Fixed-scope engagements with clear deliverables, pricing, and measurable outcomes.
Most AI implementations underperform. We benchmark your models for accuracy, latency, and cost-per-inference, then deliver a remediation plan with before/after metrics. We fix broken pipelines, optimize prompts, reduce inference costs, and improve reliability.
Start an audit →Half-day and full-day training for engineering and leadership teams. Hands-on labs covering model compression (quantization, pruning, distillation), on-device deployment, and edge inference architecture. Your team leaves with working code.
Book a workshop →Chief AI Officer as a service, specialized in edge-first strategy. Deep integration with your leadership team. We guide model selection, architecture decisions, vendor evaluation, and build/buy analysis for AI infrastructure.
Discuss a retainer →We manage your edge model lifecycle: deployment, monitoring, retraining, and observability. Built on Cloudflare Workers, Pydantic AI, and Logfire. You ship features; we keep the models running.
Start managed ops →Embed edge AI engineers directly in your team. Our engineers specialize in Cloudflare Workers, Pydantic AI, FastAPI, TinyML, and model optimization. Full-time or part-time placements.
Request engineers →Productized model compression and edge deployment tooling. Automated quantization pipelines, inference cost benchmarking, and one-click deployment to Cloudflare Workers. Built from our consulting IP.
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