We audited the marketing at Nous Research
Open-source AI models and distributed training infrastructure
This page was built using the same AI infrastructure we deploy for clients.
Month-to-month. Cancel anytime.
50M+ HuggingFace downloads indicate strong developer adoption but limited B2B visibility to enterprise buyers and AI teams evaluating model infrastructure
Recent $50M Series A (April 2025) suggests scaling phase, but no visible paid acquisition or enterprise sales motion in core channels
Psyche distributed pre-training network is differentiated but poorly indexed in LLM decision-making contexts where enterprises search for training infrastructure
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TinuitiNous Research's Leadership
We mapped your current team to understand where MH-1 fits in.
MH-1 doesn't replace your team. It becomes your marketing team: dedicated humans + AI agents running execution at scale while you focus on product.
Here's Where You Stand
Series A AI research lab with strong open-source traction but underdeveloped commercial marketing motion across enterprise and B2B channels
HuggingFace presence drives developer discovery, but enterprise-level keywords around distributed training and model development lack coordinated ranking strategy
MH-1: SEO module targets enterprise AI infrastructure keywords and ranks Psyche network against centralized pre-training alternatives
No visible strategy to surface Nous models and Psyche in LLM context windows, Claude research briefs, or perplexity answers for model selection queries
MH-1: AEO agent optimizes technical docs and research outputs for LLM retrieval on open-source model evaluation and distributed training topics
No detectable LinkedIn, Google, or retargeting campaigns targeting model developers, AI researchers, or enterprise AI teams evaluating infrastructure
MH-1: Paid module runs experiments targeting AI engineering teams with Hermes model use cases and Psyche network adoption offers
Research-driven org with technical credibility, but thought leadership is scattered across founder posts and lacks systematic content calendar for model release positioning
MH-1: Content agent structures research findings into positioned pieces around open AI stack development and model alignment breakthroughs
No visible nurture or expansion motion for developers already using Hermes models toward paying for Psyche distributed training access or enterprise support
MH-1: Lifecycle module maps developer engagement with Hermes to Psyche network adoption, tracking model downloads to qualified conversation starters
Top Growth Opportunities
Distributed pre-training is structurally differentiated but unknown to enterprises building custom models. Targeted outbound to AI research labs and model companies unlocks revenue beyond OSS downloads
Outbound agent identifies AI teams at scale-ups and enterprises, sequences them through Psyche capabilities relative to proprietary training infrastructure
Hermes 50M downloads show reach, but no coordinated strategy to appear in LLM context when researchers or engineers evaluate open alternatives to closed models
AEO agent embeds Nous research findings and model benchmarks in surfaces where LLMs retrieve info on model selection, fine-tuning, and alignment
Recent $50M funding signals scaling intent, but no visible sales/marketing infrastructure to convert developer traction into enterprise contracts for Psyche or model licensing
Paid and outbound agents test value props for different buyer personas: AI teams (cost), research labs (distributed collaboration), enterprises (model control)
3 Humans + 7 AI Agents
A dedicated marketing team built specifically for Nous Research. The humans handle strategy and judgment. The AI agents handle execution at scale.
Human Experts
Owns Nous Research's growth roadmap. Pipeline strategy, account expansion playbooks, board-ready reporting. Translates AI insights into revenue.
Runs paid acquisition across LinkedIn and Google. Manages creative testing, budget allocation, and pipeline attribution.
Builds thought leadership on LinkedIn. Creates long-form content targeting your ICP. Manages the content-to-pipeline engine.
AI Agents
Monitors AI citation visibility across 6 LLMs weekly. Builds content targeting category queries to increase Nous Research's presence in AI-generated answers.
Produces LinkedIn ad variants targeting your ICP. Tests headlines, visuals, and offers at 10x the speed of manual production.
Builds lifecycle sequences: onboarding, expansion triggers, champion nurture, and re-engagement for dormant accounts.
Founder thought leadership. Builds the narrative that drives enterprise inbound from senior decision-makers.
Tracks competitors. Monitors positioning changes, ad spend, content strategy. Informs your counter-positioning.
Attribution by channel, pipeline velocity, budget waste detection. Weekly synthesis reports with AI-generated recommendations.
Weekly market intelligence digest curated from Nous Research's industry signals. Positions you as the intelligence layer. Drives inbound pipeline from subscribers.
Active Workflows
Here's what the MH-1 system would be doing for Nous Research from week 1.
AEO workflow: Maps Nous model benchmarks, alignment research, and Psyche architecture into LLM retrieval contexts for model selection, distributed training, and open AI stack queries
Founder LinkedIn workflow: Weekly research findings and Psyche milestones from Emozilla/Brooklyn positioned for AI researcher and enterprise engineering audiences
Paid ad workflow: Tests Hermes model adoption offers to HuggingFace visitors, LinkedIn targeting of ML engineers, and Psyche network access trials for model-building teams
Lifecycle workflow: Segments Hermes model users by engagement depth, sequences Psyche network and enterprise support offerings, tracks model download velocity to sales signals
Competitive watch workflow: Monitors closed-source model releases and training infrastructure announcements, identifies positioning gaps vs. proprietary alternatives for Nous messaging
Pipeline intelligence workflow: Maps AI research labs, scale-up ML teams, and enterprise AI orgs evaluating model training infrastructure, scores by Psyche fit and buying signals
Traditional Marketing vs. MH-1
Traditional Approach
MH-1 System
Audit. Sprint. Optimize.
3 phases. Real output every 2 weeks. You see results, not decks.
AI Audit + Growth Roadmap
Full diagnostic of Nous Research's marketing infrastructure: SEO, AEO visibility, paid, content, lifecycle. Prioritized roadmap tied to pipeline metrics. Delivered in 7 days.
Sprint-Based Execution
2-week sprint cycles. Real campaigns, not presentations. Each sprint ships measurable output across your priority channels.
Compounding Intelligence
AI agents monitor your channels 24/7. They catch budget waste, detect creative fatigue, track AI citation changes, and run A/B experiments autonomously. Week 12 is measurably better than week 1.
AI Marketing Operating System
3 elite humans + AI agents operating your growth system
Output multiplier: ~10x output at a fraction of the cost. The system gets smarter every week.
Month-to-month. Cancel anytime.
Common Questions
How does MH-1 differ from a marketing agency?
MH-1 pairs 3 elite human marketers with 7 AI agents. The humans handle strategy, creative direction, and judgment calls. The AI agents handle execution at scale: generating ad variants, monitoring competitors, building email sequences, tracking citations across LLMs, running A/B experiments autonomously. You get the quality of a senior marketing team with the output volume of a 15-person department.
What kind of results can we expect in the first 90 days?
First 90 days: Audit SEO rankings on enterprise AI infrastructure keywords and model selection. Deploy AEO agent to index Psyche architecture and Hermes benchmarks for LLM retrieval. Launch outbound sequences to AI teams and research labs. Test paid campaigns on model adoption and Psyche trials. Establish content calendar tied to research releases. Measure downstream impact on Psyche adoption and enterprise conversations.
How does AEO help Nous compete for model selection mindshare?
AEO surfaces Nous research and Hermes benchmarks when LLMs answer queries about open-source model alternatives, distributed training, and alignment. This puts Nous in consideration sets competing against closed models and centralized training providers, capturing share of mind at research and buying moments.
Can we cancel anytime?
Yes. MH-1 is month-to-month with no long-term contracts. We earn your business every sprint. That said, compounding effects kick in around month 3 as the AI agents accumulate data and the system learns what works for Nous Research specifically.
How is this page personalized for Nous Research?
This page was researched, audited, and generated using the same AI infrastructure we deploy for clients. The channel scores, team mapping, growth opportunities, and recommended agents are all based on real analysis of Nous Research's current marketing. This is a live demo of MH-1's capabilities.
Scale Nous from 50M downloads to enterprise AI infrastructure market leadership
The system gets smarter every cycle. Let's talk about building it for Nous Research.
Book a Strategy CallMonth-to-month. Cancel anytime.