00 / INFRASTRUCTURE
Move AI systems from demo to production without losing the evidence, workflow, or deployment state: make retrieval traceable, execution explicit, and operations controllable.
Find the right system01 / Systems
Released
Your AI app found an answer. Can your team show why? PaveDB keeps the source, query record, result, replay, logs, and metrics together so retrieval can be audited and reproduced. Run it locally or as a managed service.
In plain words: An inspectable vector database
In internal production
Prompts, branches, tools, and channel-specific glue make an AI application hard to change safely. Flymmatik keeps deterministic steps, model decisions, conditions, and parallel work in one readable, versionable flow, then runs it on the web and in chat.
In plain words: Portable, declarative AI flows
In internal production
Once PaveDB and Flymmatik run across registered hosts, deployment state becomes another system to control. Patchgram applies placement, revision, and fleet policy through built-in drivers. PaveDB owns retrieval; Flymmatik owns flow language, staging, activation, and execution.
Pre-release
More coding agents create more output, not a delivery process. Hanzup gives planners, builders, reviewers, and fixers bounded roles, runs them in parallel, and separates writing a change from shipping it.
Live on Flowlexi Cloud
Start a dedicated PaveDB instance in minutes without giving up inspectability. Every search keeps its source, query record, and replay trail. You keep the keys, the archive, and the exit.
02 / Approach
Start by deciding what must be deterministic and where a model may interpret, generate, or judge. Flowlexi makes that boundary visible, so models act 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.
Read the plain-language guide03 / Go deeper
04 / Contact