Building intelligent systems that work in the real world.
MuFaw is an AI engineering studio turning research into production-ready systems. We build reliable, secure, and scalable AI architectures that teams can own and operate.
- Architecture blueprints and system boundaries
- Evaluation suites and regression gates
- Telemetry for cost, quality, and drift
- Runbooks and ownership handoff
Research to Production Loop
We translate research into operator-grade systems, then learn from real-world workloads.
How we work
A four-phase loop that keeps research grounded in production constraints and measurable outcomes.
Phase 01: Align
- Scope and constraints brief.
- Success metrics and risk register.
Phase 02: Build
- Minimum production slice with guardrails.
- Integration plan and reference implementation.
Phase 03: Validate
- Evaluation suite and regression gates.
- Latency, cost, and failure budgets.
Phase 04: Operate
- Telemetry dashboards and alerting.
- Runbooks and ownership handoff.
Operating principles
The operator-grade guardrails we follow on every engagement.
Reliability over demos
We ship only after failure modes, fallbacks, and runbooks are covered.
Measurable evaluations
Every release ships with evals, regression gates, and quality targets.
Safe-by-design routing
Routing respects policy, budget, and escalation paths.
Observable by default
Traces, metrics, costs, and alerts are part of the deliverable.
Ownership handoff
Runbooks, dashboards, and controls move to your operators.
Minimal complexity
Prefer the smallest system that meets reliability and scale goals.
Ready to ship something real?
Talk to MuFaw about production-grade AI systems and delivery.
