For engineering leaders and AI infrastructure owners running Claude and other LLMs in production. Architecture, cost, multi-agent systems, and the reliability layer underneath.
We work the layer between the model and your users: the orchestration, the cost shape, the failure modes, the audit trail. Engagements are scoped to a surface, not sold as a deck.
Architecture review for teams running LLMs in production. We read the system end to end: prompt structure, tool surface, memory, caching, routing, observability. We tell you what's load-bearing, what's a liability, and what to change first.
Outcome A written read on the system and a ranked list of changes worth shipping this quarter.
Fractional or project-scoped engineering on Claude, GPT, and open-weight stacks. Multi-agent orchestration, tool-use design, MCP integrations, retrieval pipelines, the production glue around model calls. We ship into your repo, on your branching model.
Outcome Working code in your repository, with the design notes that explain why it's shaped that way.
Token and dollar audit of a production AI workload. Prompt caching coverage, model routing by task class, conversation-shape rewrites, context hygiene. We measure before and after on your own traffic, not a synthetic bench.
Outcome A measured reduction in monthly spend, documented per change so your team can extend the playbook.
Hardening pass on agent swarms and orchestrator setups. Boundary check on decomposition, fan-out limits, cross-talk between specialists, retry behavior, idle and stuck-state detection, escalation paths. We flag the failure modes that don't show up until you scale the fleet.
Outcome A specific list of structural fixes, sequenced by blast radius, with the reasoning behind each one.
Observability, failure-mode mapping, rollout patterns, regression gating. The unglamorous layer that separates a demo from a system you can leave running overnight. We bring the patterns we use in our own production stack.
Outcome Dashboards, gates, and runbooks your on-call rotation can actually use.
Marathon Variety ships AI systems for its own operations and for clients. The advisory work is grounded in code that runs every day.
Production local-first AI operating environment. Multi-agent crew, persistent state, tool routing across MCP servers, running daily.
Achieve frontier AI performance at home in your local CLI: Maestro fuses the model CLIs you already run (Opus 4.8, GPT-5.5, Gemini 3.1 Pro) into one grounded answer, on a verification-first discipline layer. Inside FounderOS it plans, dispatches, and reviews work across specialist agents.
Governance proxy and policy layer for AI traffic, designed for teams that need an audit trail on what models did and why.
Recent published work: a measured 37.5% agent cost reduction playbook for teams running Claude at scale, available on Gumroad . Eighteen months of agents running in our own production stack inform the engagements above.
Weekly call plus async review. We read your architecture, your costs, and your next two sprints. We answer the hard questions before you spend a quarter answering them yourselves.
Months-long engagement inside your team. We sit on standups, own a slice of the system, and hand it back with the documentation and the patterns to keep extending it.
Project-scoped delivery. A defined system or refactor with a fixed window and a written acceptance bar. We ship, transfer, and step out.