Documents, databases, research material, and domain-specific resources with their context intact.
Technology
Systems that remain legible under real use.
Pluri builds the application, intelligence architecture, and production reality—not a thin layer around someone else’s product.
Technical architecture
A practical architecture for systems that must be trusted.
Pluri technology is organized around source quality, model behavior, review paths, working interfaces, and production responsibility.
Search, ranking, semantic matching, model behavior, and source trails.
Custom models, computational workflows, application logic, and evaluation paths.
Working interfaces, human review, production infrastructure, and iteration from real use.
Public systems
Systems where Pluri’s authorship is visible.
The systems below make the scope of Pluri’s work clear: product decisions, system architecture, and engineering are owned in-house.
Public deployment · built and operated by Pluri
A complete AI search system for health-literacy resources.
Pluri designed and built the public experience, retrieval behavior, and intelligence backbone for the Institute for Healthcare Advancement. The search and AI infrastructure are operated by Pluri.
Visit IHA Search ↗
Pluri product · designed and built in-house
A local-first workspace for notes, canvases, and vector thinking.
Lattice is a Pluri product that brings rich notes, visual canvases, and spatial organization into one working environment.
Open Lattice ↗
Pluri platform · algorithm engineering
Visual algorithm workflows for Python-based technical work.
StremeCoder helps teams design, test, and organize sophisticated computational workflows through a visual, inspectable development environment.
Discuss a workflow ↗System capabilities
Specialized systems for work that generic tools cannot carry.
Pluri builds search, prediction, clustering, review, and implementation systems shaped around the actual material and operating constraints.
Implementation fit
Technology is selected around the operating reality, not the trend.
Large language models can be useful components. They do not replace the work of designing a system, establishing its boundaries, evaluating its behavior, and keeping the whole experience operationally sound.
Use-case fit before tooling Review needs before automation Operational ownership before scale
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Shape the right technical system.
Bring the data, workflow, and decision. We will help define the architecture worth owning.