Research
Devlar is an applied AI research company. We work on four questions, and we build a working system for each one so that every finding is tested against real workloads.
Research areas
Four open questions about AI systems that have to work outside the lab.
Agent reliability
Why do autonomous agents fail in production, and whose fault was it?
Failure modes of multi-step agents, telling a broken credential from a broken upstream from broken logic, and judging a run by what it produced rather than how it exited.
Projects: AI Workforce, Docket, Agent Starter Kit
Memory & context
How should an AI system remember?
Persistent, structured and editable memory that carries context across sessions without dumping transcripts into the prompt.
Token efficiency
How little frontier-model compute does a task actually need?
Agent loops quietly multiply token spend. We study layered architectures that reserve large models for reasoning nothing cheaper can do.
Projects: AI Workforce
Human-AI interaction
What makes a person trust a personal AI?
Privacy, ownership of your own data and memory, and how people build working relationships with an AI companion over time.
Projects: Alvi
Research projects
We test every idea against a working system. These are the systems.
AI Workforce
In progressAgent reliability · Token efficiency
Can a team of specialist agents carry a multi-step goal end to end, reliably and at a predictable cost?
Docket
In progressAgent reliability
Automatic fault attribution: was it the credential, the upstream API, or the workflow's own logic?
Agent Starter Kit
Reference buildAgent reliability
What is the smallest agent scaffold that is ready for production rather than ready to demo?
Synapse
In progressMemory & context
Structured, editable, consented memory for AI systems, in place of transcript dumps.
Alvi
Early stageHuman-AI interaction · Memory & context
A personal AI companion owned by the person using it, built to study privacy, ownership and trust.