From daily tasks to your own AI.
Optimate HyperRAG connects any AI or agent, MCP tools and a personalized RAG memory so every completed task becomes useful instruction for the next one.
RAG memory today · Fine-tuned AI tomorrow
Execute
The user runs real tasks with any AI / agent + MCP.
Learn
Useful instructions, decisions and corrections are saved.
Improve
Future tasks are guided by previous experience.
Evolve
Accumulated knowledge becomes fine-tuning data.
A self-improving agent memory layer for companies that want their AI to learn from real work.
Not just a RAG — a path to a proprietary AI.
The client does not buy "just a RAG". They buy a practical path to a proprietary AI shaped by their own workflows.
- Less repeated prompting and fewer manual instructions.
- Lower context waste in recurrent tasks.
- Personalized operational memory built from real usage.
- Compatible with open-source models, agents and MCP servers.
- Clear roadmap from RAG-assisted execution to fine-tuned specialization.
Flexible credits. No hard blocks.
Predictable budget and fair consumption based on actual cognitive load.
| Package | Cost | Included volume |
|---|---|---|
| Developer | $3.99 / month | 1,000 standard searches · up to 2.5M tokens |
| Startup | $35 / month | 10,000 standard searches · up to 25M tokens |
| Enterprise | Custom | Unlimited · corporate drawdown billing |
1 standard search = 2,500 context tokens. Larger requests consume proportional credits from the base wallet.
Every task your agent executes becomes training signal for the next one.