Praxis
Real-world deployments are chaos — half-stated requirements, tacit knowledge, exceptions. Praxis turns that mess into a sequence of sprints you can actually ship.
The problem.
The hardest part of deploying AI into a real site isn't the model — it's the context. Requirements live in people's heads, conditions change room to room, and every site has exceptions no spec captured.
Turning that into something a team can deploy, in safe increments, is where most real-world AI projects stall.
The approach.
Praxis models the spec and the conditions, then sequences the work into deployable sprints.
- Captures messy, tacit project context as explicit specs and conditions
- Sequences it into deployable, verifiable sprints
- Keeps each increment scoped and shippable
- Carries the context forward so nothing gets lost between sprints
It's the delivery engine behind the firewall's real-world data trust sprints — how a site goes from "we can't let AI in" to a redaction, licensing, and provenance pipeline that actually ships.
Where it stands.
Praxis runs as the studio's internal delivery engine — the method PlexMesh uses to take a firewall pilot from chaos to a shippable sprint plan.