&Autonomous employees
We're a recursive self-improvement (RSI) lab for autonomous agent runtimes: AI employees that get better at getting better.
Trusted by 400+ Fastest Growing B2B teams
Meet Your First Employee
Rex — Autonomous SEO & GEO Employee
Rex writes, publishes, and optimizes content 24/7 so your brand ranks on Google and gets cited by ChatGPT, Perplexity, and Claude. Built on recursive self-improvement — not static automation.
Get Found on Google
- Keyword research & content strategy
- Autonomous writing & publishing
- Technical SEO & internal linking
- Rank tracking & optimization
Get Cited by AI
- AI search visibility monitoring
- Citation optimization across LLMs
- Brand mention tracking across ChatGPT, Perplexity, Claude
- Content structured for AI retrieval
Compounds Over Time
- Optimizes its own experimentation strategy
- Learns from every publish & ranking signal
- Refines evaluation criteria autonomously
- Gets better at getting better
Why Rex Is Different
Most “AI employees” are stuck at Level 0
The search game changed. Your competitors are showing up in AI answers. You don't need another SEO tool that gives you dashboards but no execution. You need an employee that actually does the work — and gets smarter doing it.
Execute Tasks
Most AI SEO tools and "AI employees" run a static playbook. Write a blog post. Optimize a title tag. No learning. The agent on day 300 is identical to day 1.
Other AI employeesLearn from Outcomes
Track what worked. Adjust strategy based on results. Better over time — but only reactively, and only on the metrics you told it to watch.
Some emerging toolsImprove How They Learn
Rex optimizes its own experimentation strategy, evaluation criteria, and learning policies. It gets better at getting better — recursive self-improvement applied to your search visibility.
Rex by Relixir →Breakthrough RSI Performance in Verticals
Rex outperforms frontier AI models and agent runtimes
Domain-specific agents vs. general-purpose agent runtimes across 1,200 production SEO workflows
Rex (Relixir)Rex (Relixir)Rex (Relixir)Domain-specific context orchestration eliminates 60-80% of wasted context window
View Benchmark →How Rex Works
From Zero to Ranking in 3 Steps
No briefs. No agency retainers. No waiting. Connect Rex and watch your search visibility compound.
Connect Your Tools
Link your CMS (WordPress, Webflow, Framer), analytics (GA4, GSC), AI search analytics, CRM (HubSpot), email, messaging (Slack, Teams), and more — Rex connects to your entire marketing stack with one click.
Rex Gets to Work
Rex starts writing, optimizing, and publishing content — targeting both Google rankings and AI citations. You approve or let Rex run autonomously.
Watch It Compound
Track rankings, AI citations, and inbound traffic in one dashboard. Rex continuously learns and improves, compounding your results every week.
Rex vs. the Alternatives
Not Another SEO Tool
 Rex | SEO Agency | In-House Hire | SEO Tool | |
|---|---|---|---|---|
| Executes content autonomously | ✓ | — | — | — |
| Covers Google + AI search | ✓ | — | — | — |
| Learns and improves over time | ✓ | — | Slowly | — |
| Scales without headcount | ✓ | — | — | N/A |
| Built on recursive self-improvement | ✓ | — | — | — |
AI is an inflection point in the history of work. For the first time, agent runtimes give a single AI employee the ability to research, write, publish, analyze, and iterate — all autonomously, with persistent memory across sessions.[1]
General-purpose AI is becoming a commodity.[2] The real opportunity isn't building a smarter base model — it's building systems that accumulate domain-specific intelligence over time.
Most AI tools execute tasks. Some learn from outcomes. The next generation will improve how they learn — optimizing their own experimentation, evaluation, and strategy policies.[3]
We're proving this in SEO and GEO today. The architecture applies to every domain where you can measure outcomes.[4]
The companies that build recursive improvement systems for specific verticals will own those verticals.
[1] Wang et al., “A Survey on Large Language Model-based Autonomous Agents,” arXiv:2308.11432, 2023.
[2] Bommasani et al., “On the Opportunities and Risks of Foundation Models,” arXiv:2108.07258, 2021.
[3] Schmidhuber, J., “Gödel Machines: Fully Self-Referential Optimal Universal Self-Improvers,” Cognitive Technologies, Springer, 2007.
[4] Relixir Labs, “RSISEO-1: RSI Context Orchestration Benchmark,” Internal Technical Report, Feb 2026.
Design Philosophy
Built on Three
Non-Negotiables
The architectural decisions that separate compounding agents from static AI wrappers. These principles are structural, not aspirational.
Autonomous by Default
Our agents don't assist — they execute. Each Claw operates end-to-end within its domain, making decisions, taking actions, and optimizing outcomes without requiring human oversight.
Recursive Intelligence
Every agent runtime improves through recursive self-improvement. Performance compounds as agents optimize their own experimentation strategies, evaluation criteria, and learning policies — not just their outputs.
Native Integration
Claws connect directly to existing work infrastructure — CRMs, CMSs, analytics platforms, communication tools. No migration. No rip-and-replace. Intelligence that meets you where you already work.
Your competitors already hired Rex
See how Rex can drive inbound pipeline from Google and AI search — fully autonomously, with recursive self-improvement built in.