---
# System prepended metadata

title: 'Jan: Personal Intelligence'

---

# Jan: Personal Intelligence

> Nothing was your own, except the few cubic centimeters inside your skull.
> — George Orwell, 1984

## Overview
 
Jan is a personal intelligence that is private, context-aware, and proactively helps you get things done.

- Jan is designed to be self-hostable and private-by-default
- Jan understands you and your big-picture goals
- Jan understands is connected to your systems and understands your context
- Jan has a "2nd brain", i.e. memory & personal knowledgebase
- Jan can take on delegated tasks, and check in with you for approvals
- Jan is proactive and takes initiative to identify actionables from noise
 
Jan consists of a self-hosted server, and a consumer web/mobile app. In the long-term we believe most users will interact with Jan like a real person - via messaging apps, voice calls, or email.

The Jan team previously built one of the more popular local AI apps with >4 million downloads, and trained two popular open-source agentic models with >100k downloads. 

## Problem

The biological human brain does not have sufficient attention and agency bandwidth to keep up with the modern world. We try to make progress on a few key important goals (e.g. job, family, health), against a blizzard of:

- Planetary-level information firehoses
- [Super-Dunbar number](https://en.wikipedia.org/wiki/Dunbar%27s_number) of communication channels
- An accelerating world that generates more tasks that we can handle

Human today rely on a limited, serial mental triage system and context window to provide agency, focus and persistence to work towards our goals. We are supported by a mishmash of passive productivity tools that fundamentally rely on human initiative to be effective (spoiler: doesn't work). 

## Solution & Product

Jan is a personal intelligence that is private, context-aware, and proactively helps you get things done.

Beneath the hood, Jan is an agent with 5 core components:

- Your Profile (i.e. you, your background, your preferences, your goals)
- High-level goals
- Todo list
- Knowledgebase (e.g. notes, articles, videos)
- Connectors to your systems (e.g. calendar, email, notes, files)

Jan's agent has two key capabilities:

- Jan knows about you via its long-term memory
- Jan can do tasks for you via its action engine

## Market Opportunity

We believe that personal intelligence will grow to be a TAM of approx $38-48b annually. 

- ~400m users who already pay for, or are realistic future payers for self-paying productivity tools[^1]
- Blended ARPU of ~$8-20/mth (estimate driven by task offload value)
- Implies $38-48b TAM annually

This TAM does not take into account potential longer-term upside from "agent platform" revenue  (e.g. marketplace, third-party tools, API usage), nor Bitwarden-style self-hosted and small-team deployments that can layer on SMB/departmental contracts that nudge ARPU higher.

## Why Now?

We believe a personal intelligence is now possible:

- Agents and connectors: e.g. [MCP ecosystem](https:/modelcontextprotocol.io)
- Long-term memory and task execution: e.g. [MemGPT](https://arxiv.org/abs/2310.08560)
- Personal knowledgebases & Retrieval: e.g. [Obsidian](https://obsidian.md/), [Pocket](https://getpocket.com/home), [Context Retrieval](https://www.anthropic.com/news/contextual-retrieval)
- World model and agentic design patterns: e.g. [JEPA](https://arxiv.org/abs/2403.00504), [Voyager](https://arxiv.org/abs/2305.16291)

Furthermore, a personal intelligence can be viable from a price-performance perspective: LLM inference costs have dropped significantly, and [quantization-aware training](https://pytorch.org/docs/stable/quantization.html#quantization-aware-training) enables us to optimize gross margins in the future with Jan's own agentic models. 

## Business Model

Jan's business model is a consumer subscription model that starts at $5-20/month, and extends up to >$200/month tiers for power users.

On unit economics, Jan targets 45%+ gross margins from launch, that can expand to 70%:
1. Data compaction and compression
2. Migrate higher % inference to Jan's own optimized models
3. Optimize our in-house infrastructure spend rather than pay hyperscaler markups.  

## Traction & Metrics

> TODO: fill in with real numbers after pilot

- Traction
  - Y DAU
  - Y 7-d Retention, Z 30-d Retention
  - X waitlist signups
  - Y active weekly pilot users completing ~N tasks each.
- Agent Success Rate
  - A% of submitted tasks successfully completed
  - B% fully automously completed
  - C% partially autonomously completed (requires human-in-the-loop)
- Connectors live: 
  - Gmail, Outlook, Notion, Google Drive, Slack
  - 15 additional connectors in development.

## Go-to-Market Strategy

> TODO

Jan's near-term TAM is to focus on people who already pay for personal productivity tools, who would pay for Jan to free up their time. 

<!-- 
Jan grows through a deliberately constrained, invite-only paid model that optimizes for quality of experience and density of value rather than raw top-of-funnel. 

- Paid from Day 1 with proof-of-value: Jan enters users' lives as a paid $5 product, i.e. a high-intent product vs. a free toy
- Invite-only access with a visible waitlist: new users join a public waitlist, access is granted in controlled cohorts to manage infra and support, with existing power users earning a small number of invites they can share with friends and colleagues
- Narrative as a growth lever: lean into the personality of Jan (slightly rude, extremely effective) through founder content, live demos, and case studies that show real tasks being handled end-to-end
- Community: Build a community of early adopters and power users who treat Jan as infrastructure for their lives

This GTM strategy is designed to keep the product's perceived value and actual reliability ahead of growth, using invite-only paid access to concentrate on the right users, generate strong unit economics early, and then expand.  -->

## Competitive Landscape

Jan competes with a variety of AI chatbots, but is differentiated by its clear focus on a personal intelligence (vs. horizontal LLM or Agent products). 

- Poke, by Interaction Company
- ChatGPT, by OpenAI
- Claude, by Anthropic
- Gemini, by Google

## Technology & Differentiation

Jan's core technology is are proprietary long-horizon agentic models, agent infra that is optimized for existing personal productivity systems (e.g. email, messengers, calendars, notes), and data engine, and in-house infrastructure. 
 
- Jan v1, Jan v2 agentic models with long-horizon capability, with proprietary eval and data feedback loops
- Jan Agent orchestration layer (e.g. long-term goals, planning, workflow, state management)
- Jan Agent Infra infrastructure layer (e.g. MCPs, browsers, VMs, Telephony)

We believe that [The Model is the Product](https://vintagedata.org/blog/posts/model-is-the-product), and have taken the contrarian strategy of training our own Jan's own agentic models that we believe will be the differentiator in capability and cost competition in the long term. 

## Team

Jan team previously built: 

- Jan, a desktop-based local AI app (4 million downloads)
- Jan models, an agentic model capable of long-horizon tasks

## Financials & Forecast

### Revenue Projections
| Year | Revenue | Paying Customers | Key Assumptions       |
| ---- | ------- | ---------------- | --------------------- |
| 2026 | $XM     | 2,000            | Paid beta, $Y/mo ARPU |
| 2027 | $10M    | 20,000           | $50/mo ARPU           |
| 2028 | $100M   | 84,000           | $A/mo ARPU            |

### Gross Margin Path 

- 55% → 70% as Jan's in-house models get optimized and cheaper to run
- Jan is run from our own datacenter, and we do not pay hyperscaler markups

## Funding Ask
<!--
- **Raise:** $XM seed at a $XXm-capped SAFE
- **Runway:** 12 months to reach $10M ARR exit velocity for Series A.
- **Use of funds:**
	- 60% product & engineering (agent reliability, connectors, infra)
	- 20% GTM (marketing-led sales, growth experiments, community)
	- 10% operations & compliance (security reviews, SOC2)
	- 10% contingency / working capital
-->

## Risks & Mitigation
<!-- 
1. **Agent reliability:** 
    - Risk: Agent success rate is <70%, affecting user adoption
    - Solution: Invest in evaluation harnesses, continuous training pipeline, and create datasets from failure cases
2. **Data security & compliance:** 
    - Risk: Todo apps hold a lot of private, confidential information
    - Encrypt data at rest, pursue SOC2, invest in threat defense
3. **Customer education:** 
    - Autonomous agents are new for mainstream users. 
    - Invest in marketing, customer education, and customer success -->

# Appendix
<!-- 
## Jan's Desktop App

Jan's main repo (with 38k stars) will continue to be public:
- Grows into a multi-repo product
- `janhq/jan` will be an informational repo
- `janhq/server` will be the more actively maintained server
- `janhq/desktop-server` will be the current Jan Desktop

## Jan's Repo Structure

Jan's will have a new repo, that 

- `janhq/jan`: our main repo with 36k stars

Jan's AI Agent is delivered via Jan's servers: 

- `janhq/server`: Recommended infrastructure/backend (API, Agent), AGPLv3 license
- `janhq/desktop-server`: Lightweight desktop-based server, MIT license

Jan is used by consumers through clients that connect to Jan Servers:
- `janhq/mobile`: Jan mobile apps (iOS, Android), closed source
- `janhq/clients`: Jan web, browser extension, etc

Jan's model training pipeline is closed-source: 

- `janhq/models`: Jan's Agentic models
- `janhq/training`: Jan's Training Pipeline 
- `janhq/datasets`: Jan's Dataset (and crown jewels)

We may open-source Jan's smaller or last-generation models, but keep high-capability models proprietary. 

_Example: Bitwarden_
![image](https://hackmd.io/_uploads/Hy9dOtax-g.png) -->

[^1]: https://vintagedata.org/blog/posts/model-is-the-product
[^2]: https://x.com/MatternJustus/status/1876829656483086601