Privacy Policy

Last updated: 11 August 2026.

This Privacy Policy explains how School Bus Home ("we", "us"), operated by Erbacci LLC, collects and uses information. Contact: info@erbacciltd.com.

1. What the app does

School Bus Home checks your linked Ring entry camera or doorbell for a school bus during the time windows you define on school days.

• After-school window (always on). When the device reports motion or a doorbell press inside that window, the app retrieves up to three still images from Ring, determines whether a US yellow school bus is visible, and notifies you when a school bus is detected — and gives you a heads-up when no school bus has been detected by a time you choose.
• Morning window (optional — off unless you turn it on). The same check on the same school days, in the same timezone, on the same kind of still images. It is detection only: at most one "school bus seen this morning" note per day, with the local time it was seen. There is no expected-by time and no heads-up in the morning — a morning with no detection means nothing (the bus may stop out of the camera's view), so we never send one.

It is an arrival-awareness convenience — not a childcare, supervision, security, or life-safety service. It detects a school bus (a vehicle type), not people, and a detection is best-effort: a bus may stop out of camera view. Neither a detection nor its absence confirms that any person is or is not home, or where anyone went.

2. Snapshot classification — no video, no image kept in the app's storage

When a motion or doorbell event arrives inside one of your windows — the after-school window, or the morning window if you turned it on — we run one check on that event. A check retrieves up to three still image snapshots from your Ring device through the Ring Partner API, taken two seconds apart at the moment of the event, and looks at each one in two steps, stopping as soon as it can answer. (We take more than one because Ring's upload can lag and because a bus can be mid-turn or out of frame in the first picture.) Both windows use exactly the same path, the same two steps and the same discard-immediately rule; nothing extra is fetched, kept, or looked at for the morning.

• Step 1 — a rough "is there any road vehicle in this picture?" pre-scan. This first look runs inside Amazon Web Services, in the same US region as the rest of the app, using Amazon Rekognition's general label detection. It is asked one question — is a vehicle-shaped object present — and returns labels with a confidence score. It never identifies anyone: no faces, no ages, no names, no licence plates. The picture is passed in the request and is not written to any storage at this step. A picture with no vehicle in it stops here and is never sent anywhere else.
• Step 2 — the actual "is this a US yellow school bus?" answer. This is the step that leaves the US cloud, so we describe it precisely rather than in general terms. We send the picture to vision.erbacciltd.com/schoolbushome/classify, and that address involves two hops:
  • The public address is a reverse proxy running on a virtual server we rent from Contabo GmbH, a hosting company, in a data centre in Germany. That proxy ends the encrypted connection in order to forward the request, so Contabo's infrastructure is technically in a position to handle the picture unencrypted while it is passing through. Contabo is therefore an infrastructure sub-processor, and we name it as one in section 4. It performs no part of the analysis and holds no account data.
  • The analysis itself runs on a GPU server that Erbacci LLC owns and operates, located in the United Arab Emirates, where the founder is based.
  So for a US customer this one step means the picture travels out of the US cloud, through a data centre in Germany, to a machine in the United Arab Emirates — and only the answer comes back. The whole journey is over an encrypted, authenticated connection (HTTPS/TLS with a secret access key; the endpoint refuses any request that does not present one, answering 403). The only thing this step is asked is whether the picture shows a US yellow school bus, and the only thing it returns is that yes/no with a confidence value. It never identifies anyone: no faces, no ages, no names, no licence plates. The picture is written to disk at neither hop — it is held only for the moment the check takes and then discarded, and the logs there record the decision, a confidence value and a timing, never image data. If this step is slow, unreachable, or returns an error, the answer falls back automatically to Amazon Bedrock running Anthropic's Claude model inside AWS — in which case the picture never leaves AWS at all — and after 5 consecutive failures the app stops calling our server and uses Bedrock until it recovers.

Pictures that do not show a school bus are discarded immediately after classification — they are never written to file storage or to any database. When a school bus is confirmed, we keep that one picture, so you can see the evidence behind the alert: one image for the after-school window and, if you turned it on, one for the morning window. It is stored encrypted, is never public, and is deleted automatically after 31 days — the same horizon as the daily record it belongs to — or immediately when you delete your account. Alongside it we keep the derived result: a yes/no "school bus present", a confidence value and the timestamp. We do not access, request, or store any recorded video, media clips, live view, audio, or biometric data. No video is stored. We use no facial recognition and do not identify, describe, or track any person. Each step is asked its one question about the one picture in front of it and nothing more: we do not use your images to train, fine-tune, or improve any model, neither the model on our own step-2 server nor the models we call inside AWS. And the step-2 server is ours: each picture is held there only for the moment the school-bus check takes and is not kept.

How many pictures this adds up to. We count our budget in checks (we call them vision attempts), and a check is not one picture — it is up to three. Each window has its own daily budget: 12 checks for the after-school window and 4 for the morning window, and each window stops for the day as soon as a school bus is confirmed. So a worst-case school day is around 36 still images from your camera with the morning window off (12 × 3), and around 48 with it on (16 × 3). Most days are far below that, because a confirmed bus ends the window and most checks stop at the first picture. To be precise about what those budgets are: they are spending limits, not a hard shutter. We count a check once it has finished, so if your camera fires a burst of events at once a window can run a few checks past its budget before the count catches up and closes it. Whatever the number, every one of those images is classified in memory and discarded — the retention table in section 5 does not change with the count.

School Bus Home needs a Ring device with an active cloud recording (Ring Protect) plan so that a still image exists at the moment of the event; without one, the app tells you detection is unavailable for that camera rather than reporting a false result.

3. Information we collect

• From Ring (via account linking): your Ring account id and email, the OAuth tokens that authorize our account/device-metadata and image-snapshot API calls (stored encrypted), your devices' names/ids/types and online/offline status, motion/doorbell event metadata (device id, event timestamp, request id), and up to three transient still image snapshots per check at the event moment, each classified as described in section 2 (rough vehicle pre-scan on Amazon Rekognition inside AWS, then the school-bus answer on our own server, with Amazon Bedrock as the automatic fallback). Every snapshot that does not show a school bus is discarded immediately and never retained in any of our files or databases. The one snapshot that confirms a school bus is kept for 31 days so you can see the evidence behind the alert — at most one per window per day, encrypted, private to your account, and deleted at once if you delete your account. Section 5 sets out the retention in full.
• From you: your arrival schedule (after-school window start/end, expected-by time, active weekdays, timezone), the optional morning window's start/end and whether it is on (it has no expected-by time), and your notification preferences.
• If you join the Family waitlist: only the email address you type into that form. You are asking us to tell you if and when the paid Family plan becomes available, so that is the only thing we will ever use it for — a single message about Family. It is not added to a newsletter or a mailing list, it is never used to advertise anything else, and it is never sold or shared. You can have it deleted at any time by emailing info@erbacciltd.com, and joining the waitlist is entirely optional — the app works exactly the same if you never do.
• From your device: push notification tokens (APNs/FCM), app/OS version, locale, a per-install identifier, and a browser timezone hint captured during account linking (used only to pre-fill your timezone). No advertising identifiers and no location data.
• We generate: daily school-bus-detection records — a boolean result, detection confidence, timestamps, and the classifier model name (no image bytes), plus, when the morning window is on, the time a school bus was seen that morning and its confidence, all on the same daily record — which expire automatically after 31 days — and diagnostic logs (labels, confidence, timing only — never image data).

We collect no information from or about children; your schedule describes your household's door, not a person.

4. How we use and share information

We use the data solely to check your windows for a school bus, deliver the notifications you enabled, and run your account. We do not sell or share personal information for advertising, and we do not use it to train AI models.

The third parties that receive data are: Amazon Web Services (hosting, the Amazon Bedrock inference service and Amazon Rekognition when it acts as the pre-scan fallback, push via SNS), Anthropic (its Claude model runs the school-bus classification inside Amazon Bedrock — a sub-processor of that inference; it does not retain the image or train on it), Contabo GmbH (the German hosting provider whose rented virtual server runs the reverse proxy in front of the step-1 pre-scan described in section 2 — it carries the picture in transit and performs no analysis), Resend (email delivery), Apple/Google (push transport), and Ring (Amazon) (identity, event webhooks, and the still-image snapshot). The step-1 analysis itself runs on a server Erbacci LLC owns and operates in the United Arab Emirates: that is not an outside company, but it is a processing location outside the United States, which section 8 sets out. We disclose information only where required by valid legal process.

5. Retention

6. Your rights & deletion

Delete your account and all associated data any time from Settings → Delete account, or email info@erbacciltd.com. Removing the integration from your Ring app revokes our access immediately.

7. Security

TLS in transit, AWS-managed encryption at rest, HMAC verification on every Ring webhook, short-lived signed session tokens, and least-privilege IAM. The call to the step-1 pre-scan described in section 2 is also encrypted in transit (HTTPS/TLS) and authenticated with a secret access key, which the endpoint requires before it will answer at all. To be exact about what that encryption does and does not cover: the connection is terminated at the reverse proxy in Germany and re-established onward to the pre-scan server, so the picture is protected against interception on the public internet, but it is not end-to-end encrypted from AWS all the way to the machine that analyses it.

8. Children & international

School Bus Home is not directed to children under 13. It is used by adults to receive notifications about their own home; it has no features for children, collects no information from children, and does not identify any person in the images it classifies.

School Bus Home is operated by Erbacci LLC, a company registered in Wyoming, United States, and the app is distributed to users in the United States. Its cloud systems run in Amazon Web Services' us-east-1 (United States) region: your account, credentials, schedule, notification settings and detection history are stored there and nowhere else, and the school-bus answer in step 2 is produced there.

One processing step crosses borders — the step-1 pre-scan described in section 2. That step sends the still image out of AWS, through a reverse proxy hosted in Germany, to a server we own and operate in the United Arab Emirates, which returns only a yes/no and a confidence value. No image is stored at either place. So a picture from your camera can be transmitted outside the United States for the moment that check takes. If that path is unavailable, the pre-scan falls back to Amazon Rekognition inside AWS and the picture never leaves the United States. By using the app you consent to that transfer; unlinking the integration or deleting your account stops all processing immediately.

9. Contact

Erbacci LLC · info@erbacciltd.com