
Technology
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AI Automation
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YC W26
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Valuation:
Undisclosed

Last Updated:
March 24, 2026

Builds an AI automation platform that learns from user behavior via system events and anonymized network traffic, then proactively generates and executes personalized workflow automations with no prompts and no manual setup.
Proactive, zero-prompt automations that learn from system events and anonymized network traffic. Privacy-first: no screen recording. Targeting knowledge workers at high-growth tech companies.
Fully proprietary closed-source ML stack. OS-level agent development (likely macOS/Linux). 'No screen recording' is deliberate competitive wedge against Adept AI and legacy RPA. Lean founder-only build phase.
Cofia's ML models passively observe system events and network traffic to discover repeatable user workflows and automatically generate executable automations without any user prompts or manual configuration.
It's like having an invisible assistant that watches how you work and quietly starts doing the boring stuff for you.
It's like a sous chef who watches you cook the same meal five times, then one morning you walk into the kitchen and breakfast is already made—exactly how you like it.
Cofia uses reinforcement learning from user feedback to rank and prioritize which discovered automations to surface or execute, calibrating trust levels to each user's comfort with autonomous action.
The AI learns which automations you'll actually trust and stops bugging you with ones you won't.
It's like a new dog that starts by asking permission before fetching your slippers, but after a few weeks just brings them to you every morning because it knows you'll say yes.
Cofia aggregates anonymized workflow patterns across its user base to identify common automation opportunities, enabling new users to benefit from pre-built automations discovered from collective behavior—without exposing any individual's data.
New users get smart automations on day one because the system already learned common workflows from thousands of other people—without ever seeing anyone's private data.
It's like moving to a new city and your GPS already knows the best shortcuts because thousands of drivers before you figured them out—without anyone knowing where you specifically went.
Paola Martínez (Stanford CS undergrad & MS&E MS concurrent, 2019 Mayfield Fellows Program, Schmidt Futures Impact Fellow) was Senior Product Manager leading retention at Brilliant.org. Moses Wayne (CS & Economics at Duke) was Engineering Director at Duolingo leading Monetization to >$1B in revenue annually. Deep expertise in user engagement and monetization at scale enables them to build automation that learns passively without requiring user effort.