Local LLMs, inference servers, model gateways, private deployments, quantization, and cost-controlled AI infrastructure.
For regulated industries, defense-adjacent work, and any organization that treats data as a liability. We focus on inference servers, GPU economics, and the trade-offs between local control and capability.
Evaluate and design private or self-hosted AI infrastructure where data sensitivity, latency, cost, and operational control matter.
Recurring contexts where this track typically becomes useful.
Failure modes and constraints this track is built to surface and address.
Generic deliverables a client could receive from this track. These describe the form of the work, not past engagements.
One human specialist owns this track end-to-end and validates every client-facing recommendation.
Each track can be supported by focused AI-agent roles for research, comparison, evaluation, risk review, documentation, and implementation planning. The detailed AI-agent organization is shown on the track lead profile.
These tools are watched, tested, validated, or rejected within this track. Inclusion means active evaluation, not endorsement.
Inference server for self-hosted LLMs with throughput-oriented batching and an OpenAI-compatible API surface.
"Validated for self-hosted inference serving where throughput, batching, and operational control matter more than desktop simplicity."
Local model runtime aimed at developer ergonomics on workstation-class hardware.
"Useful for prototyping and local development. We do not treat it as a production serving layer."
Business problems where this track may provide tool intelligence, architecture options, or implementation patterns.
Engineering teams need a single internal entry point to local and managed AI models, with auth, quotas, and per-tenant rate limits — without each application reinventing the integration.
This track starts with one human owner and focused AI-agent support. Over time, mature tracks can grow into larger specialist units with additional contributors, playbooks, implementation patterns, and client delivery capacity.
