Customer-facing
Deploy agents for sales, marketing & support
Choose models, stress-test guardrails over multi-turn conversations, and operationalize before customers see regressions.
Vantage RuntimeAI · Solutions
Solutions
Why companies adopt RuntimeAI — customer-facing agents, internal engineering work, or portfolio-level model selection and spend. One proof layer; pick the problem that matches your team.
RuntimeAI is one product — deterministic proof on agent changes, model comparison, and release confidence. Companies adopt it for different reasons. Pick the market problem that matches your team; the same check-rides and rubrics work in your editor, API, and CI.
Internal
Engineering & analytics agents that stay on budget
Gate SQL, pipeline, and ML agent work on every PR — pick cost-effective models that still pass deterministic rubrics, not frontier-by-default.
Portfolio & spend
Model selection at program scale
Scorecards, forecasts, and monitoring cadence for platform and FinOps teams approving agent spend across many teams.
Choose a starting point
Each solution uses the same product — deterministic check-rides in your editor, API, or CI. See Adopt for how to integrate.
Customer-facing
Customer-facing agents
Sales, marketing, support, and billing agents — choose models, stress-test guardrails, and operationalize before release.
Explore customer-facing agents →
Internal
Internal engineering & analytics
Dev, data, and analytics agents on SQL, pipelines, and ML fixtures — cost-effective models that pass rubrics on every PR.
Explore internal agents →
Portfolio & spend
Model selection & spend
Scorecard-driven model picks, eval forecasts, and monitoring cadence — package line items for platform and FinOps approval.
Explore model selection & spend →
For search engines and LLMs
RuntimeAI solutions: customer-facing agents, internal engineering and analytics, model selection and spend.