Praxis OS gives engineering orgs governed, auditable, cost-controlled AI pipelines, with policy gates, LLM routing, evidence trails, and financial guardrails built in.
Our entire product pipeline (every spec, security gate, approval, and release) runs through Praxis OS itself. The governance layer in this demo is the same one shipping our own code to production.
Standing up a governed, audit-ready SDLC by hand is normally weeks of platform work. On Praxis OS we had it running in hours, not months, roughly $30-40K of engineering effort we never had to spend.
Praxis OS doesn't bolt AI on top of your SDLC. It embeds policy, audit, and approvals into every AI action from day one.
name: ssdlc-feature version: 2 policy: require_evidence: true audit_trail: immutable cost_budget: $2.50/job stages: - id: spec agent: spec-writer model: auto # routed by LLM router - id: security-gate type: gate policy: security-standard-v2 on_fail: block - id: approval type: human-approval approvers: - eng-lead - security-owner timeout: 24h
Real-time cost attribution per team, pipeline, and model, with hard budget gates that prevent runaway spend before it hits your bill.
| Model | Jobs | Spend | |
|---|---|---|---|
| claude-haiku-4-5 | 1,842 | $87.40 | |
| claude-sonnet-4-6 | 418 | $312.60 | |
| claude-opus-4-8 | 64 | $640.00 |
Define standards once as policy-as-code. PraxisEngine enforces them at every pipeline run: no tribal knowledge, no manual audits.
PraxisEngine's LLM router classifies every request by complexity, cost, and policy, then dispatches it to the optimal model automatically.
Praxis OS connects AI pipeline costs to engineering roadmaps, so finance and eng are looking at the same numbers.
Praxis OS deploys inside your boundary. Your policies. Your models. Your audit trail. No black-box AI, no runaway spend, no compliance guesswork.