Pricing
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Ways to work with MuFaw
Choose the engagement model that fits your team—from rapid discovery to embedded infrastructure squads focused on orchestration, evaluation, and safety.
2-3 weeks · architecture + evaluation sprint
Features
Architecture review and model-flow blueprint tailored to your stack
Evaluation, safety, and reliability plan with guardrails
Pilot pipeline with instrumentation and telemetry hooks
Runbooks and tooling handoff for your engineers
Async support for 4 weeks and option to extend into an embedded pod
Recommended
Embedded Pod8-12 weeks · embedded model-flow pod
Features
Dedicated MuFaw engineers and researchers embedded with your team
Multi-model orchestration and tool/retrieval integration
Evaluation harness plus regression suite and triage loops
Reliability, SLA design, and observability dashboards
Security, safety, and compliance alignment with your standards
Weekly delivery reviews and roadmap alignment with your leads
Quarterly · platform + governance partnership
Features
Long-term roadmap co-design with product and platform leadership
Custom platform extensions and governance processes
On-prem or VPC deployment support with compliance alignment
Advanced safety, human-in-the-loop controls, and escalation paths
Performance tuning across models, data, and retrieval layers
Dedicated on-call for production incidents and postmortems
How we work
Quick answers about MuFaw’s model-flow systems, research rigor, and how we deliver in production.
What does MuFaw build?
We design and ship AI infrastructure: model-flow orchestration, retrieval pipelines, evaluation harnesses, and safety controls so multiple models and tools behave like one reliable system in production.
How do engagements start?
Most teams begin with a discovery sprint to map risks, design the flow, and build a pilot. From there, we embed a pod alongside your team to scale orchestration, observability, and governance.
Do you provide a product or services?
Both. We maintain internal platform components and research, then deploy and operate them inside your stack—cloud, on-prem, or VPC—through hands-on engineering.
How does MuFaw handle safety and evaluation?
Every flow ships with evaluators, regression suites, telemetry, and optional human-in-the-loop controls. We track drift, fallbacks, and safety flags the same way we track reliability, with explicit policies you can review.
Can you work with our existing models and tools?
Yes. We integrate the stack you already run—foundation models, retrieval layers, internal tools, and queues—and add orchestration, evaluation, and governance around it instead of forcing a full rewrite.
What’s the best way to engage?
Share your use case and reliability goals. We’ll propose a discovery sprint or embedded-pod scope, align on milestones, metrics, and deployment model. The first conversation is purely exploratory.