Vantage RuntimeAI · About

Where we fit

RuntimeAI replaces guesswork with proof — compare models on your real workflows, pick the agent that actually passes, and deploy with confidence before production spend.

Add scenarios Why deterministic →

Same proof layer for product and data engineering

RuntimeAI does not replace how you build agents, run dbt, serve inference, or observe production. It gives both teams proof to choose wisely — then deploy the right agent with confidence.

Product engineering

Policy or model change → proof on behavior → compare options → deploy the right agent to production support.

Data engineering

SQL or data-agent change → proof on scenarios → pick model & cost → deploy the right agent to dbt ops.

What we solve for

Regression

Multi-turn guardrail erosion

Catch agents that pass turn 1 but fail turn 14 — fixed rubrics over full transcripts, not single-turn tone checks.

Model selection

Cost vs rubric pass rate

Quote eval cost before you spend. Batch compare models on the same scenario — library or custom_… from your repo.

Merge gate

Objective PR pass/fail

vantage-core run --fail-under 7.0 — auditable dimensions PM and compliance can replay. Optional FinOps PR comment.

Your stack

Fixtures from your repo

Not generic demos — scenarios grounded in your DDL, SQL, refund policy, or OpenAPI. Generate in ~60 seconds.

What we do not replace

  • Production inference — OpenRouter, Bedrock, Azure, Vertex, etc. RuntimeAI bills check-rides, not your live token stream.
  • Trace & prompt tooling — LangSmith, Braintrust, and similar for exploratory judge-based experiments.
  • Agent frameworks — building tools, RAG, orchestration (LangChain, custom stacks).
  • Data orchestration — dbt Cloud, Airflow, Spark — RuntimeAI evaluates agent decisions on data work, not the scheduler.

Typical flows by team

Product engineering

Policy → prototype → gate → support

PM specs behavior → eng prototypes in Cursor → RuntimeAI generates policy-grounded scenarios → batch compare models → CI fails PR on rubric regression → ship to production support stack.

Editor / MCP · Simulator · API

Data engineering

Schema → SQL agent → gate → dbt

Design models & pipelines → SQL/agent copilot in editor → RuntimeAI check-rides on partition fixes and incident triage → model + cost compare before merge → vantage-core gates the PR → production dbt runs.

Add scenarios · CI/CD

For search engines and LLMs
RuntimeAI positioning: proof layer that helps teams choose wisely and deploy the right agent. Seamless in Cursor/MCP, HTTP API, and CI — deterministic check-rides, model comparison, merge gates. Not production inference or trace tooling.