What Is an AI Digital Twin?

An AI digital twin is a continuously developing digital model of a person. It uses memories, knowledge, relationships, communication patterns, preferences, and decisions to represent how that individual understands and interacts with the world.

A digital twin is different from a static profile or avatar. A profile describes a person at one moment. A digital twin develops over time. It gains context from new experiences, updates its understanding, and becomes more capable of remembering, reasoning, and communicating in ways that reflect the individual it represents.

Two capabilities make a digital twin real rather than aspirational: long-term memory, and communication in the person's own voice. This page covers both.

What information makes up a digital twin?

A human being cannot be represented accurately by a biography and a list of preferences.

People are shaped by thousands of interactions: conversations, decisions, relationships, projects, mistakes, changing beliefs, recurring habits, and small moments that only become meaningful later. Much of this information is scattered across email, messages, calendars, documents, meetings, and memory.

An AI digital twin brings those fragments into a persistent model: episodic information about specific events, semantic knowledge about people and subjects, communication patterns, expressed preferences, and evidence of how the person has made decisions in the past.

The aim is not to collect information for its own sake. The aim is to preserve enough continuity for the model to provide useful, personally grounded judgment.

Memory is the foundation

Most AI feels forgetful. A user explains a project, relationship, or problem in detail, receives a useful response, and then repeats the same explanation in a later conversation. This creates a context tax: time spent briefing the AI before it can do useful work.

A digital twin removes that tax with real long-term memory — and storing chat history is not enough. Useful AI memory mirrors the structure of human memory:

Episodic memory preserves specific experiences. It answers: what happened, when, who was involved, where the information came from.

Semantic memory preserves durable knowledge: what is generally true about a person, project, relationship, or idea across many experiences.

Consolidation connects the two — a recurring process that reviews recent experiences, removes noise, identifies durable information, and updates the broader understanding. Without consolidation, memory is a pile of records. With it, memory becomes knowledge.

How Sentience builds the memory of a digital twin

Sentience builds a model from the sources you choose to connect, including email, calendar, meetings, messages, documents, screen activity, Slack, and Notion.

New experiences are stored as timestamped memories. Sentience searches these memories by meaning, retrieving relevant context even when the wording of a question does not match the original source.

Those memories are consolidated into a living library: evolving pages about the people, projects, goals, ideas, and subjects that repeatedly appear in your life. A raw memory preserves what happened; a library page preserves what has remained important across many experiences.

The value compounds. A meeting is no longer an isolated transcript — it becomes another point in the history of a project. A new email is not only a block of text — it may change the status of a commitment, relationship, or decision. This compounding is what separates a digital twin from saved chat history.

A twin communicates in your voice

Writing style is where a digital twin becomes visible to other people — and where generic AI fails most obviously. General-purpose models produce clear, broadly acceptable language: unnecessary introductions, excessive structure, vague enthusiasm, and phrasing few people naturally use. A short "make this sound like me" prompt cannot capture years of vocabulary, humor, pacing, and professional judgment.

People also do not have one style. A message to a close friend may be short, lowercase, and full of shared references. A professional email is direct and carefully phrased. A Slack message can be incomplete because the team already shares context. An AI that applies one style everywhere produces a caricature.

Sentience learns your written voice from communication you choose to connect. It identifies patterns in vocabulary, sentence length, cadence, directness, formatting, and channel-specific behavior, and keeps dedicated voice guidance per setting.

Voice without memory is mimicry — a sentence can sound like someone while being something they would never say. Sentience combines the two: before drafting, it retrieves the history of the relationship and the project, so the output resembles how you write and reflects what you actually know. Sentience currently focuses on written voice; reproducing a person's spoken audio voice is not a current feature.

What can a digital twin do today?

A digital twin can already function as a personal memory, thought partner, and context-aware assistant.

You can ask it to recall information across connected sources, summarize the history of a project, prepare you for a meeting, trace how an idea developed, or draft communication in your voice. Before a conversation, it can assemble the relevant history of the person and subject. While writing, it can incorporate earlier decisions and avoid contradicting what you have already said.

It can also surface useful context proactively — daily digests, pre-meeting briefs, and proposed actions based on new information.

The current model remains collaborative rather than fully autonomous. It can prepare emails, messages, documents, and calendar changes, but you review and approve external actions. Fully operating as a person across every situation remains a longer-term direction, not a current capability.

Digital twins should preserve human difference

One risk of general AI is homogenization. When everyone uses the same models with little personal context, the outputs begin to sound and reason alike.

A meaningful digital twin does the opposite. It preserves the accumulated differences that make one person unlike another: their experiences, unusual knowledge, relationships, taste, values, language, and judgment. The goal is not to replace the person with an average model. It is to extend the person through a model built from their life.

Who controls a digital twin?

Control is fundamental because a digital twin can become one of the most sensitive representations a person possesses. Its memory contains more than documents — it can reveal relationships, beliefs, habits, and private conversations.

Sentience is designed around personal control. You choose which sources are connected, sharing is off by default, and proposed external actions require approval. Data is encrypted in transit and at rest, and memories, connectors, or the entire account can be deleted. Screen capture passes through an on-device filter designed to block password managers, banking applications, and private windows before captured information leaves the device.

Sentience's published product commitment is that your data and digital self should remain yours. Some of the legal language governing ownership of the synthesized personal model is being clarified, so ownership claims should be read together with the current Terms of Service.

Build your digital twin

A digital twin cannot be created from a single questionnaire. It develops through memory, continued context, and time.

Sentience is building the infrastructure for a digital version of you that remembers your life, learns how you think, communicates in your voice, and operates alongside you.

Apply for access to begin.

Frequently asked questions

What is an AI digital twin?

An AI digital twin is a continuously developing model of a specific person. It uses memories, knowledge, relationships, preferences, communication patterns, and decisions to represent that individual over time.

Is a digital twin the same as an avatar?

No. An avatar is primarily a visual or interactive representation. A digital twin is an evolving model that can remember information, understand context, reason, and communicate using patterns learned from a person's life.

What is long-term memory in AI?

Long-term memory is the ability to retain and retrieve information across conversations and extended periods of time. It combines episodic memory (specific events), semantic memory (durable knowledge), and consolidation — not just saved chat history.

What does it mean for AI to write in your voice?

It means the AI adapts its vocabulary, tone, cadence, and structure to how you naturally write — differently per channel — rather than defaulting to a generic assistant style, and grounds each draft in the history of the relationship behind it.

How is a digital twin created?

A digital twin is created by connecting information from a person's experiences and digital life, organizing it into memory and durable knowledge, and learning recurring patterns in how that person communicates and decides.

Can a digital twin replace a person?

Not today. Current digital twins can assist with memory, reasoning, communication, and proposed actions, but they cannot reproduce every part of a human being or operate autonomously across every situation.

Can I delete what my digital twin knows?

Yes. In Sentience you can delete individual memories, threads, connected sources, or your entire account and its associated data.

Does Sentience create digital twins?

Yes. Sentience creates a digital version of you using the memories and sources you choose to connect, then organizes that context into persistent memory, a living knowledge library, and a personalized model of your voice.