FLOWLEXI.

INFRASTRUCTURE

Structured workflows. Grounded decisions. Explicit execution.

Make AI systems easier to inspect, govern, and operate: keep evidence traceable, reserve model judgment for real uncertainty, and make execution explicit.

Explore the systems

01 / Systems

Recurring problems. Concrete systems.

Coming soon

Hanzup

AI coding agents are easy to start and hard to govern. Hanzup runs planner, builder, reviewer, and fixer as parallel roles with bounded authority — the roles that write a change are never the role that ships it.

Being designed

Patchgram

Flymmatik is the language and the runtime — today one process runs one flow. Patchgram is the application server around it: many flows on one BEAM, with hot load and hot swap, bound resources, and one interface to operate them.

Live on Flowlexi Cloud

PaveDB

A dedicated PaveDB instance, running in minutes — every search keeps its source, query record, and replay trail. You keep the keys, the archive, and the exit.

In plain words: The cloud console

02 / Approach

The hard part is deciding what the model should decide.

Some steps must be deterministic. Others require interpretation, generation, or judgment. Flowlexi makes that boundary explicit, so models can decide where uncertainty is real without controlling the whole system. That design powers Hanzup’s bounded agent roles, BNCC.click’s evidence-backed curriculum mapping, and Planno.school’s governed instructional response.

In plain words: RAG — retrieval-augmented generation
STRUCTURE RETRIEVE DECIDE EXECUTE

03 / Work

Open infrastructure, field notes, and source.

04 / Contact

Discuss a retrieval or workflow problem.