Research
Research that turns information into understanding—and understanding into action.
The world is full of signals: numbers, documents, conversations, and quiet details that don’t fit neatly into a dashboard. Our research asks a simple question: what would it take for AI to help people see clearly, reason carefully, and move forward with confidence?
We explore methods and build prototypes at the intersection of intelligence, systems, and real work. When ideas hold up, we share them—through writing, tools, and collaborations—so others can learn, adapt, and build.
Making sense of messy reality
Useful insights rarely live in one place. They are scattered across sources and shaped by context—what changed, what stayed the same, and what people actually mean. We study how AI can bring scattered pieces together without losing nuance, so a team can align on the same picture of reality.
Reasoning with evidence
Good decisions aren’t just fast—they are grounded. We explore ways for AI to connect statements to supporting material, highlight uncertainty, and make room for debate. The goal is not a single “answer,” but a clearer set of options and the reasons behind them.
From ideas to everyday practice
Research matters when it survives contact with day-to-day work. We prototype, test, and iterate with real constraints—limited time, imperfect inputs, and changing priorities. Over time, we try to turn what we learn into patterns that teams can reuse and build on.
Focus areas
Our research follows the same arc as real deployments: turning fragmented signals into shared context, turning context into decisions, and learning from outcomes over time.
From signals to context
Connecting scattered data and documents into a coherent view—so teams can start from the same facts.
Clarity, grounded in evidence
Reasoning and writing that stay close to the underlying material, with uncertainty made visible rather than hidden.
Workflows that move
Human-AI collaboration patterns that fit real teams: handoffs, approvals, and action in the loop.
Measured in practice
Evaluation and observability that track quality, cost, and impact—so systems improve rather than drift.
Projects
Projects are where our questions become concrete. Each one is a small bet on a better way to understand, decide, and act.
OpenPort Protocol (OPP)
PreviewAn open protocol for reliable AI tool access—authorization-aware discovery, draft-first writes, and stable responses that hold up in real workflows.
Research philosophy
Our goal is to turn complexity into clarity, trust, and intelligence—so teams can move from questions to decisions, and from decisions to action.
We start with the workflow, not the demo. We study the moments where information is fragmented and responsibility is real—handoffs, approvals, and decisions that have consequences in the world.
We optimize for clarity that people can share. Claims should stay close to underlying material, and uncertainty should be visible—so teams can reason together and move with confidence.
We treat systems as living. Evaluation, monitoring, and feedback loops are how prototypes become practice—and how they keep improving as data and needs change.
When it helps others build, we share lessons through writing, prototypes, and collaborations. Some work may be shared selectively due to privacy, sensitivity, or partner constraints.