Improved SEO signals that LLMs use to verify your business
LLM Optimisation
We make it easier for ChatGPT, Claude and other AI tools to describe your business accurately, and put AI agents to work on your own tools with memory of how you operate.
Structured data and schema optimised for AI parsing
Content structured to answer the prompts your customers use
What you get
- AI discoverability audit and SEO alignment plan
- Advanced schema markup for entity recognition
- LLM-friendly content structure (FAQ, lists, citations)
- Prompt testing to validate how AI describes your services
- Knowledge graph and citation cleanup
- Custom agent harness built around your tools and permissions
+ 5 more
- Agent setup and hosting, for example Hermes Agent on your own server
- Long-term memory for your agents, for example Hindsight
- Handover notes so your team can run and adjust it
- Local AI stack: chat (llama.cpp or oMLX), speech to text, image and video (ComfyUI)
- Fine-tuning on RunPod GPUs, with the trained model handed back to run locally
How it runs
- 01
AI visibility audit
Baseline of how AI sees you
- 02
SEO and entity alignment
Stronger authority signals
- 03
Content optimisation
Content that AI likes to cite
- 04
Validation and monitoring
Regular checks of how AI assistants describe you
Questions
How is this different from regular SEO?
LLM optimisation focuses on how AI systems understand and represent your content, beyond traditional search rankings.
Which AI platforms benefit?
ChatGPT, Claude, Google Gemini, Perplexity and other AI search assistants.
How do you measure success?
Through prompt testing, knowledge graph monitoring, and AI search result accuracy.
Can you set up AI agents for us, not just optimise for AI search?
Yes. We build custom harnesses, set up agents such as Hermes Agent connected to your email, calendar or files, and add memory systems such as Hindsight so the agent keeps context between conversations. It starts with Discovery so the setup fits how you work.
Can you run AI privately, without sending data to OpenAI or Anthropic?
Yes. We set up a local stack on your own server or Mac: language models through llama.cpp or oMLX, plus audio, image and video models through tools like ComfyUI. If you need a model tuned to your data, we train the fine-tune on RunPod GPUs and run the result locally.
Prefer to write it down? Ask a question
Not sure which you need?
Tell us what is getting in the way. We will tell you what we would do and roughly what it costs.