Research for
Abundant Intelligence
Merge Research studies how institutions can deploy increasingly capable systems without losing policy coherence, operator authority, or public legitimacy.
What we study
Our research sits between models and institutions: context systems, constrained workflows, evaluation, and human authority.
Operational routines with human authority built in
Merge encodes procedures, review points, and escalation logic so AI can increase throughput without dissolving accountability.
Institutional context carried across every decision
Policies, SOPs, case history, and system state become reusable context that keeps outputs tied to operational reality.
Deployment inside existing systems, not beside them
We design workflows that sit inside your real tools, data boundaries, and approval chains rather than forcing a parallel operating model.
Custom systems for consequential institutions
Merge does not ship generic copilots. We build domain-specific routines, observability, and controls for institutions operating under real constraints.
Core research areas
Merge Research focuses on the system requirements for high-stakes institutional AI rather than model capability in isolation.
Institutional Memory as a Context Layer
Represent procedures, approvals, system state, and tacit knowledge in forms that AI systems can actually use.
Policy-Constrained Agent Workflows
Design agents that act within law, procedure, and organizational policy rather than treating governance as post-processing.
Boundary-Case Synthesis
Generate rare but high-consequence scenarios that expose weak assumptions before deployment.
Human Authority in Self-Improving Systems
Preserve override, accountability, and institutional legitimacy as workflows become more capable.