AI infrastructure that ships and stays cheap.
LLM-native engineering, real evals, and infra cost discipline.
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.
Why Sage Ideas for AI Startups
What we solve
The specific operational challenges we've already debugged in the AI stack.
A demo that breaks at scale
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.
No evals — every prompt change is a coin flip
You change the system prompt, ship it, and find out a week later it broke a use case nobody tested. We build a golden dataset, an LLM-as-judge harness with calibration against human ratings, and CI that blocks merges when quality regresses on any segment.
OpenAI bill growing faster than revenue
No per-feature token attribution, no caching, no model routing, no cap on runaway agent loops. We instrument cost per request and per feature, push easy work to cheaper models, add semantic and exact-match caches, and surface the unit economics every PM should be staring at.
RAG quality flatlining at 60%
You have a vector DB, an embedding model, and a "good enough" retrieval step — but answers are wrong half the time. We diagnose with recall@k metrics, fix the chunking and hybrid retrieval, add a reranker, and prove the lift on a held-out eval set rather than vibes.
Recommended tiers
Productized engagements ordered by relevance to ai startups workloads.
Relevant work
AI Startups questions
What evals framework do you use?
We avoid frameworks-for-frameworks-sake. The minimum viable eval stack is: a golden dataset of 50–200 representative inputs and expected behaviors, a deterministic test runner (pytest or vitest works fine), and an LLM-as-judge prompt calibrated against human-rated samples to confirm it correlates. CI runs the suite on every prompt change, comparing pass rate and segment-level metrics against the previous prompt. For richer needs we use Braintrust, Langfuse, or Inspect — but only after the basics are in place.
How do you keep LLM costs under control?
Four levers. First, attribution — every request is tagged with feature, user, and model so cost per feature is queryable. Second, routing — cheap models (gpt-4o-mini, Haiku, Flash) handle classification and extraction, expensive models handle generation only when needed. Third, caching — exact-match caches for deterministic prompts, semantic caches for retrieval-heavy flows. Fourth, hard limits — per-user and per-feature token budgets enforced server-side, so a runaway agent loop cannot torch the bill before someone notices.
Which vector database should we use?
For most products under 10M vectors with predictable QPS, pgvector on Postgres is the right answer — one database, transactional consistency with your application data, no separate operational surface. Pinecone or Weaviate make sense when you need multi-tenant namespace isolation, high QPS with sub-50ms latency, or features like hybrid search out of the box. Turbopuffer is excellent when you have hundreds of millions of vectors and cost is dominant. We will not pick the trendy answer; we will pick what your access pattern justifies.
How do you version and test prompts?
Prompts live in source control as templated files (typically Markdown or TOML), not in a database. Every change ships as a PR, runs through the eval suite in CI, and is deployed behind a feature flag so it can be rolled back instantly. We version model + prompt as a unit because they co-evolve. For richer experimentation we plug in Braintrust or Langfuse, but the source-of-truth is always the repo.
Can you build agents — or do you think they are overhyped?
Both. Multi-step tool-using agents work for narrow, well-bounded tasks where the action space is small and reversible: data extraction, code review, scheduled research, narrow customer-support flows. They struggle when the action space is large, irreversible, or requires real judgment under ambiguity. We build agents with hard step limits, tool-call budgets, structured outputs, deterministic fallbacks, and full observability into every reasoning trace — because debugging an agent that "just stopped working" without traces is genuinely awful.
Bring your demo, your eval gap, or your runaway OpenAI bill — we will turn it into infrastructure.
Book a 30-minute discovery call. We'll talk through your AI stack and tell you directly which engagement — if any — is the right fit.
Book a Discovery Call