Personal context
Kvantia AI — local continuity for an individual. Status: Working prototype.
Invest in one AI company with three connected paths to market
Kvantim is building a personal local AI, a supervision layer for business AI work and a trainable transformer model. The products solve different problems, but they compound one company's capability in deployment, control, evaluation and AI systems that can adapt to a specific owner or domain.
This investor page is published by CODARS DESIGN, SIA (registration No. 40103472693), Latvia, trading as Kvantim. The company thesis, product status, business models, milestones and risks below are current. Round terms, detailed financials and the data room are shared directly with qualified investors — nothing on this page is an offer of securities.
Why Kvantim exists
The next useful AI systems need more than a bigger generic model
General cloud models made powerful AI widely accessible. They also exposed three gaps. Personal AI lacks durable, user-controlled continuity. Business agents need execution controls before they can be trusted with real systems. Domain users need a path to train and evaluate model capability around their own requirements.
Kvantim is building one company around those three gaps: personal context, controlled execution and trainable model capability.
One strategy
Three layers, one company
Controlled execution
Kvantia Harness — rules and approvals for business work. Status: In early access.
Trainable capability
Kvantim LLM — a classic transformer model rebuilt from an open-source base. Status: In development.
Portfolio logic
What actually compounds across the three directions
- Evaluation methods and failure analysis.
- Local, on-premise and controlled deployment experience.
- Product security, permissions and audit design.
- Customer discovery and real workflow requirements.
- Distribution across personal, business and technical buyers.
- One team, company operations and investor relationship.
What does not get conflated
- Kvantia AI and Kvantim LLM are not the same architecture.
- Harness is not itself the model.
- A future integration is not current traction.
- A product concept is not trained model weights.
- Open-source foundations do not automatically become proprietary IP.
Evidence today
Current state, in the same status language used across every site
No customer counts, revenue or benchmarks are published here. Demonstrations and evaluation evidence are shared with qualified investors through the data-room process.
Kvantia Harness
In early access. A supervised business-work flow can be demonstrated on request. Evidence shared with qualified investors.
Kvantia AI
Working prototype. The core personal-AI interaction runs today; persistent memory is in development.
Kvantim LLM
In development. A classic transformer model rebuilt from an open-source base; the base model and modifications are disclosed to investors under review.
Commercial paths
Each direction has a buyer and a route to revenue
This is a proposed strategy, not a claim about current contracts.
| Direction | Initial buyer | Entry offer | Revenue model |
|---|---|---|---|
| Kvantia Harness | European SMEs / operations teams | Private demo → one-workflow pilot | Licence, implementation, support |
| Kvantia AI | Privacy-conscious early adopters | Prototype / early access | Subscription or licence per personal instance |
| Kvantim LLM | Domain partners / enterprises | Paid discovery or model pilot | Training project, deployment licence, support |
What can become hard to copy
The moat is the system the company proves
Defensibility is presented here as a buildable thesis, not a finished asset:
- Product & workflow IP — capability controls, approval policy, credential handling and audit design inside Harness.
- Model & training know-how — Kvantim's modifications to the open-source base, training pipeline, data curation and evaluation.
- Personal/local architecture — Kvantia AI's memory, teaching and on-device operation, once demonstrated.
- Evaluation & deployment knowledge — repeatable tests, failure datasets and operational learning.
We do not claim a proprietary full stack until ownership and licences can be mapped layer by layer.
What this round unlocks
Use of funds — by workstream
- Kvantim LLM — compute, rights-cleared data, training and evaluation.
- Kvantia AI — persistent memory, local performance, packaging and onboarding.
- Kvantia Harness — product hardening, desktop/runtime, integrations and pilots.
- Team — the critical roles this round funds.
- Security & legal — threat model, privacy, licences and contracts.
- Operations — hosting, tooling, finance and contingency.
Exact amounts, allocation and milestones are in the investor deck. No figures are published on this page.
Financing plan
What we intend to prove with this round
- Milestone 1
Prove one controlled business workflow, repeatedly In progress
Pilot/customer workflows with completion and approval metrics, a named deployment boundary and commercial-conversion evidence.
- Milestone 2
Open Kvantia AI early access around a defined core Planned
Supported hardware, onboarding success, local conversation, an approved memory capability and privacy/data-flow verification.
- Milestone 3
Complete the next Kvantim LLM training and evaluation gate Planned
A model artifact, documented base and licence, data rights and a reproducible evaluation.
- Milestone 4
Demonstrate ecosystem leverage Planned
One shared technical or commercial asset produces measurable benefit across two directions.
What has to go right
We show the risks because this is deep product and model work
| Risk | Why it matters | How it is staged |
|---|---|---|
| Three directions dilute a small team | Execution focus | Milestone gates and named owners |
| Local AI underperforms large cloud models | Product value | A narrow personal-continuity proposition, tested on target hardware |
| Open-source licence / IP constraints | Commercial rights | Legal review and a component map |
| Model training costs or results differ from plan | Capital risk | Staged runs with exit criteria |
| Harness relies on external model providers today | Margin / data dependency | Explicit boundary and provider choice |
| Enterprise sales cycles are long | Runway | Narrow, tightly-scoped workflow pilots |
We never claim a risk is removed — only how it is staged, measured and governed.
The data room
What qualified investors receive
- Investor deck and financial model.
- Product demonstrations.
- Technical architecture summaries.
- Model base, licence and IP map.
- Status and evaluation evidence.
- Risk register and milestone plan.
We share public product information openly. Commercial, customer, model and financing detail is available to qualified investors through the data-room process.
The round
Request the investor deck
Tell us who you invest for, your typical stage and what you want to examine. We will reply with the appropriate material and next step. We do not ask for a commitment through this form.