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We are aware of the recently disclosed PHP2Shell vulnerabilities (CVE-2026-109618 and CVE-2026-109619) and can confirm Filestack is not impacted by these vulnerabilities.

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Filestack AI Image Recognition API

Tag images and video, recognize objects and faces, read text and detect explicit content as each file uploads. Every call returns JSON your code can act on.

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Plans & Pricing
A close-up, top-down view of a tabby cat with green eyes looking up, showing its striped fur, pointed ears, and long white whiskers.

What Filestack AI Returns for Each File

A photo, a photo of a document and a video, with every response they returned.

One Photo Through Six Image Tasks

Tags, a safety verdict, a caption, the face position, emotion scores and a blurred copy, each from its own URL.

A woman with a backpack smiling on a city street.

  1. Image Tagging

    tags
    • adult99
    • backpack99
    • bag99
    • blazer55
    • body part56
    • clothing98
    • coat98
    • dimples99
    • face99
    • female99
    • hair63
    • happy99
    • head99
    • jacket55
    • neck56
    • person99
    • photography97
    • portrait97
    • smile99
    • woman99
    {
      "tags": {
        "auto": {
          "adult": 99,
          "backpack": 99,
          "bag": 99,
          "blazer": 55,
          "body part": 56,
          "clothing": 98,
          "coat": 98,
          "dimples": 99,
          "face": 99,
          "female": 99,
          "hair": 63,
          "happy": 99,
          "head": 99,
          "jacket": 55,
          "neck": 56,
          "person": 99,
          "photography": 97,
          "portrait": 97,
          "smile": 99,
          "woman": 99
        },
        "user": null
      }
    }

  2. Explicit Content Detection

    sfw

    truesafe for work

    { "sfw": true }

  3. Image Captioning

    caption

    a person walking down a city street

    { "caption": "a person walking down a city street" }

  4. Face Detection

    detect_faces
    id
    1
    x
    425
    y
    195
    width
    225
    height
    328
    [
      {
        "id": 1,
        "x": 425,
        "y": 195,
        "width": 225,
        "height": 328
      }
    ]

  5. Image Sentiment

    image_sentiment
    • Angry0.000715
    • Calm0.003707
    • Confused0.001719
    • Disgusted0.000614
    • Fear0.000331
    • Happy100
    • Sad0.000376
    • Surprised0.006236
    {
      "emotions": {
        "ANGRY": 0.0007152557373046875,
        "CALM": 0.003707408905029297,
        "CONFUSED": 0.0017186006298288703,
        "DISGUSTED": 0.0006139278411865234,
        "FEAR": 0.00033080577850341797,
        "HAPPY": 100,
        "SAD": 0.00037550926208496094,
        "SURPRISED": 0.006236135959625244
      }
    }

  6. Face Blurring

    blur_faces    Returns an image
    The same street photo with the woman's face blurred.

A Photo of a Document, Straightened and Read

doc_detection crops and flattens the card, and ocr reads the result in the same URL.

  1. File

    A printed guest feedback card photographed at an angle on a wooden table.

  2. doc_detection

    The feedback card cropped out of the photo and straightened into a flat scan.

  3. ocr9 lines

    Response
    textx, y
    1. GUEST FEEDBACK139, 74
    2. The delivery arrived two123, 175
    3. days late and the packaging116, 217
    4. was completely crushed.114, 265
    5. I have been a customer for108, 307
    6. years and this is the worst105, 353
    7. experience I have had.102, 399
    8. Very disappointed.98, 443
    9. Order 44812213, 626
    {
      "document": {
        "text_areas": [
          {
            "bounding_box": [
              { "x": 139, "y": 74 },
              { "x": 490, "y": 67 },
              { "x": 491, "y": 96 },
              { "x": 140, "y": 103 }
            ],
            "lines": [
              {
                "bounding_box": [
                  { "x": 139, "y": 74 },
                  { "x": 490, "y": 67 },
                  { "x": 491, "y": 96 },
                  { "x": 140, "y": 103 }
                ],
                "text": "GUEST FEEDBACK",
                "words": [
                  {
                    "bounding_box": [
                      { "x": 139, "y": 74 },
                      { "x": 266, "y": 71 },
                      { "x": 267, "y": 100 },
                      { "x": 140, "y": 103 }
                    ],
                    "text": "GUEST"
                  },
                  {
                    "bounding_box": [
                      { "x": 281, "y": 71 },
                      { "x": 490, "y": 67 },
                      { "x": 491, "y": 96 },
                      { "x": 282, "y": 100 }
                    ],
                    "text": "FEEDBACK"
                  }
                ]
              }
            ],
            "text": "GUEST FEEDBACK"
          },
          {
            "bounding_box": [
              { "x": 93, "y": 174 },
              { "x": 539, "y": 165 },
              { "x": 545, "y": 473 },
              { "x": 99, "y": 482 }
            ],
            "lines": [
              {
                "bounding_box": [
                  { "x": 123, "y": 175 },
                  { "x": 496, "y": 167 },
                  { "x": 497, "y": 197 },
                  { "x": 124, "y": 206 }
                ],
                "text": "The delivery arrived two",
                "words": [
                  {
                    "bounding_box": [
                      { "x": 123, "y": 175 },
                      { "x": 179, "y": 174 },
                      { "x": 180, "y": 205 },
                      { "x": 124, "y": 206 }
                    ],
                    "text": "The"
                  },
                  {
                    "bounding_box": [
                      { "x": 188, "y": 174 },
                      { "x": 311, "y": 171 },
                      { "x": 312, "y": 201 },
                      { "x": 189, "y": 204 }
                    ],
                    "text": "delivery"
                  },
                  {
                    "bounding_box": [
                      { "x": 320, "y": 171 },
                      { "x": 427, "y": 169 },
                      { "x": 428, "y": 199 },
                      { "x": 321, "y": 201 }
                    ],
                    "text": "arrived"
                  },
                  {
                    "bounding_box": [
                      { "x": 439, "y": 168 },
                      { "x": 496, "y": 167 },
                      { "x": 497, "y": 197 },
                      { "x": 440, "y": 198 }
                    ],
                    "text": "two"
                  }
                ]
              },
              {
                "bounding_box": [
                  { "x": 116, "y": 217 },
                  { "x": 540, "y": 211 },
                  { "x": 540, "y": 244 },
                  { "x": 116, "y": 250 }
                ],
                "text": "days late and the packaging",
                "words": [
                  {
                    "bounding_box": [
                      { "x": 116, "y": 217 },
                      { "x": 185, "y": 216 },
                      { "x": 185, "y": 249 },
                      { "x": 116, "y": 250 }
                    ],
                    "text": "days"
                  },
                  {
                    "bounding_box": [
                      { "x": 194, "y": 216 },
                      { "x": 249, "y": 215 },
                      { "x": 249, "y": 248 },
                      { "x": 194, "y": 249 }
                    ],
                    "text": "late"
                  },
                  {
                    "bounding_box": [
                      { "x": 259, "y": 215 },
                      { "x": 316, "y": 214 },
                      { "x": 316, "y": 247 },
                      { "x": 259, "y": 248 }
                    ],
                    "text": "and"
                  },
                  {
                    "bounding_box": [
                      { "x": 325, "y": 214 },
                      { "x": 375, "y": 213 },
                      { "x": 375, "y": 246 },
                      { "x": 325, "y": 247 }
                    ],
                    "text": "the"
                  },
                  {
                    "bounding_box": [
                      { "x": 384, "y": 213 },
                      { "x": 540, "y": 211 },
                      { "x": 540, "y": 244 },
                      { "x": 384, "y": 246 }
                    ],
                    "text": "packaging"
                  }
                ]
              },
              {
                "bounding_box": [
                  { "x": 114, "y": 265 },
                  { "x": 487, "y": 254 },
                  { "x": 488, "y": 284 },
                  { "x": 115, "y": 296 }
                ],
                "text": "was completely crushed.",
                "words": [
                  {
                    "bounding_box": [
                      { "x": 114, "y": 265 },
                      { "x": 170, "y": 263 },
                      { "x": 171, "y": 294 },
                      { "x": 115, "y": 296 }
                    ],
                    "text": "was"
                  },
                  {
                    "bounding_box": [
                      { "x": 180, "y": 263 },
                      { "x": 349, "y": 258 },
                      { "x": 350, "y": 289 },
                      { "x": 181, "y": 294 }
                    ],
                    "text": "completely"
                  },
                  {
                    "bounding_box": [
                      { "x": 356, "y": 257 },
                      { "x": 479, "y": 253 },
                      { "x": 480, "y": 284 },
                      { "x": 357, "y": 288 }
                    ],
                    "text": "crushed."
                  }
                ]
              },
              {
                "bounding_box": [
                  { "x": 108, "y": 307 },
                  { "x": 525, "y": 302 },
                  { "x": 525, "y": 330 },
                  { "x": 108, "y": 334 }
                ],
                "text": "I have been a customer for",
                "words": [
                  {
                    "bounding_box": [
                      { "x": 108, "y": 307 },
                      { "x": 124, "y": 307 },
                      { "x": 124, "y": 334 },
                      { "x": 108, "y": 334 }
                    ],
                    "text": "I"
                  },
                  {
                    "bounding_box": [
                      { "x": 129, "y": 306 },
                      { "x": 204, "y": 305 },
                      { "x": 204, "y": 333 },
                      { "x": 129, "y": 334 }
                    ],
                    "text": "have"
                  },
                  {
                    "bounding_box": [
                      { "x": 212, "y": 305 },
                      { "x": 286, "y": 304 },
                      { "x": 286, "y": 332 },
                      { "x": 212, "y": 333 }
                    ],
                    "text": "been"
                  },
                  {
                    "bounding_box": [
                      { "x": 297, "y": 304 },
                      { "x": 313, "y": 304 },
                      { "x": 313, "y": 331 },
                      { "x": 297, "y": 331 }
                    ],
                    "text": "a"
                  },
                  {
                    "bounding_box": [
                      { "x": 325, "y": 304 },
                      { "x": 468, "y": 302 },
                      { "x": 468, "y": 330 },
                      { "x": 325, "y": 332 }
                    ],
                    "text": "customer"
                  },
                  {
                    "bounding_box": [
                      { "x": 479, "y": 302 },
                      { "x": 525, "y": 302 },
                      { "x": 525, "y": 330 },
                      { "x": 479, "y": 330 }
                    ],
                    "text": "for"
                  }
                ]
              },
              {
                "bounding_box": [
                  { "x": 105, "y": 353 },
                  { "x": 514, "y": 343 },
                  { "x": 515, "y": 376 },
                  { "x": 106, "y": 386 }
                ],
                "text": "years and this is the worst",
                "words": [
                  {
                    "bounding_box": [
                      { "x": 105, "y": 353 },
                      { "x": 187, "y": 351 },
                      { "x": 188, "y": 384 },
                      { "x": 106, "y": 386 }
                    ],
                    "text": "years"
                  },
                  {
                    "bounding_box": [
                      { "x": 196, "y": 351 },
                      { "x": 253, "y": 350 },
                      { "x": 254, "y": 382 },
                      { "x": 197, "y": 383 }
                    ],
                    "text": "and"
                  },
                  {
                    "bounding_box": [
                      { "x": 263, "y": 349 },
                      { "x": 321, "y": 348 },
                      { "x": 322, "y": 381 },
                      { "x": 264, "y": 382 }
                    ],
                    "text": "this"
                  },
                  {
                    "bounding_box": [
                      { "x": 330, "y": 348 },
                      { "x": 356, "y": 347 },
                      { "x": 357, "y": 379 },
                      { "x": 331, "y": 380 }
                    ],
                    "text": "is"
                  },
                  {
                    "bounding_box": [
                      { "x": 365, "y": 347 },
                      { "x": 416, "y": 346 },
                      { "x": 417, "y": 378 },
                      { "x": 366, "y": 379 }
                    ],
                    "text": "the"
                  },
                  {
                    "bounding_box": [
                      { "x": 424, "y": 345 },
                      { "x": 514, "y": 343 },
                      { "x": 515, "y": 376 },
                      { "x": 425, "y": 378 }
                    ],
                    "text": "worst"
                  }
                ]
              },
              {
                "bounding_box": [
                  { "x": 102, "y": 399 },
                  { "x": 450, "y": 391 },
                  { "x": 451, "y": 423 },
                  { "x": 103, "y": 432 }
                ],
                "text": "experience I have had.",
                "words": [
                  {
                    "bounding_box": [
                      { "x": 102, "y": 399 },
                      { "x": 270, "y": 395 },
                      { "x": 271, "y": 428 },
                      { "x": 103, "y": 432 }
                    ],
                    "text": "experience"
                  },
                  {
                    "bounding_box": [
                      { "x": 280, "y": 395 },
                      { "x": 294, "y": 395 },
                      { "x": 295, "y": 427 },
                      { "x": 281, "y": 427 }
                    ],
                    "text": "I"
                  },
                  {
                    "bounding_box": [
                      { "x": 301, "y": 394 },
                      { "x": 375, "y": 392 },
                      { "x": 376, "y": 425 },
                      { "x": 302, "y": 427 }
                    ],
                    "text": "have"
                  },
                  {
                    "bounding_box": [
                      { "x": 383, "y": 393 },
                      { "x": 443, "y": 392 },
                      { "x": 444, "y": 424 },
                      { "x": 384, "y": 425 }
                    ],
                    "text": "had."
                  }
                ]
              },
              {
                "bounding_box": [
                  { "x": 98, "y": 443 },
                  { "x": 389, "y": 441 },
                  { "x": 389, "y": 476 },
                  { "x": 98, "y": 479 }
                ],
                "text": "Very disappointed.",
                "words": [
                  {
                    "bounding_box": [
                      { "x": 98, "y": 443 },
                      { "x": 169, "y": 442 },
                      { "x": 169, "y": 478 },
                      { "x": 98, "y": 479 }
                    ],
                    "text": "Very"
                  },
                  {
                    "bounding_box": [
                      { "x": 178, "y": 442 },
                      { "x": 382, "y": 440 },
                      { "x": 382, "y": 476 },
                      { "x": 178, "y": 478 }
                    ],
                    "text": "disappointed."
                  }
                ]
              }
            ],
            "text": "The delivery arrived two\ndays late and the packaging\nwas completely crushed.\nI have been a customer for\nyears and this is the worst\nexperience I have had.\nVery disappointed."
          },
          {
            "bounding_box": [
              { "x": 213, "y": 625 },
              { "x": 391, "y": 626 },
              { "x": 391, "y": 652 },
              { "x": 213, "y": 651 }
            ],
            "lines": [
              {
                "bounding_box": [
                  { "x": 213, "y": 626 },
                  { "x": 391, "y": 627 },
                  { "x": 391, "y": 653 },
                  { "x": 213, "y": 651 }
                ],
                "text": "Order 44812",
                "words": [
                  {
                    "bounding_box": [
                      { "x": 213, "y": 626 },
                      { "x": 295, "y": 627 },
                      { "x": 295, "y": 652 },
                      { "x": 213, "y": 651 }
                    ],
                    "text": "Order"
                  },
                  {
                    "bounding_box": [
                      { "x": 304, "y": 626 },
                      { "x": 391, "y": 627 },
                      { "x": 391, "y": 653 },
                      { "x": 304, "y": 652 }
                    ],
                    "text": "44812"
                  }
                ]
              }
            ],
            "text": "Order 44812"
          }
        ]
      },
      "page_height": 724,
      "page_width": 594,
      "text": "GUEST FEEDBACK\nThe delivery arrived two\ndays late and the packaging\nwas completely crushed.\nI have been a customer for\nyears and this is the worst\nexperience I have had.\nVery disappointed.\nOrder 44812",
      "text_area_percentage": 35.39911081347545
    }

https://cdn.filestackcontent.com/security=p:POLICY,s:SIGNATURE/doc_detection/ocr/HANDLE

A Video With Tags and Timestamps

A workflow returns tags for the whole clip and the time ranges each one appears in.

video_tags121 tags in 308 time ranges

Tags at 0.0 s
  1. animal90
  2. canine90
  3. mammal90
  4. person90
  5. dog89
  6. pet89
  7. walking dog82
  8. outdoors78
  9. nature76
  10. plant73

Confidence 60 and up

{
  "details": {
    "tags": [
      { "confidence": 51, "end_offset": 0, "name": "accessories", "start_offset": 0 },
      { "confidence": 90, "end_offset": 18000, "name": "animal", "start_offset": 0 },
      { "confidence": 52, "end_offset": 3833, "name": "autumn", "start_offset": 0 },
      { "confidence": 53, "end_offset": 7625, "name": "autumn", "start_offset": 4625 },
      { "confidence": 50, "end_offset": 9000, "name": "autumn", "start_offset": 9000 },
      { "confidence": 52, "end_offset": 18000, "name": "autumn", "start_offset": 9625 },
      { "confidence": 52, "end_offset": 1000, "name": "baseball", "start_offset": 0 },
      { "confidence": 51, "end_offset": 2208, "name": "baseball", "start_offset": 1833 },
      { "confidence": 50, "end_offset": 8208, "name": "baseball", "start_offset": 8208 },
      { "confidence": 50, "end_offset": 9416, "name": "baseball", "start_offset": 9000 },
      { "confidence": 50, "end_offset": 10625, "name": "baseball", "start_offset": 10625 },
      { "confidence": 51, "end_offset": 12625, "name": "baseball", "start_offset": 12208 },
      { "confidence": 51, "end_offset": 15416, "name": "baseball", "start_offset": 13625 },
      { "confidence": 52, "end_offset": 1000, "name": "baseball game", "start_offset": 0 },
      { "confidence": 51, "end_offset": 2208, "name": "baseball game", "start_offset": 1833 },
      { "confidence": 50, "end_offset": 9416, "name": "baseball game", "start_offset": 9208 },
      { "confidence": 51, "end_offset": 15416, "name": "baseball game", "start_offset": 13625 },
      { "confidence": 50, "end_offset": 0, "name": "bicycle", "start_offset": 0 },
      { "confidence": 50, "end_offset": 1416, "name": "bicycle", "start_offset": 833 },
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      { "confidence": 50, "end_offset": 18000, "name": "oak", "start_offset": 18000 },
      { "confidence": 50, "end_offset": 6833, "name": "performer", "start_offset": 6208 },
      { "confidence": 51, "end_offset": 9416, "name": "performer", "start_offset": 9000 },
      { "confidence": 50, "end_offset": 10625, "name": "performer", "start_offset": 10416 },
      { "confidence": 50, "end_offset": 6833, "name": "crawling", "start_offset": 6416 },
      { "confidence": 51, "end_offset": 8000, "name": "crawling", "start_offset": 7625 },
      { "confidence": 50, "end_offset": 6833, "name": "kissing", "start_offset": 6625 },
      { "confidence": 52, "end_offset": 7416, "name": "photography", "start_offset": 6625 },
      { "confidence": 50, "end_offset": 9833, "name": "photography", "start_offset": 9833 },
      { "confidence": 50, "end_offset": 14416, "name": "photography", "start_offset": 14416 },
      { "confidence": 50, "end_offset": 6833, "name": "romantic", "start_offset": 6625 },
      { "confidence": 51, "end_offset": 15416, "name": "romantic", "start_offset": 15000 },
      { "confidence": 50, "end_offset": 7208, "name": "lion", "start_offset": 7000 },
      { "confidence": 52, "end_offset": 8625, "name": "fallen person", "start_offset": 7416 },
      { "confidence": 51, "end_offset": 8208, "name": "pilates", "start_offset": 7833 },
      { "confidence": 52, "end_offset": 8208, "name": "pushing", "start_offset": 8000 },
      { "confidence": 50, "end_offset": 8000, "name": "stretch", "start_offset": 8000 },
      { "confidence": 50, "end_offset": 8208, "name": "playing baseball", "start_offset": 8208 },
      { "confidence": 50, "end_offset": 10625, "name": "playing baseball", "start_offset": 10625 },
      { "confidence": 50, "end_offset": 12416, "name": "playing baseball", "start_offset": 12208 },
      { "confidence": 50, "end_offset": 9416, "name": "skating", "start_offset": 9000 },
      { "confidence": 50, "end_offset": 9833, "name": "play", "start_offset": 9208 },
      { "confidence": 51, "end_offset": 10208, "name": "hound", "start_offset": 9625 },
      { "confidence": 50, "end_offset": 14416, "name": "hound", "start_offset": 14416 },
      { "confidence": 51, "end_offset": 10000, "name": "pointer", "start_offset": 9625 },
      { "confidence": 50, "end_offset": 11416, "name": "pointer", "start_offset": 11416 },
      { "confidence": 50, "end_offset": 10000, "name": "bulldog", "start_offset": 10000 },
      { "confidence": 50, "end_offset": 10416, "name": "gravestone", "start_offset": 10416 },
      { "confidence": 50, "end_offset": 12833, "name": "neighborhood", "start_offset": 12833 },
      { "confidence": 50, "end_offset": 18000, "name": "neighborhood", "start_offset": 18000 },
      { "confidence": 50, "end_offset": 13000, "name": "summer", "start_offset": 13000 },
      { "confidence": 50, "end_offset": 18000, "name": "summer", "start_offset": 18000 },
      { "confidence": 50, "end_offset": 14416, "name": "husky", "start_offset": 14208 },
      { "confidence": 61, "end_offset": 15416, "name": "people", "start_offset": 14625 },
      { "confidence": 50, "end_offset": 16625, "name": "people", "start_offset": 16625 },
      { "confidence": 50, "end_offset": 15416, "name": "camera", "start_offset": 15208 },
      { "confidence": 52, "end_offset": 16833, "name": "camera", "start_offset": 16000 },
      { "confidence": 51, "end_offset": 15416, "name": "electronics", "start_offset": 15208 },
      { "confidence": 52, "end_offset": 16833, "name": "electronics", "start_offset": 16000 },
      { "confidence": 50, "end_offset": 16625, "name": "photoshoot", "start_offset": 16208 },
      { "confidence": 50, "end_offset": 17833, "name": "suburb", "start_offset": 17000 },
      { "confidence": 50, "end_offset": 18000, "name": "trail", "start_offset": 17833 },
      { "confidence": 50, "end_offset": 18000, "name": "tree trunk", "start_offset": 17833 },
      { "confidence": 50, "end_offset": 18000, "name": "backyard", "start_offset": 18000 },
      { "confidence": 50, "end_offset": 18000, "name": "yard", "start_offset": 18000 }
    ]
  },
  "tags": {
    "auto": {
      "accessories": 51,
      "adult": 87,
      "animal": 97,
      "antelope": 50,
      "autumn": 68,
      "backyard": 50,
      "bag": 53,
      "baseball": 53,
      "baseball game": 53,
      "bicycle": 51,
      "body part": 58,
      "bulldog": 50,
      "camera": 52,
      "camping": 50,
      "canine": 97,
      "chasing": 52,
      "city": 52,
      "clothing": 82,
      "coat": 76,
      "crawling": 52,
      "cycling": 51,
      "dancing": 58,
      "deer": 51,
      "dog": 97,
      "electronics": 52,
      "fallen person": 56,
      "female": 76,
      "fitness": 57,
      "flare": 96,
      "footwear": 70,
      "fox": 52,
      "glasses": 51,
      "golden retriever": 53,
      "grass": 99,
      "gravestone": 50,
      "graveyard": 63,
      "ground": 56,
      "grove": 94,
      "guitar": 50,
      "guitarist": 50,
      "hand": 58,
      "holding hands": 53,
      "horse": 78,
      "horseback riding": 78,
      "hound": 51,
      "husky": 51,
      "impala": 50,
      "jungle": 53,
      "kissing": 51,
      "koala": 51,
      "land": 94,
      "landscape": 69,
      "leisure activities": 78,
      "light": 96,
      "lion": 50,
      "male": 87,
      "mammal": 97,
      "man": 87,
      "music": 50,
      "musical instrument": 50,
      "musician": 50,
      "nature": 99,
      "neighborhood": 50,
      "oak": 50,
      "outdoors": 99,
      "panoramic": 56,
      "park": 99,
      "path": 53,
      "pedestrian": 51,
      "people": 77,
      "performer": 52,
      "person": 99,
      "pet": 97,
      "photography": 54,
      "photoshoot": 50,
      "pilates": 51,
      "plant": 99,
      "play": 50,
      "playing baseball": 50,
      "pointer": 51,
      "puppy": 55,
      "pushing": 53,
      "rainforest": 50,
      "red wolf": 50,
      "road": 51,
      "romantic": 54,
      "running": 57,
      "scenery": 97,
      "shoe": 70,
      "sitting": 70,
      "skating": 51,
      "sky": 98,
      "sport": 57,
      "street": 51,
      "stretch": 50,
      "suburb": 50,
      "summer": 50,
      "sun": 85,
      "sunlight": 99,
      "sunrise": 57,
      "sunset": 55,
      "tomb": 52,
      "trail": 51,
      "transportation": 51,
      "tree": 97,
      "tree trunk": 50,
      "urban": 51,
      "vegetation": 99,
      "vehicle": 51,
      "volleyball": 51,
      "volleyball game": 51,
      "walking": 99,
      "walking dog": 92,
      "wildlife": 54,
      "wolf": 51,
      "woman": 68,
      "woodland": 94,
      "working out": 58,
      "yard": 50,
      "yoga": 57,
      "zoo": 52
    },
    "user": null
  }
}

https://cdn.filestackcontent.com/APIKEY/security=p:POLICY,s:SIGNATURE/workflow_status=job_id:JOB_ID

Run Filestack AI by URL or on Upload

Put a task in a CDN URL and read the JSON back.

By URL
GET https://cdn.filestackcontent.com/
  security=p:POLICY,s:SIGNATURE/
  caption/
  pnlLw9AsSJWS21KtIQbL

// 200 OK
{ "caption": "a dog standing in a room" }

Attach a workflow to the picker and every upload runs its AI tasks.

On upload
const client = filestack.init('YOUR_API_KEY');
const options = {
  storeTo: { workflows: ['YOUR_WORKFLOW_ID'] },
};
client.picker(options).open();

// each result arrives on the fs.workflow webhook

Why You’ll Love AI-Powered Uploads

Quick and Easy Uploads

An image upload API and picker for local files, social accounts and cloud storage.

AI Image Tagging and Categorization

Object recognition labels every image as it arrives, ready to search.

Enhanced Image Analysis

Object recognition, OCR and explicit content detection, read as the file lands.

Seamless Integration

SDKs for JavaScript, React, Angular, Python, Ruby, PHP, Java, Go and Android.

Secure and Reliable

Signed policies on every AI call, on a SOC 2 compliant platform.

Actionable Insights

Scores and labels your code can branch on, in a workflow or your backend.

Frequently Asked Questions About Filestack AI

Are my uploaded documents safe?

Yes, your uploaded documents are safe. Filestack uses secure transfer methods to handle sensitive files, ensuring your data is protected during uploads.

How many documents can I upload?

The number of documents you can upload depends on your subscription plan. Filestack typically limits the size per file and the total storage capacity. For specific limits, you should refer to the details of your plan.

Is Filestack Image Uploader Powered by AI Free?

Uploads and the picker are on every plan. Filestack AI tasks such as image tagging, OCR and explicit content detection run on paid plans. Check the Filestack pricing page or contact our support team for up-to-date details.

How does an AI File Uploader work?

The AI File Uploader by Filestack automatically analyzes and processes files as uploaded. It can detect and filter content, such as identifying unsafe material, and perform transformations like resizing or optimizing files before they reach your application.

What is AI Image processing?

AI Image processing uses advanced artificial intelligence algorithms to analyze and modify images, enhancing their quality or applying various effects automatically. This technology can save time and improve results compared to manual editing.

How can AI Image processing benefit my workflow?

AI Image processing can streamline your image editing workflow by automating many tasks that would typically require manual intervention. This can lead to increased productivity, consistency in results, and the ability to process large volumes of images quickly and efficiently.

What is the "enhance" preset, and how does it work?

The “enhance” preset is an AI-enabled feature powered by eyeq.photos technology. It uses sophisticated machine learning algorithms to automatically improve image quality, adjusting elements like brightness, contrast, and sharpness. This preset can breathe new life into your images with just a single click, making them more vibrant and visually appealing.