Personal Intelligence

The intelligence layer between you and AI.

A persistent understanding of your history, preferences, relationships, goals, decisions, and changing circumstances—built privately, controlled by you, and able to survive a change of AI provider.

The missing context

General intelligence still needs to understand the person.

AI can know a great deal about travel, finance, products, health, work, and the wider world while knowing very little about the individual asking for help. Every new assistant and application tries to reconstruct a small, temporary version of the user.

Personal Intelligence is the persistent understanding that lets an AI ask a more useful question than “What is the best answer?” It helps the system ask, “What is the best answer for this person?”

How Mimoto develops

From conversation history to context for personal AI.

  1. Understand your conversations.Today Mimoto turns supported message history into local reports, patterns, timelines, and structured exports.
  2. Understand the person behind them.Conversation history can contribute facts, preferences, constraints, relationships, goals, intentions, and important events.
  3. Connect decisions with outcomes.A useful model preserves the situation, options, decision, rationale, and what happened next—not just an isolated preference label.
  4. Supply context to authorised AI.In the longer-term direction, a user-chosen tool could request the minimum relevant intelligence for a task without receiving the person's entire archive.
The important structure

A preference becomes more useful when its history remains attached.

Facts and provenance

What is known, where it came from, when it was true, and how confident the system should be.

Preferences and constraints

What the person tends to prefer, which trade-offs matter, and what limits apply in this situation.

Relationships and goals

Who matters, what the person is trying to achieve, and how those priorities change over time.

Decisions and rationales

Which option was chosen, what alternatives existed, and why one consideration mattered more than another.

Outcomes and corrections

What happened afterwards and whether the result changed a preference, plan, or earlier conclusion.

Selective forgetting

More data is not automatically better. Irrelevant, stale, or harmful context should not become a permanent verdict.

Portable by design

Models should be replaceable. Personal Intelligence should persist.

A person should not have to rebuild themselves every time they change AI provider. Useful intelligence needs exportable schemas, timestamps, confidence, provenance, permissioning, revocable access, and a practical way to correct what the system believes.

Portability also limits disclosure. A travel agent may need family size, approximate budget, and hotel requirements. It does not need the conversations from which those preferences were learned.

The economic case

The value lies mainly in use, not resale.

Raw personal data is often worth little as an isolated record. It becomes more useful when organised into intelligence that can improve a decision for the individual.

Spend less time searching

Preserve relevant requirements so every decision does not begin with the same explanation.

Avoid unsuitable choices

Remember what was tried before, why it failed, and which constraint actually mattered.

Improve recurring decisions

Compare services, notice avoidable costs, and act with richer context as circumstances change.

Represent the buyer

Help an authorised agent compare and negotiate from the individual's side instead of only improving a seller's profile.

Alignment is a business-model choice

The individual has to remain the customer.

Better understanding can create value for a seller, an intermediary, or the person being understood. Those outcomes are not the same. Mimoto's optimisation objective should remain explicit: make the intelligence useful to the individual first.

Third parties may eventually pay for infrastructure or a transaction, but that should not quietly turn a private understanding layer into another merchant-owned profile.

Mimoto

Useful today. Built towards a larger role.

Mimoto currently analyses supported iMessage history on Mac and user-selected WhatsApp exports on iPhone. It creates local reports, patterns, timelines, and structured exports. Baxnet does not currently publish a public Mimoto API, SDK, or agent integration.

The ambition is to grow from that practical starting point into the persistent, private, and portable understanding future AI systems will need in order to act in the individual's interests.