AI Startups · sprint
Automate shaped for AI Startups & ML Platforms: LLM-native engineering, real evals, and infra cost discipline. End-to-end workflow automation with LLM orchestration, monitoring, and runbook in 3 weeks.
service
Automate
industry
AI Startups
timeline
3 weeks
mode
sprint
specific fit
Most AI startups burn the same way: a brilliant prototype, a six-month effort to "make it production," and an OpenAI bill that grows faster than revenue. Sage Ideas builds AI-native systems the way they should be built — RAG pipelines with measurable evals, prompt versioning under source control, LLM cost tracking per request, and the boring infrastructure that turns a demo into a real product.
The single-user prototype hits 50 RPS and falls apart — rate limits, hot keys, streaming connections that pile up, and timeouts that take the whole worker pool down. We harden the request lifecycle: queues, backoff, circuit breakers, and the model-routing layer that keeps latency budgets intact.
Workflow design + architecture diagram (Excalidraw + written) End-to-end implementation (LLM orchestration, integrations, fallbacks) Monitoring + alerting (Sentry / CloudWatch / Slack) Operator-facing dashboard for visibility
This route does not pretend the engagement includes llm token spend (passed through at cost), long-term retainer (rolls into operate), multi-tenant or customer-facing automation (rolls into build).
route to revenue
Each page is unique to the service and industry pair, but the conversion path stays simple: qualify the problem, inspect proof, then book the right first move.
01
The buyer is looking for automate in a AI Startups context.
02
LLM orchestration in production: tool-use, function-calling, structured output, multi-step agents, and the streaming UX that does not fall apart on retries — across OpenAI, Anthropic, Gemini, and open-weight models.
03
Automate: 3 weeks, $3,500.
04
Move into checkout or booking depending on whether the offer is self-serve.