Context is not a prompt problem.
It’s a systems architecture problem.
And solving it requires research that lives in production, not just papers.
Empromptu is both an AI research lab and a product company. We invent new architectures for how AI systems reason, remember, and improve, and we ship those ideas directly into the platforms our customers run every day
Dr. Sean Robinson
Co-founder and CTO
Empromptu.ai
Dr. Sean Robinson, co-founder and CTO, came to AI from computational astrophysics. He spent years building systems that process massive, noisy datasets to find signals that would otherwise disappear.
The challenge was never the algorithm. It was the architecture.
How do you design a system so the right information surfaces at the right time, regardless of scale? Whether the data comes from space, hardware, healthcare, or enterprise systems, the problem is the same: signal is fragile, noise compounds, and naive approaches break.
Production AI faces the same problem.
Most of the field treats it as prompt engineering. How do you squeeze more into the context window? How do you summarize without losing too much? How do you template your way to better results?
We think that approach hits a ceiling. The question isn't how to squeeze more in. It's how to build memory, attention, and optimization as infrastructure that works at any scale.
Our Research Focus Section
Our research focuses on foundational problems in production AI systems. These are not isolated experiments. They are architectural primitives that ship directly into customer products. The areas below are representative of our work, not exhaustive.
Empromptu's research lab is active and expanding. Current areas of exploration include:
Memory as a persistent layer
Published as Infinite Memory. Shipped 2025.
The standard approach treats memory as part of the prompt. Everything gets injected into the context window, every time. This creates hard limits on conversation length, document size, and complexity.
We propose separating memory from context entirely. Memory becomes a persistent layer that captures interactions, decisions, and state over time. The context window becomes a retrieval target, not a storage container.
This reframes the problem from, how much can we fit? to what should we retrieve?
Attention as self-optimizing infrastructure
Published as Adaptive Context Engine. Shipped 2025.
Most systems treat context selection as static. Rules determine what gets included. Engineers tune until it works.
We think attention should optimize itself. Which context leads to good outcomes? Which creates errors?
The system should learn from results and improve over time, treating attention as an evolving problem rather than a fixed configuration.
Domain knowledge as architectural foundation
Published as Custom Data Models. Shipped 2025.
Foundation models are general. They lack a deep understanding of any specific domain. The standard fix is fine-tuning or prompt engineering.
We propose making domain knowledge a first-class element of system architecture.
Structure the domain explicitly. Define entities, relationships, constraints. Give the AI a foundation to reason from rather than text to pattern-match against.
Ongoing Research
Empromptu’s research lab is active and expanding. Current areas of exploration include:
FENMs - Functional Expert Nano Models
Reasoning architectures for multi-step workflows
Agent output simulation
What others are saying.