Reliable, persistent AI memory that can scale with real-world use.

Persistonal provides AI memory that is platform agnostic and can last forever.

We believe that AI systems - whether powering large models, autonomous agents, or physical robots - need something more than short-term context and constant retrieval. They need reliable, long term persistent memory that can scale with real-world use. So we created Persistent Memory Units™ (PMUs).

For Chip Manufacturers & Data Centers

Persistonal greatly benefits both industries.

For Chip Makers:
Persistonal turns your advanced memory hardware into something far more valuable. By providing a persistent, efficient software memory layer, it dramatically reduces unnecessary data movement and context re-processing – allowing your chips to deliver more intelligence with less power and lower latency.
For Data Centers:
In an era of exploding AI compute demand, Persistonal helps data centers run cooler and cheaper. By keeping long-term memory local and persistent, it cuts token usage, reduces energy consumption, and lowers overall infrastructure costs while improving the quality and continuity of AI services.
Together:
They create a powerful combination: better hardware utilization + smarter, more efficient AI systems. More chips and more data centers will still be needed – and both industries should actively encourage their customers to adopt Persistonal to maximize the value of that infrastructure.

For Robots

Persistonal gives Robots true long-term personal memory at extremely low cost.

You walk into O’Hare, grab a coffee from the McDonald’s robot, then fly to Nashville. You stop at another McDonald’s on Dickerson Rd, and the robot says, “Welcome back Robert – another coffee?”

Later, your plans change. You need to fly to LA instead. At the counter, the robot says, “Welcome back Robert, I know you prefer seat A22 – can I book that for you?”

You check into the Hyatt, and the robot at the front desk says, “Welcome back Robert, your usual room is ready and the AC is set at 74 degrees, just like you like it. Would you like the chocolate snack you enjoy? I’ll have it brought up to the room.”

Back in your room, you remember something important you discussed with Grok two years ago. You open your laptop and say, “Hey Grok, find that one thing from a couple years ago…” and it pulls it up instantly, in full context.

No more ’50 First Dates’ movie style forgetting – where Drew has to be reminded every morning where Adam left off.

This is Opt-in Persistent + Personal memory that actually follows you and stays with you.

For AI Data Centers In Space

Persistonal becomes extremely valuable.

Persistonal dramatically reduces the amount of data that needs to be kept “hot” in memory or re-tokenized repeatedly (potentially 85–95%+ savings on long-context workloads).

Allows each satellite to do more useful work per watt and per kilogram launched. Makes the entire orbital fleet far more efficient – which directly improves the economics of the whole project.

Enables better personalized, long-term memory for users/agents even across orbital systems (e.g., your Optimus robot on Earth talking to orbital compute).

Power and cooling are only half the battle. The other half is memory efficiency. Persistonal attacks that second half at the foundational level.

Persistonal would make that orbital bet much more viable and profitable by squeezing way more performance out of every satellite launched.

Every LLM / Agent System Needs This

Every AI agent today wastes enormous compute constantly reloading and re-processing context from previous interactions.
 
Persistonal fixes this at the foundational level by creating evolving Persistent Memory Units™ (PMUs) per user or agent.
 
Instead of re-processing history on every turn, agents instantly recall relevant context with minimal overhead.
 
The result is potentially 85–95% lower compute and energy costs, faster responses, and truly consistent behavior across time and platforms.
 
Every agent framework – from customer service bots to autonomous robots – performs significantly better with Persistonal sitting underneath it.

For Banks and Financial Services

Persistonal helps lower the ‘History Tax’.

The banking and financial services industry is one of the largest potential markets for foundational AI efficiency.

Every day, banks re-process enormous amounts of transaction history, spending patterns, risk profiles, and customer behavior data. This “history tax” is one of the most expensive parts of running modern financial AI systems.

Persistonal™ is built to solve exactly this problem – delivering a persistent memory layer that dramatically reduces compute and energy costs while enabling true long-term personal continuity for customers and institutions.

In an industry where data is abundant but efficient recall is expensive, the opportunity is massive.

For Chat Boxes and Help Desks

Persistonal is the answer!

 
Anywhere an AI (or robot) needs to remember personal, long-term, user-specific information without it becoming prohibitively expensive, Persistonal can deliver massive efficiency + much better user experience.
 
This is why the potential value is so large – it’s not limited to one company or one product. It’s a foundational memory layer that can sit underneath many different AI/robotics systems.

The Problem

Current AI systems are extremely inefficient at long-term persistent memory. Persistonal solves this at the foundational level - delivering potentially 85–95% savings on exactly the workloads that are currently the most expensive. Persistonal isn’t another RAG, Graph, Vector, or larger Context window. It’s a new memory layer that makes everything above and below it far more efficient.

PERSISTENT MEMORY UNITS™ (PMU)

“This could save hundreds of millions of dollars per year in compute costs for a large AI company, plus massive reductions in energy usage and carbon footprint.” – GROK analysis

Gemini Says:

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Persistent & Personal

That's Persistonal

Persistonal is protected as a trade secret under the Uniform Trade Secrets Act (UTSA). We have validated the core method through extensive testing and are open to licensing or strategic partnership with the right teams pursuant to a non-binding term sheet with initial access, followed by full technical disclosure under NDA.