Make AI monitoring a service you bill for.
You already keep an eye on the AI models your clients depend on — the LLMs behind their chat and agents, plus the image, audio, and video models they call from OpenAI, Anthropic, Google, and others. AI Stack Watch turns that quiet, unbilled work into a monitored workspace, an approved baseline, and a white-label brief you can forward as your own deliverable.
A monitored workspace
One per client — every alert tells you whose stack it touches.
An approved baseline
The standard each candidate model is held to before you switch.
How baselines work →A white-label brief
Source-cited and client-ready — forward it as your own deliverable.
View sample client deliverables →The AI models your product runs on — and the companies behind them.
Modern software runs on AI models served by outside companies: the large language models (LLMs) behind chat and agents, the embedding models behind RAG and search, and the image, video, audio, and speech models behind everything else. Enginerds tracks those models — and the providers that ship them, including OpenAI, Anthropic, Google, Meta, Mistral, xAI, Cohere, AWS, and Azure — so a change on their side never blindsides you.
What it isn't: not uptime monitoring, server metrics, or analytics for your own app. It's intelligence on the model providers you build on — the part of your stack you don't control and can't freeze. When they change pricing, capabilities, or retire a model, your product can break or your bill can jump. We catch it first.
Monitoring is real work — and it's invisible to the client.
If you run AI in production for other people, you're already tracking provider changelogs, pricing pages, and deprecation notices in the background. It's genuine, skilled work — but because nothing breaks when you do it well, the client never sees it, and it never makes it onto an invoice.
The first time it does become visible is usually the worst time: a model hits end-of-life, a bill jumps, or an API behavior shifts — and you're explaining it after the fact.
Turn the watch into a deliverable.
AI Stack Watch gives that hidden work a shape your client can see: a per-client workspace, an approved synthetic baseline, and a source-cited brief on your cadence. Monitoring stops being a fire drill and becomes a proactive, billable touchpoint — you bring clients the heads-up before the provider does.
Three steps, then it runs in the background.
Look ahead of the curve — on the record.
- Defend the retainer. A recurring, professional brief makes ongoing value visible, even in a quiet month.
- Stay proactive. You raise model and pricing changes before the client notices them — or asks why the bill moved.
- Earn trust with sources. Every change links to the provider's own page and the exact wording that triggered it. Nothing paraphrased.
- Send it as your own. White-label the brief under your brand; the "Powered by Enginerds" line is configurable off on the Agency tier.
Per-client by design.
A workspace per client
Every alert tells you exactly whose stack it touches — scoped to what each pipeline actually depends on, not a firehose of the whole market.
Decision-support, not decisions
We tell you what changed and what to evaluate against the approved baseline. The call — and the client relationship — stays yours.
No proprietary client data required
Validation baselines are synthetic and editable. We don't need your client's real prompts, private records, files, or API keys — AI Stack Watch monitors the providers' own public pages and evaluates changes against the approved synthetic baseline for the workspace. The result is decision-support, not production certification. Cancel anytime, self-serve.
Every model in a client's stack is watched for the changes that move the needle — pricing, capabilities, and end-of-life. For the models behind chat, agents, and search (large language and embedding models), you also get the deeper layer worth billing for: a before-you-switch comparison against the client's approved baseline, plus alerts when a model's quality quietly drifts. Image, video, and voice models are fully tracked for pricing and capability changes. That said, hands-on behavioral testing doesn't apply to generated media — a picture or a voice clip can't be graded the way structured text can. See how Validation & Baselines works →
Give every client stack a watch worth billing for.
Start with one client workspace, approve a baseline, and let the briefs do the talking.
Questions about plans? Read the pricing FAQ.