Turn "should we switch?" into an evidence-based answer.
When a model is repriced, deprecated, or out-shipped by a newer one, the real question is whether a replacement is actually safe to run in a workspace you manage. AI Stack Watch answers it with a validation baseline — the standard you set once, as the expert, and hold every future candidate to. No proprietary client data required.
The standard behind every decision brief
A validation baseline is your definition of what "good" looks like for one workspace — the must-pass expectations a model has to meet to run that pipeline. You set it once, as the expert who owns that stack; from then on it's the fixed bar.
When a replacement candidate actually matters, a validation impact check measures the current model and the candidates against that same baseline, and reports how each did on your must-pass checks. It's what turns a decision brief into evidence instead of an opinion.
You set the standards. We generate and run the tests.
Setup is light and stays in plain language: turn on the checks that matter for a workspace and write a one-line standard for each. From there, AI Stack Watch generates the synthetic test cases and runs every current and candidate model against them — so the heavy lifting isn't yours, and no real client data is ever involved.
Synthetic, set once, the same bar every time
Synthetic — no client data
Baselines run on representative synthetic cases, so there's no real prompt, record, file, or API key to hand over. The confidentiality risk that would make this a hard sell simply isn't there.
You set the bar, as the expert
You define what "good" means up front — the standard you were hired to own. Locking it in once is what lets a later recommendation stand on evidence instead of a judgment call in the moment.
The same check, every time
Every future candidate is measured against the identical bar, so comparisons are consistent and repeatable — not a fresh, subjective look each time a model changes.
Before-you-switch confidence — and a heads-up when quality drifts
The baseline does two jobs for you:
- Before a switch. When a model reaches end-of-life or a stronger option appears, the impact check shows which candidates clear your bar — so the move is made on evidence, on your schedule, not on a provider's deadline.
- While nothing's changing. It's the reference point for catching quiet quality drift, so a model that slowly stops behaving the way it used to becomes a heads-up instead of a surprise.
Whether you walk a client through any of it is entirely your call — the baseline is your working standard as the expert they rely on. Sharing it is an option, never part of the setup.
What validation does and doesn't cover
Every model in a stack is watched for pricing, capability, and lifecycle changes. These hands-on validation checks apply to the models behind chat, agents, and search — large language and embedding models, whose output can be graded objectively. Image, video, and voice models are fully tracked for pricing, capability, and lifecycle changes. But we don't run behavioral tests on generated media — there's no objective way to grade a picture or a voice clip the way structured text can be graded. And the result is always decision-support: it surfaces which candidates fit your stated standards, never a verdict on which model is "best."
What setting up a baseline looks like
This is the whole setup for one workspace — turn on the checks that matter and state your standard for each, in plain language. No prompts to engineer, no test data to gather. Illustrative example: a patient-support assistant you run for a dental group.
Illustrative mock of the setup screen — fictional workspace, representative fields. Four plain-language standards is a typical baseline; you decide which apply.
What that setup gives you back
When a model is retiring or a candidate appears, AI Stack Watch runs each one against the baseline you set and shows you exactly where they stand — so the evidence you need is already organized the moment you open the brief. The call stays yours; we just make sure it's an informed one.
| Model | Output contract | Refusal & tone | Cost profile |
|---|---|---|---|
| Current model (reaching EOL) | Pass | Pass | Pass |
| Candidate A | Pass | Pass | Pass |
| Candidate B | Not run | Not run | Not run |
| Candidate C | Fail | Pass | Pass |
- Candidate A clears every standard you set — the clearest drop-in.
- Candidate B couldn't be reached in this run, so it's reported "Not run," not guessed — a paper-only option until that's resolved.
- Candidate C fails your output contract — ruled out, with the reason.
A "Not run" result is never reported as a pass. Coverage is always explicit, so the call you make is fully informed.
Illustrative result — representative Pass / Not-run / Fail marks, not real model output. Each check maps to a standard you set above.
No proprietary client data required
Baselines are synthetic and editable. AI Stack Watch never needs a client's real prompts, private records, files, or API keys. You can revise and re-approve a baseline as a workspace evolves — and the result is decision-support, not production certification.
Give every workspace a standard its AI is held to.
Set a few plain-language standards once, and let every future model change be judged against them — before you switch, not after.
Questions about plans? Read the pricing FAQ, or see how it all works.