01
Open-Core Utility
The local testing driver (vantage-core) is open-source, free, and runs entirely in your local workspace using your own environment variables. No gated walls for core developer utilities.
About
Why we are building Vantage
After a model or prompt change, eng has traces, finance has spend, and nobody shares one artifact: this path, this bar, this cost — ship or don’t. In-house fixtures can look fine at small scale. They are not enterprise governance when the agent surface grows — and they don’t reduce real exposure the way a continuous attested decision does.
Thesis
Classic software was deterministic: same input, same output. Unit tests and CI gates were built for that world.
Agents are not. A support bot can pass a one-turn staging check and still drop a compliance rule, dilute context, or burn tokens by turn fifteen in production.
We built Vantage so teams can prove the paths that matter before the miss reaches a customer — and re-prove them when the next change lands. Logging the failure in live traffic is too late.
You keep models, gateway, and traces. Partners author critical paths as local contracts. We return score, rough cost, and a CI exit — portable runtimeai.decision/v1, with deterministic rubrics — so eng, PM, and risk share one ship decision. We don’t ask you to rip out pytest or traces. We refuse the idea that those alone are good enough once more than a handful of agents matter to your clients. RuntimeAI is the free engine; paid Vantage protects suite history.
Same paths run in the browser during design, via API before a release review, and as exit-code gates in the CI you already merge through — from your schema, policy, and API context, not only a canned library. Continuous production monitoring stays complementary observability; our wedge is the ship decision.
See it
What the gate looks like
Score, rough cost, and an exit code — beside the stack you already trust for inspect and runtime.
Beside the platforms you already run
Keep Intercom. We’re not replacing the support stack. We sit next to the change you’re about to ship — same scenario, score + rough USD, ship / no-ship.
Same line for Salesforce, Zendesk, Genesys, and the rest. Those systems own the inbox and CRM. We own the ship decision on the agent path you customize. If the vendor AI is out-of-the-box and nobody owns agent behavior, Support isn’t our wedge — other agents at the same company often still are.
| Channel layer | What it owns | What RuntimeAI owns |
|---|---|---|
| Intercom / Fin | Messaging & default Fin | Ship/no-ship on customized support scenarios |
| Salesforce / Agentforce | CRM & Service Cloud runtime | Pass line + cost after owned path changes |
| Zendesk / Genesys / Freshdesk | Tickets & contact center | Authored multi-turn scenarios before you ship |
More detail: Where we fit · FAQ.
Built for Production Realities
Vantage was co-founded by Simon Brightman, a veteran product, data, and analytics executive with over 20 years of hands-on experience architecting SaaS data platforms, data engineering pipelines, and complex enterprise monetization strategies.
As an active, hands on product, analytics and data engineering leader who has spent decades managing real-world compute telemetry, latency budgets, and compounding API costs, Simon built Vantage not out of an abstract research lab—but to fix a glaring operational blind spot in the modern generative deployment cycle.
Talk to us
Questions about ship gates, design partners, or how RuntimeAI fits your stack — reach Simon and the team directly.
Core principles
02
Hard Telemetry Only
We eliminate subjective “vibe checks.” Every simulation run outputs deterministic engineering metrics — scored with auditable rubrics, not LLM-as-judge: Guardrail Erosion Velocity, path-dependent token bloat, and P95 latency.
03
Native Integration
Drop vantage-core into GitHub Actions or GitLab CI as a regression gate — the bundled example exits 1 when guardrail erosion exceeds threshold. Cadence follows the use-case family: analytical suites gate every pull request; conversational agents often use scheduled baseline reruns (same scenarios vs last known good) after deploy. Size that cadence on Cost Forecast — this is periodic proof, not always-on production streaming.