AWS Rekognition

· پراستفاده‌ترین #294

See every face, object, and word — no vision code required.

تحلیل دادهDeveloperAISecurityاتوماسیون

AWS Rekognition is Amazon's cloud-native computer vision service that lets agents analyze images and videos for faces, objects, text, celebrities, PPE, and explicit content — using pre-trained models that are accurate from the first call. Connect it to Actionist and your agents can verify identities, moderate user-generated content, extract text from receipts, flag safety violations on camera footage, and train custom image classifiers — all triggered by real business events, not manual spot-checks.

میانگین زمان صرفه‌جویی‌شده
11 ساعت
برای هر نفر · در هر ماه
تقریبا 1 روز کاری برگشتی

کار دستی را حذف می‌کند. Rekognition automation replaces the frame-by-frame image inspection and footage review that HR, operations, and trust-and-safety teams do by hand every week.

زمان‌بندی

عامل AWS Rekognition شما چه چیزهایی را خودکار اجرا می‌کند

یک هفته کارهای زمان‌بندی‌شده که عامل Actionist از طرف شما اجرا می‌کند.

28کارهای زمان‌بندی‌شده
7عامل‌های فعال
24/7همیشه روشن
عامل‌ها
چهارشنبهجمعه
چهارشنبه
پنجشنبه
جمعه
7a
8a
9a
10a
11a
12p
1p
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گردش‌کارهای چنداپلیکیشنی

AWS Rekognition × همه اپلیکیشن‌های دیگر شما

اتوماسیون‌های سرتاسری که چند اپلیکیشن را به هم وصل می‌کنند؛ هرکدام یک خروجی واقعی کسب‌وکار.

6گردش‌کارها
9اپلیکیشن‌های درگیر
حدود 103 ساعتصرفه‌جویی در هفته
6نقش‌های پوشش‌داده‌شده
برای موفقیت مشتری
ویژه4 اپلیکیشن

Auto-moderate user photos at upload

When a support ticket arrives with an attached user photo, the agent scans it for explicit or violent content with Rekognition, routes clean images to the resolution queue, and flags violations to the trust-and-safety team via Slack — with bounding-box evidence attached. Reviewers never open a raw ticket blind again; moderation latency drops from hours to under 30 seconds per image at any volume.

حدود 60 ساعت

زمانی که تیم شما هر هفته و به‌صورت خودکار پس می‌گیرد

جریان کار
تریگر·When a Gmail support ticket with an image attachment is received
نتیجه
Compare face against known-bad actor collectionPost moderation verdict with flagged labels to trust-and-safety channelBook moderation review slot if human escalation required
برد اصلی
صرفه‌جویی در هر اجرا
45 دقیقه
اجرا در هفته
~80×
Zero unreviewed policy violations
اجرا توسطCustomer Support Agent
بازگشت سرمایه

صرفه‌جویی

چیزی که تیم شما پس می‌گیرد: کارهای دستی‌ای که حذف می‌شوند و ارزشی که ایجاد می‌شود.

بدون Actionist

کاری که امروز دستی انجام می‌دهید

با Actionist

کاری که عامل شما برایتان اجرا می‌کند

  • Sales
    19 دقیقه در هفته
    Identity verification calls

    Sales ops manually checks inbound demo-request photos against CRM records to prevent impersonated bookings — 19 minutes of cross-referencing per flagged request.

    عامل Sales
    ۰ دقیقه
    Agent compares faces in under 5 seconds

    The agent runs CompareFaces against the CRM contact photo and logs the similarity score — only confirmed identities get a calendar invite.

  • Marketing
    14 دقیقه در هفته
    Influencer content brand-safety review

    Marketers open each submitted post, inspect for explicit content and unlicensed celebrity appearances, and manually log verdicts — 14 minutes per batch review cycle.

    عامل Marketing
    ۰ دقیقه
    Agent scans and verdicts every submission

    The agent runs moderation and celebrity detection on each image and writes pass/fail to the campaign tracker before a human ever opens the file.

  • Customer Support
    19 دقیقه در هفته
    User photo moderation triage

    Support agents open flagged tickets, inspect attached images for policy violations, and manually route them to trust-and-safety — 19 minutes of queue management per shift.

    عامل Customer Support
    ۰ دقیقه
    Agent detects and routes violations instantly

    The agent scans every uploaded image at intake and routes violations with labelled evidence to the right queue — no ticket sits unsorted.

  • Human Resources
    8 دقیقه در هفته
    New-hire ID verification checks

    HR coordinators manually compare submitted headshots with government ID scans during onboarding — 8 minutes per new employee including logging.

    عامل Human Resources
    ۰ دقیقه
    Agent confirms face match at intake

    The agent runs CompareFaces on selfie vs. ID scan during the onboarding form submission and flags mismatches before the case reaches an HR reviewer.

  • Finance
    14 دقیقه در هفته
    Receipt data entry from photos

    Finance staff open each emailed receipt image, read the vendor and total by eye, and key the data into the expense system — 14 minutes per batch of 10 receipts.

    عامل Finance
    ۰ دقیقه
    Agent extracts receipt fields via text detection

    The agent runs DetectText on every receipt image and creates a pre-filled expense draft — staff click approve, not type.

  • Operations
    30 دقیقه در هفته
    PPE compliance photo inspection

    Shift supervisors manually review shift-start photos frame by frame for hard hat, face mask, and glove compliance across five sites — 30 minutes of manual checking per week.

    عامل Operations
    ۰ دقیقه
    Agent runs PPE detection on every shift photo

    The agent checks each worker in the image for required protective equipment and flags violations with bounding-box coordinates before the line starts.

  • Legal
    6 دقیقه در هفته
    Biometric data inventory tracking

    Legal and compliance teams manually track which Rekognition collections are active, when they were created, and whether retention limits have been breached — 6 minutes of audit work weekly.

    عامل Legal
    ۰ دقیقه
    Agent audits collections against retention policy

    The agent lists all active collections weekly, cross-references creation dates against your retention schedule, and flags overdue collections for deletion approval.

+ صدها اتوماسیون دیگر AWS Rekognition
میانگین ماهانه
11 ساعت / نفر / ماه
میانگین ماهانه
11 ساعت / نفر / ماه
محاسبه‌گر

محاسبه کنید تیم شما چه چیزی ذخیره می‌کند

اندازه تیم
10 نفر
نرخ ساعتی
20 دلار / ساعت
ساعت ذخیره‌شده / هفته
28
ساعت ذخیره‌شده / سال
1,400
بازگشت سالانه
$28,000

بر اساس الگوی رایج استفاده تیمی از AWS Rekognition: کارهای قابل مشاهده به‌علاوه چند اتوماسیون دیگر که عامل اجرا می‌کند: حدود2.8 ساعت / نفر / هفته کار اداری خودکار می‌شود.

اتصال

چطور AWS Rekognition را به Actionist وصل کنید

روش اتصالی را انتخاب کنید که با محیط کاری شما سازگار است.

The fastest path to Rekognition. Actionist installs the AWS MCP server and authenticates via your IAM credentials in one guided flow — no token rotation, no SDK boilerplate, your agent is calling DetectFaces in minutes.

1
Open the Apps tab

Find AWS Rekognition in the Apps library and click Connect. MCP is selected by default.

2
Provide your AWS credentials

Enter your AWS Access Key ID and Secret Access Key (or choose IAM Role if running in AWS). Actionist scopes the session to the Rekognition, S3, and SNS permissions listed in the setup guide.

3
Test the connection

Actionist runs a read-only ListCollections call to verify the handshake. You're ready.

اکشن‌ها

17 اکشن که عامل شما می‌تواند اجرا کند

عملیات خواندن و نوشتنی که برای عامل Actionist شما در دسترس است.

تریگرها

6 رویداد که عامل شما می‌تواند به آن واکنش نشان دهد

رویدادهایی که عامل شما زیر نظر می‌گیرد و در پاسخ به آن‌ها اکشن اجرا می‌کند.

مهارت‌ها

مهارت‌هایی که با AWS Rekognition خوب کار می‌کنند

مهارت‌های قابل استفاده مجدد عامل که کنار این اپلیکیشن مفید هستند.

هنوز مهارت جفت‌شده‌ای آماده نشده است. این اپلیکیشن را به عامل خود اضافه کنید تا گزینه‌های مناسب را کشف کنید.
سرورهای MCP

سرورهای MCP سازگار با AWS Rekognition

Actionist را به سرورهای MCP ساخته‌شده برای این اپلیکیشن یا پیرامون آن وصل کنید.

هنوز سرور MCP برای این اپلیکیشن فهرست نشده است.
پرسش‌ها

پرسش‌ها درباره AWS Rekognition + Actionist

How do I connect Actionist to AWS Rekognition?
Open the Apps tab, find AWS Rekognition, and click Connect. Choose MCP for the fastest path — Actionist guides you through entering your AWS Access Key ID, Secret Access Key, and region. Actionist then runs a ListCollections call to confirm the credentials work. Prefer IAM roles over long-lived access keys; use a dedicated service user with the minimum policy (rekognition:*, s3:GetObject for your target buckets) rather than an admin key.
What IAM permissions does Actionist need for Rekognition?
Your IAM policy needs rekognition:* for the actions you use (DetectFaces, SearchFacesByImage, etc.), s3:GetObject scoped to the specific buckets containing your images or videos, and sns:Publish if you use asynchronous video jobs that notify via SNS. Avoid using AdministratorAccess or AmazonRekognitionFullAccess with broad S3 — scope it to the least privilege your workflows actually require, then expand if the agent reports AccessDenied on a specific call.
Can I use Rekognition together with Gmail, Slack, and HubSpot in one workflow?
Yes — Rekognition is a read/write API step, not a trigger, so it slots into any multi-app workflow. A common pattern: a Gmail trigger fires when a support ticket with an attachment arrives; the agent calls DetectModerationLabels on the image; if a flag is returned, a Slack message is posted to the trust-and-safety channel and a HubSpot ticket is created for the case. Every step is connected through Actionist's agent layer — no custom code or webhook plumbing required.
What types of images and videos does Rekognition accept?
For images, Rekognition accepts JPEG and PNG files up to 5 MB delivered inline in the API call, or larger files referenced by S3 object key — with no hard size limit when reading from S3. For asynchronous video analysis (StartFaceDetection, StartLabelDetection), the video must be stored in S3 and can be MP4, MOV, or AVI up to 10 GB and 6 hours long. Minimum face size for reliable detection is 40x40 pixels; images under 80 pixels on the shortest side produce lower-confidence results.
How do I avoid runaway API costs when agents process large batches?
Set a concurrency cap on your Actionist workflow so the agent doesn't hammer the API in tight loops — Rekognition charges per image analyzed, so 1,000 unthrottled calls in a minute will appear on your bill. Use S3 event triggers or SQS queues with a visibility-timeout so each image is processed exactly once. For video jobs, prefer asynchronous StartFaceDetection over frame-by-frame image calls — a 60-minute video costs the same whether you submit it as a single job or 108,000 individual frames, but the job path is 100x cheaper in agent time.
How does face liveness prevent replay and photo spoofing attacks?
Rekognition Face Liveness challenges the user with a randomised light-reflection sequence the camera captures in real time — a printed photo or looped video cannot reflect light the way a live face does. The session returns a confidence score (0–100) alongside a SUCCEEDED or FAILED status. Set your threshold at 90 or above for high-assurance flows like KYC or account recovery; lower thresholds suit lower-risk gates like newsletter sign-up confirmations. The audit reference ID returned with each session is suitable for a compliance log.
Can I disconnect AWS Rekognition and what happens to my face collections?
Disconnecting Rekognition in Actionist removes the credential binding — it does not delete your AWS resources. Your face collections, indexed face vectors, and Custom Labels projects remain in your AWS account at rest. If you need biometric data fully removed (e.g. for GDPR compliance), use the agent's Delete collection action or call DeleteFaces on individual face IDs before disconnecting. Actionist never stores Rekognition's biometric payloads on its own servers — all face data stays in your AWS account.
How do I train and deploy a Custom Labels model through the agent?
Use the Create project action to provision a Custom Labels project, then upload labelled images to the associated S3 bucket. Start the training job from the Rekognition Custom Labels console or via the AWS CLI — when the job completes, the Custom labels project version trained trigger fires in Actionist. Your agent can then evaluate the returned metrics, and if F1 exceeds your threshold, call Start project version to bring the model online for real-time inference. The whole promotion decision — train, evaluate, deploy — runs without manual console visits.