Desktop
In development. For deeper work, teaching and memory inspection. Exact operating-system, RAM, storage and accelerator requirements are published from the release build.
Personal AI should become more useful without becoming less yours
Kvantia is a personal AI designed to run on a device you control, learn only from what you choose to teach it and carry useful context across time. The working prototype proves the core interaction. Persistent memory, teachable skills and broader distribution are being built in clearly labelled stages.
Kvantia features
Working prototype · Local-first design · No conversation-data training · Roadmap shown honestly
What you can expect today
What works today, and what is still being built
This is the honest picture before any feature claims below. We use the same status words on every Kvantim website.
- Working prototype
Core local conversation
The core runs and can be demonstrated on supported test hardware.
- Working prototype
Grounded response process
The system can use its knowledge structure and express uncertainty. This does not guarantee every answer is correct.
- In development
Persistent memory across sessions
The long-term memory system is being built and tested. We do not promise unlimited memory today.
- In development
User teaching and reusable skills
The teaching flow is being developed after persistent memory.
- Planned
Skill marketplace
Catalogue examples are product direction, not products for sale today.
- In development
Desktop, mobile and server distribution
Each platform has its own status — see the device section below. Nothing is a finished download yet.
Your device is the home
Your personal AI should not require your private life to become cloud input
Benefit. Kvantia is designed to run its personal AI workload on a device you choose. The aim is simple: your conversations and personal memory stay in your environment instead of becoming a remote service’s conversation history.
Example. Teach Kvantia how you organise a project, what decisions were made and which details matter. That personal context stays associated with the instance on your device.
How it works
- the core runs locally on supported hardware;
- conversation and memory data are stored locally;
- account and licence services are separate from conversation processing;
- a periodic licence check may require a connection;
- optional backup or external integrations are described separately when they exist.
Boundary. Local-first does not mean the device never connects to anything. Account, licence and future optional services may use the internet. The product shows what is sent before those services are enabled.
Data boundary
What stays on your device, and what the account service holds
On your device
- Conversation
- Local AI core
- Personal memory
- Teaching data
- Local settings
Account service
- Licence status
- Billing record
Not sent as account data
- Conversation text
- Personal memory content
- Local teaching material
Persistent memory
Keep useful context without pretending that every detail deserves forever
Benefit. Kvantia is being built to remember selected facts, preferences, projects and decisions across sessions. You should be able to inspect, correct and remove what it remembers.
Example. Instead of explaining your project, preferred working style and previous decision every week, you continue from the context you chose to keep.
Boundary. Persistent memory is not perfect recall. It can be incomplete, outdated or misinterpreted. Important decisions still need source material and human verification.
Before this feature can be called available, it must ship its controls: a memory viewer; edit and correct; delete one item; delete all memory; a source or date for each memory where possible; a clear export and backup policy; and defined behaviour when a subscription or licence ends.
Memory lifecycle
How a memory is made, kept and controlled
Persistent memory is in development. This is how it is being built to work.
A conversation produces possible memories
Candidate memories are proposed, not silently stored.
Temporary context is separated from what is worth keeping
Not every sentence deserves to be permanent.
Saved memories connect to topics, people, projects or decisions
Links make later retrieval meaningful.
Relevant memories are retrieved when a new request needs them
Context returns when it is useful.
You review, correct, pin or delete
The person stays in control of what is kept.
The last stage feeds back into the first — the loop continues.
Controls at every stage: inspect · correct · pin · forget.
Your context, by choice
Teach it what matters to you — without handing over everything
Benefit. Kvantia is designed to learn from deliberate teaching: a correction, an example, a document or a repeated preference you choose to keep. The goal is a personal system shaped by use, not a global model trained on private conversations.
Example. Show Kvantia the structure you use for trip planning, the constraints you always apply and a good previous result. The next plan starts closer to your way of working.
Boundary. “Learns from you” must never mean every sentence silently becomes permanent. The interface keeps chat context, saved memory and an installed skill visibly separate. Teaching is in development.
Teaching loop
How teaching is meant to flow
- 01
Show an example
Give Kvantia a concrete instance of what you want.
- 02
Explain what matters
Point out the constraints and details that count.
- 03
Kvantia proposes what it learned
It shows back its understanding before keeping it.
- 04
You confirm, correct or discard
Nothing is saved without your decision.
- 05
It applies in a future task
The next result starts from your confirmed way of working.
Confidence should be earned
A better answer can also be an honest “I don’t know”
Benefit. Kvantia is being designed to connect an answer to what it currently knows and to expose uncertainty when that knowledge is weak or conflicting.
Example. Ask about a project decision. Kvantia should distinguish a remembered fact, a conclusion and a missing source instead of presenting all three with the same confidence.
What the interface should show
- known from saved memory;
- inferred from current context;
- based on a user-provided source;
- uncertain or conflicting;
- not known.
Boundary. Grounding reduces unsupported answers; it does not make errors impossible. Kvantia can still misunderstand, retrieve the wrong context or reason incorrectly. High-impact decisions require verification.
Choose where it lives
Desktop, mobile or home server — only when that build is ready
One personal AI, on a device you control. Each option shows its own status. None is a finished download yet.
Mobile
In development. Conversation and lightweight interaction on the go. Supported iOS/Android versions and how much runs on device are named at test.
Home server
Planned. An always-on personal instance on your own network. Docker/NAS support, RAM/storage, network security and backup are your responsibility.
Reusable skills
Start with a capability, then adapt it to your way of doing things
Status: Planned. A skill is intended to package a defined body of knowledge, examples and behaviour so Kvantia does not need to be taught from zero. After installation, the skill can continue adapting to your confirmed preferences.
A skill card will eventually show
- what the skill knows and does;
- what data it can read;
- whether it can connect externally;
- author and version;
- evaluation and limitations;
- price and licence;
- uninstall and delete behaviour.
How a skill layers up: Kvantia core + an installed skill (defined knowledge and behaviour) + your confirmed preferences and examples = a capability adapted to you.
Boundary. Marketplace examples and prices are not current offers while the marketplace is planned. When it opens, skills carry their own status and licence — there is nothing to purchase here today.
Useful within local limits
Local AI should be honest about the trade-off
Kvantia is not trying to be the largest general-purpose cloud model. It is designed around a smaller personal system that can live close to its user and become useful through context, memory and teaching.
| Local personal AI | Large cloud model |
|---|---|
| Personal data can remain on owned hardware | Requests are processed in provider infrastructure |
| Capability is constrained by the device | Access to very large general models |
| Can be shaped around one person | Same base service used by many people |
| Works offline for confirmed local features | Usually depends on a connection |
| You manage hardware, storage and backup | The provider manages infrastructure |
Boundary. Kvantia may not match a frontier model on broad coding, writing or world knowledge. The proposition is personal continuity and control, not winning every benchmark.
Personal outcomes
Start with continuity, not a list of AI tricks
Each outcome is labelled by the capability it depends on, so nothing here reads as available before it is.
Continue a long-running project
Keep approved decisions, vocabulary and working preferences connected over time. Requires persistent memory — in development.
Build a private knowledge companion
Teach selected personal material and ask questions against that context, without it becoming part of a central conversation service. Exact source and file support will be confirmed.
Practise a skill over time
Use repeated examples and corrections so the experience develops with you. Depends on teaching and skills — in development and planned.
Use a personal AI where connectivity is limited
Continue confirmed local features without a constant cloud-model connection. A periodic licence renewal still needs an occasional connection.
Different by design
Different by design, not by a longer feature list
| Question | Typical cloud assistant | Kvantia direction |
|---|---|---|
| Where is inference performed? | Provider infrastructure | On supported user-controlled hardware |
| Who holds conversation memory? | Provider account or service | The local Kvantia instance |
| Does every conversation become permanent? | Depends on service settings | No — memory should be visible and controllable |
| Can the user inspect learned context? | Often limited | Memory controls are a product requirement |
| Does it require internet? | Usually | Local features are offline-first; licence and optional services may connect |
| Is it the largest general model? | Often a frontier model | No — it optimises for personal continuity and control |
This compares a typical cloud assistant; it does not claim every provider trains on all user conversations.
Honest limits
A personal AI is more trustworthy when its boundaries are visible
- It is not guaranteed to be correct.
- It is not a search engine or a continuously updated copy of the web.
- It is not yet a finished marketplace of expert skills.
- It is not the largest cloud model running on a small device.
- It does not remember perfectly or understand intent perfectly.
- Local operation does not remove your responsibility for device security and backup.
- A working prototype is not the same as a generally available product.
Questions people ask
Features FAQ
Does Kvantia send my conversations to Kvantim?
The intended architecture keeps conversation and personal-memory processing on the device. Account, licence and billing data use the account service. This answer is updated if crash reports, support exports, backup or optional integrations transmit content.
Does “grounded” mean Kvantia never makes mistakes?
No. Grounding and uncertainty states are designed to reduce unsupported answers and make limits clearer. Errors remain possible.
Can it remember everything?
No product should promise perfect or unlimited memory. Kvantia is being built to retain useful, user-controlled context across time.
Can I teach it without coding?
That is the intended experience: examples, corrections and explicit memory or skill controls. The exact teaching capabilities carry their current status.
Is Kvantia AI the same as Kvantim LLM?
No. They are two directions in the same Kvantim ecosystem. Kvantim LLM is a classic transformer model rebuilt from an open-source model base and designed for further training. Kvantia AI follows a separate personal, local AI direction.
Is Kvantia Harness part of the same company?
Yes. Kvantia Harness is the Kvantim ecosystem’s business product for supervising AI work with rules, approvals and an audit trail.
Follow the product as it becomes real
Join early access with the right expectations
Tell us which device you want to use and what you want your personal AI to remember or learn. We invite people in stages as each capability meets the status described on this page.
Early access to the working prototype — not a finished product. No sensitive personal material needed to join.