Two prerequisites for
a real AI agent.
We believe there are two fundamental prerequisites that must be solved before an AI agent can meaningfully act on your behalf: deep context and reliable capability.
Context
Most AI assistants only access data available through APIs — your email, calendar, maybe Slack. But this is a fraction of who you are. The most valuable personal data is locked inside walled gardens that don't offer API access.
Where your data actually lives
Twitter direct messages, interactions across all platforms, Instagram direct messages — all of this personal data is scattered across platforms. When combined with data that can be granted via APIs (your Gmail, Calendar, Slack), we can finally construct a user profile with the depth for an agent to truly understand who you are, what you want, and what you may need tomorrow or next week.
This allows agents to proactively prepare rather than waiting for you to give a request.
We're not just obtaining data from easily accessible APIs. We're obtaining the entire digital footprint of a user.
For the first time in the internet era, we can aggregate our own data and leverage it to provide value to ourselves — rather than relying on platforms to act nicely and give us this data back. Intelligence has always flowed from users to platforms. We're reversing that flow.
The Personal Intelligence Pipeline
Just like companies build pipelines for business intelligence, we build a personal intelligence pipeline — one that everyone should have and will have. It processes decades of data you've created across all platforms.
Raw Inputs
App connectors, files, conversationsHigh-fidelity personal data, collected at the source.
Indexing
Full-text search, embeddings, vector searchMulti-modal retrieval layer for fast, contextual lookup.
Features
Aggregates, behavioral features, graphsDerived behavioral signals from raw data.
Detection & Modeling
Topic models, change points, anomaliesStructure discovery and change detection.
Insights & Presentation
Dashboards, RAG narratives, nudgesGrounded, explainable outputs delivered as proposal cards.
Capability
A personal assistant agent must be able to operate software interfaces on your behalf. Due to the limitations of APIs, the things agents can do are by nature limited. GUI agents — agents that can interact with graphical interfaces — are always needed to fill the gap. In most cases, they are the only way to get things done.
But the current state of GUI agents is fundamentally broken.
The problem with current GUI agents
Most GUI agents today use a vision-based approach. They take a screenshot, feed it to a vision model, and get back a low-level action to execute:
After each granular step, the agent takes a new screenshot and asks the vision model what to do next. This approach is general, but comes with massive trade-offs in cost, latency, and reliability — especially for long-horizon tasks where context explodes and errors compound.
Our approach vs. the status quo
Three things that matter
Speed
If you take 30 seconds to find a contact and draft a message, the agent should take 3 seconds — not 3 minutes. Actions must be faster than doing it yourself.
Reliability
When you want to send a message, you need 100% confidence it will be sent with the exact content you consented to. Deterministic actions build trust over time.
Cost
If it costs $3 to send an email via a GUI agent, adoption will be zero. Our pre-trained agents drop the cost to effectively nothing.
We have pre-trained our GUI agents to execute deterministic actions on specific platforms. Instead of guessing what to click next, our agents know exactly what to do — every time. This makes actions fast, reliable, and virtually free. It builds trust so users can gradually delegate more sensitive tasks over time.