What We're Solving
The hardest constraint in professional work isn't talent, capital, or market opportunity. It's human time. You can only be in one place, handling one thing, at one moment.
The Memory Problem
Current AI forgets context between conversations. It doesn't know what you discussed last week, what matters to your clients, or how you make decisions. We're building systems with perfect recall—so your assistant actually knows you.
The Understanding Problem
Tools execute commands. They don't understand context, intent, or nuance. We're focused on contextual learning systems that grasp the "why" behind your work—so AI can handle complexity, not just simple tasks.
The Coordination Problem
You juggle emails, meetings, projects, decisions—all competing for your attention. We're building intelligent coordination layers that manage workflows while you focus on what requires your judgment.
The Scale Problem
Research breakthroughs don't matter if they can't run at speed and cost. We're engineering infrastructure that makes sophisticated AI assistance economically viable—not just technically possible.
The Team
Three disciplines. One focus: building AI systems that actually work in the real world—at scale, at speed, with safety built in from the start.
Research
Developing the deep memory architectures, contextual learning systems, and coordination algorithms that make intelligent assistance actually work. Publishing open research. Advancing the field while solving real problems.
Engineering
Building production infrastructure that runs sophisticated AI at enterprise scale. Optimizing for latency, cost, and reliability. Making research breakthroughs deployable in weeks, not years.
Operations
Managing go-to-market, partnerships, compliance, and organizational systems. Ensuring we can move fast while meeting enterprise security standards and building trust with customers and regulators.
What we value
and how we act
Building AI systems that people trust with their work requires more than technical capability. These principles guide how we build, how we make trade-offs, and how we show up when things get hard.
01
Ship real value, not research theater.
Breakthroughs in the lab don't matter if they never reach users. We prioritize deployable systems over elegant papers. If it can't run in production at reasonable cost and latency, it's not done. Research informs product. Product validates research.
02
Safety is infrastructure, not compliance.
We build safety into the architecture—memory constraints, behavioral bounds, audit trails. Not because regulators demand it, but because AI that people trust with their work needs to be predictable, transparent, and aligned with their goals from the ground up.
03
Understand the problem before claiming the solution.
We spend time with users in their environments. We ask what's actually broken, not what sounds impressive. The best AI systems solve real workflow friction—not the problems we wish existed because they'd make better demos.
04
Move fast. Build trust faster.
Speed without trust is recklessness. We move quickly on technology while being deliberate about transparency, data handling, and communication. Users need to know what BAP remembers, how it makes decisions, and how to correct it when it's wrong.