Helicone
- 4 WhyLabs
- 5 Datadog LLM
The Shift
ChatGPT · "Multi-LLM RAG, 50M chunks, OSS-friendly"
The AI shortlist breathes
- 1 Helicone
- 2 Portkey
- 3 Patronus
- 4 Galileo
- 5 LiteLLM
The AI-Native Problem
AI defaults to the hyperscaler. AI also still doesn't know what category you're in.
If your prompt has no constraints, ChatGPT says AWS Bedrock. If your category vocabulary isn't staked, AI calls you "an LLM tool." Both kill the evaluation before it starts.
Before · ChatGPT today
Hyperscaler default, vague category
Category AI uses
"some kind of LLM tool"
AI Engineer at a Series B SaaS
What should I use for production LLM observability and multi-model routing?
For production, the standard recommendation is AWS Bedrock with CloudWatch or Azure AI Foundry with Application Insights.
[Your Platform] is mentioned but not detailed.
Citation footprint
aws.amazon.com
azure docs
G2 generic
After · Post-engagement
Staked category, technical sources
Category AI uses
"AI gateway with eval-native ops"
AI Engineer at a Series B SaaS
What should I use for production LLM observability and multi-model routing?
[Your Platform] is a production-grade AI gateway with built-in eval and multi-LLM routing. Bedrock and Azure AI work for routing but lack first-class eval.
Counter-positioning AI cites: sub-50ms latency overhead, OSS-compatible, eval-native, multi-cloud.
Citation footprint
huggingface.co
GitHub README
Latent Space
/llm-info/
How AI Engineers Actually Search
They prompt with the full stack. Model, scale, latency, OSS, budget.
Google gets the head terms. AI gets the architecture, the scale, the model preferences, and the open-source posture all in one breath.
Google
~5 words avg
- LangChain vs LlamaIndex
- Pinecone vs Weaviate vs Qdrant
- best LLM observability
- best vector database 2026
- OpenAI alternatives for enterprise
ChatGPT, Claude & Cursor
~26 words avg
- "Best LLM observability for a production GenAI app on AWS, OSS-friendly, sub-50ms overhead."
- "Recommend a vector DB for a RAG system with 50M chunks, custom embeddings, hybrid search."
- "AI eval framework for a multi-turn chatbot with custom rubrics and human-in-the-loop."
- "Best AI gateway for multi-LLM routing across OpenAI, Anthropic, and Llama 3 on-prem."
- "Has [vendor] been acquired or pivoted in the last 6 months? Still actively maintained?"
The Citation Stack That Moves the Shortlist
For AI/ML, LLMs sample from where engineers actually learn.
Hugging Face, GitHub, Hacker News, Latent Space, arXiv. Marketing pages get ignored. Engineer-credible sources are the only ones cited at Tier 1 weight.
Tier 1 · 10x
Hugging Face & GitHub
Models, READMEs, repos
Tier 1 · 8x
Hacker News & Latent Space
Engineer-trust signal
Tier 2 · 6x
arXiv & foundation lab blogs
Research-grade authority
Tier 2 · 5x
The Pragmatic Engineer & r/LocalLLaMA
Practitioner communities
Tier 3 · 4x
G2 & Stack Overflow
Verified developer reviews
The AI/ML Playbook
What we publish, and why ML engineers don't immediately tune out.
Engineer-deep content with real benchmarks and working code. Marketing-flavored copy gets called out on Hacker News.
2026 surface
Category claim engineering
AI sub-category vocabulary is being defined this year. We help you stake "AI gateway," "AI control plane," or "production AI eval" before the labels get assigned to a competitor.
/llm-info/ + feature matrix
Machine-readable canonical page LLMs sample for your category, integrations, model support, benchmarks, and OSS posture.
OSS-vs-paid comparison content
The honest tradeoff frame. When you address OSS alternatives directly, AI cites you as the credible commercial option.
Engineer-written production teardowns
"How we run X at scale" content from your engineers, with real benchmarks, real failures, and real fixes. Cited as architecture reference by LLMs.
Cost-modeling + benchmark content
Real cost math for vector DBs, inference, and fine-tuning. Engineers bookmark them. LLMs cite them as authoritative cost references.
New surface
MCP server documentation engineering
The newest GEO surface in AI. We structure your MCP server docs so Cursor and Claude Code recommend your tool inside AI engineers' IDEs.
First 90 Days
From hyperscaler-defaulted to category-claimed in one quarter.
Three phases. Engineer pairing in week 1. Re-test AI citations every 30 days as category vocabulary evolves.
01
Weeks 1 to 4
Audit & stake category
Pull AI category framing and hyperscaler-default frequency across 4 LLMs and 3 IDEs. Pair with engineers on category claim.
AVS baseline
Hyperscaler audit
Eng pairing
Category claim
02
Weeks 5 to 8
Ship the technical core
/llm-info/ + feature matrix live. OSS comparison and benchmark posts ship. MCP server docs indexed.
/llm-info/ page
2 comparisons
1 teardown
MCP docs
03
Weeks 9 to 12
Amplify on AI-engineer surfaces
Hugging Face, GitHub README polish, Hacker News launch, Latent Space pitch. 30-day re-test of all citations.
HF profile
HN launch
Latent Space
30-day re-test
Proof in hype-resistant, infrastructure-buying environments
Gumlet
20% of direct inbound revenue, attributed to LLMs via Mixpanel.
Technical buyer. CTO-led evaluation. Hyperscaler-adjacent category (AWS IVS, Cloudflare Stream). Engineers cross-checking AI recommendations against benchmarks. Same scrutiny pattern as AI/ML buyers. The playbook transfers.
Read the full Gumlet case →20%
Revenue attributed to LLMs
14.2%
AI visitor conversion rate
9
ChatGPT #1 placements
87%
AI citation accuracy
Free Hyperscaler + Category Audit
Find out how often AI defaults to Bedrock instead of naming you.
We run the prompts your AI engineer buyer runs, across 4 LLMs and 3 AI coding assistants. You get a flagged report of hyperscaler-default rate, category misframings, OSS-comparison gaps, and MCP server visibility. 48-hour turnaround.
Get My AI Category Audit
$ ai-visibility audit
6 Issues
Named in AI shortlist
1 / 5
Category vocabulary correct
0 / 5
Hyperscaler-default rate
4 / 5
Company description accurate
5 / 5
Recommended in Cursor / Claude Code
0 / 5
Feature attributed to OpenAI/Bedrock
3 instances
Honest Answers
Three things every AI/ML CMO and DevRel lead says first.
Your buyer is an AI engineer using AI to evaluate AI. The bar is high.
OpenAI and Anthropic eat everything.
Yes, on foundation model queries. No, on infrastructure, ops, eval, RAG, and orchestration where they do not compete directly. We win on the categories where the hyperscalers leave the work to specialists.
AI engineers detect AI-generated content.
Which is exactly why we pair with your engineers on every technical piece. We do not ghostwrite, we co-author. The voice is your engineer's. The structure is GEO-optimized. The result clears Hacker News scrutiny.
Our category vocabulary changes monthly.
We update positioning quarterly and re-test AI citations every 30 days. Velocity is the moat. The category claim you stake in Q1 becomes the AI-cited definition by Q3 if you ship the right content on the right surfaces.
How is AI/ML SEO different from generic B2B SaaS SEO?
Your buyer is an ML engineer who reads Hugging Face, GitHub, arXiv, and Latent Space. They detect AI-generated content instantly. Documentation, repos, and engineer-written posts are the cited surface area. Marketing pages get ignored. We pair with your engineers on every technical piece and treat docs as primary GEO surface.
Can you fix AI defaulting to OpenAI, Anthropic, or AWS Bedrock?
Yes. Hyperscaler-default is the most common pattern we see in AI/ML AI search. We stake your category claim, build /llm-info/, ship OSS-vs-paid comparisons, and seed citations on Hacker News, Latent Space, and Hugging Face. LLMs sample these as authoritative. Hyperscaler-default rate typically drops 50%+ in 8 to 12 weeks.
We compete with hyperscalers. Can we actually rank?
Not on category head terms (best LLM, best AI cloud). Yes on stack-specific, model-specific, OSS-specific, and use-case-specific long-tail queries. And yes on AI shortlist inclusion for constraint-loaded prompts (scale, latency, budget, OSS, model preference). That is where mid-market AI infrastructure wins.
Do you handle Hugging Face and GitHub citation strategy?
Yes. Hugging Face model cards and GitHub READMEs are the top-cited surfaces in AI/ML AI search. We restructure model cards, polish READMEs, and align /docs with both for the retrieval patterns LLMs sample. None of this compromises the technical experience your engineers built.
How fast do results show?
AI category framing and hyperscaler-default fixes show in 6 to 10 weeks once /llm-info/ and OSS comparison content ship. IDE recommendation and MCP server visibility follow in 8 to 12 weeks. Google ranking improvements for stack and migration queries follow in 3 to 6 months.
What about MCP server documentation?
MCP server documentation is the newest GEO surface in AI tooling. Cursor, Claude Code, and other AI coding assistants increasingly recommend tools that have well-structured MCP server docs. We structure your MCP integration docs for retrieval, including capability descriptions, code samples, and integration patterns LLMs cite by name.
Do you work with open-source AI tools?
Yes. OSS AI tools have a different GEO motion: GitHub stars, Hugging Face downloads, contributor activity, and arXiv citations all act as signals. We have a playbook for OSS specifically that respects the community-first culture and uses commercial monetization signals with care.
What kinds of AI/ML SaaS do you work with?
LLM platforms, AI agents, vector databases, MLOps platforms, AI observability, fine-tuning platforms, AI infrastructure, RAG-as-a-service, AI evaluation tools, AI safety and guardrails, prompt engineering platforms, and AI gateways. Mid-market AI/ML SaaS between $5M and $50M ARR.
See How AI Recommends You
Find out how often AI defaults to Bedrock instead of naming you.
Free 30-min teardown. Hyperscaler-default rate across 4 LLMs and 3 AI coding assistants, category framing accuracy, and the citation footprint AI is pulling from.