Build & Train AI
Vertical AI
Explore our full suite of AI platforms, data marketplaces, and expert services designed to build, train, fine-tune, and deploy reliable, production-grade AI systems at scale.
Build & Train AI
Vertical AI
Explore our full suite of AI platforms, data marketplaces, and expert services designed to build, train, fine-tune, and deploy reliable, production-grade AI systems at scale.
Build & Train AI
Vertical AI
Explore our full suite of AI platforms, data marketplaces, and expert services designed to build, train, fine-tune, and deploy reliable, production-grade AI systems at scale.
Build & Train AI
Vertical AI
Explore our full suite of AI platforms, data marketplaces, and expert services designed to build, train, fine-tune, and deploy reliable, production-grade AI systems at scale.
![](data:image/svg+xml,<svg display="block" role="presentation" viewBox="0 0 67 12" xmlns="http://www.w3.org/2000/svg"><g d="M 8.704 0.336 C 8.629 0.206 8.481 0.168 8.35 0.224 L 5.898 1.306 C 5.879 1.306 5.86 1.306 5.842 1.306 L 3.389 0.224 C 3.259 0.168 3.11 0.224 3.036 0.336 C 2.961 0.467 2.999 0.616 3.129 0.691 L 5.507 2.164 C 5.507 2.164 5.693 2.276 5.86 2.276 C 6.065 2.276 6.232 2.164 6.232 2.164 L 8.611 0.691 C 8.722 0.616 8.759 0.467 8.704 0.336 Z M 2.161 1.698 L 5.432 4.421 C 5.432 4.421 5.636 4.608 5.859 4.608 C 6.064 4.608 6.287 4.421 6.287 4.421 L 9.557 1.698 C 9.688 1.605 9.743 1.437 9.65 1.288 C 9.576 1.139 9.39 1.083 9.242 1.157 L 6.157 3.228 L 5.878 3.395 C 5.859 3.395 5.822 3.395 5.785 3.395 L 5.506 3.228 L 2.422 1.157 C 2.273 1.083 2.106 1.157 2.013 1.288 C 1.939 1.437 1.976 1.605 2.106 1.698 Z M 11.621 8.935 C 11.751 8.86 11.788 8.711 11.732 8.58 L 10.654 6.118 C 10.654 6.1 10.654 6.081 10.654 6.062 L 11.732 3.6 C 11.788 3.47 11.732 3.321 11.621 3.246 C 11.491 3.171 11.342 3.209 11.268 3.339 L 9.8 5.727 C 9.8 5.727 9.688 5.913 9.688 6.081 C 9.688 6.286 9.8 6.454 9.8 6.454 L 11.268 8.842 C 11.342 8.953 11.491 8.991 11.621 8.935 Z M 10.264 2.37 L 7.551 5.652 C 7.551 5.652 7.365 5.857 7.365 6.081 C 7.365 6.286 7.551 6.51 7.551 6.51 L 10.264 9.793 C 10.357 9.924 10.524 9.979 10.673 9.886 C 10.821 9.812 10.877 9.625 10.803 9.476 L 8.74 6.38 L 8.573 6.1 C 8.573 6.081 8.573 6.044 8.573 6.007 L 8.74 5.727 L 10.803 2.631 C 10.877 2.481 10.803 2.314 10.673 2.22 C 10.524 2.146 10.357 2.183 10.264 2.314 Z M 3.054 11.863 C 3.129 11.993 3.277 12.031 3.407 11.975 L 5.86 10.893 C 5.879 10.893 5.897 10.893 5.916 10.893 L 8.369 11.975 C 8.499 12.031 8.648 11.975 8.722 11.863 C 8.796 11.732 8.759 11.583 8.629 11.509 L 6.251 10.035 C 6.251 10.035 6.065 9.923 5.897 9.923 C 5.693 9.923 5.526 10.035 5.526 10.035 L 3.147 11.509 C 3.036 11.583 2.999 11.732 3.054 11.863 Z M 9.595 10.501 L 6.324 7.778 C 6.324 7.778 6.12 7.592 5.897 7.592 C 5.692 7.592 5.469 7.778 5.469 7.778 L 2.199 10.501 C 2.069 10.594 2.013 10.762 2.106 10.912 C 2.18 11.061 2.366 11.117 2.515 11.042 L 5.599 8.972 L 5.878 8.804 C 5.897 8.804 5.934 8.804 5.971 8.804 L 6.25 8.972 L 9.334 11.042 C 9.483 11.117 9.65 11.042 9.743 10.912 C 9.818 10.762 9.78 10.594 9.65 10.501 Z M 0.137 3.265 C 0.007 3.339 -0.031 3.489 0.025 3.619 L 1.103 6.081 C 1.103 6.1 1.103 6.119 1.103 6.137 L 0.025 8.599 C -0.031 8.73 0.025 8.879 0.137 8.954 C 0.267 9.028 0.415 8.991 0.49 8.86 L 1.958 6.473 C 1.958 6.473 2.069 6.286 2.069 6.119 C 2.069 5.913 1.958 5.746 1.958 5.746 L 0.49 3.358 C 0.415 3.246 0.267 3.209 0.137 3.265 Z M 1.493 9.83 L 4.206 6.547 C 4.206 6.547 4.392 6.342 4.392 6.118 C 4.392 5.913 4.206 5.689 4.206 5.689 L 1.493 2.369 C 1.4 2.239 1.233 2.183 1.084 2.276 C 0.936 2.351 0.88 2.537 0.954 2.687 L 3.017 5.783 L 3.184 6.062 C 3.184 6.081 3.184 6.118 3.184 6.156 L 3.017 6.436 L 0.954 9.532 C 0.88 9.681 0.954 9.849 1.084 9.942 C 1.233 10.017 1.4 9.979 1.493 9.849 Z M 19.616 4.552 C 20.117 4.552 20.506 4.664 20.84 4.887 C 21.174 5.111 21.397 5.418 21.536 5.837 L 23.344 5.837 C 23.122 4.971 22.677 4.301 22.037 3.826 C 21.397 3.351 20.59 3.128 19.588 3.128 C 18.587 3.128 18.114 3.295 17.529 3.658 C 16.917 3.994 16.472 4.496 16.11 5.139 C 15.776 5.781 15.609 6.507 15.609 7.345 C 15.609 8.183 15.776 8.937 16.11 9.551 C 16.444 10.193 16.917 10.696 17.529 11.031 C 18.141 11.394 18.809 11.562 19.588 11.562 C 20.367 11.562 21.369 11.311 22.009 10.836 C 22.649 10.333 23.094 9.691 23.344 8.853 L 21.536 8.853 C 21.23 9.719 20.59 10.138 19.588 10.138 C 18.587 10.138 18.364 9.886 17.947 9.384 C 17.529 8.881 17.334 8.211 17.334 7.317 C 17.334 6.423 17.529 5.753 17.947 5.278 C 18.364 4.776 18.893 4.552 19.588 4.552 Z M 30.106 3.631 C 29.494 3.295 28.826 3.128 28.047 3.128 C 27.268 3.128 26.545 3.295 25.932 3.658 C 25.32 3.994 24.847 4.496 24.513 5.139 C 24.179 5.781 24.013 6.507 24.013 7.345 C 24.013 8.183 24.179 8.937 24.541 9.551 C 24.903 10.193 25.376 10.696 25.988 11.031 C 26.6 11.394 27.296 11.562 28.075 11.562 C 28.854 11.562 29.828 11.311 30.468 10.836 C 31.108 10.361 31.553 9.747 31.803 8.993 L 29.995 8.993 C 29.633 9.747 28.993 10.138 28.075 10.138 C 27.157 10.138 26.906 9.942 26.489 9.551 C 26.044 9.16 25.821 8.63 25.765 7.987 L 31.97 7.987 C 31.998 7.736 32.026 7.457 32.026 7.149 C 32.026 6.367 31.859 5.669 31.525 5.055 C 31.191 4.44 30.718 3.966 30.134 3.631 Z M 25.738 6.619 C 25.821 5.976 26.072 5.474 26.489 5.111 C 26.906 4.748 27.407 4.552 27.991 4.552 C 28.576 4.552 29.16 4.748 29.605 5.111 C 30.05 5.474 30.273 5.976 30.273 6.619 L 25.765 6.619 Z M 38.982 3.519 C 38.481 3.24 37.896 3.128 37.257 3.128 C 36.616 3.128 36.311 3.212 35.893 3.407 C 35.476 3.603 35.114 3.854 34.836 4.189 L 34.836 3.267 L 33.166 3.267 L 33.166 11.422 L 34.836 11.422 L 34.836 6.87 C 34.836 6.144 35.031 5.585 35.392 5.194 C 35.754 4.803 36.255 4.608 36.895 4.608 C 37.535 4.608 38.008 4.803 38.397 5.194 C 38.759 5.585 38.954 6.144 38.954 6.87 L 38.954 11.422 L 40.623 11.422 L 40.623 6.619 C 40.623 5.865 40.484 5.25 40.206 4.72 C 39.928 4.189 39.51 3.798 39.01 3.547 Z M 44.101 1.229 L 42.404 1.229 L 42.404 3.268 L 41.458 3.268 L 41.458 4.636 L 42.404 4.636 L 42.404 9.16 C 42.404 9.97 42.599 10.529 43.016 10.892 C 43.406 11.255 43.99 11.422 44.769 11.422 L 46.105 11.422 L 46.105 10.026 L 45.075 10.026 C 44.741 10.026 44.491 9.97 44.352 9.831 C 44.213 9.691 44.129 9.468 44.129 9.16 L 44.129 4.636 L 46.105 4.636 L 46.105 3.268 L 44.129 3.268 L 44.129 1.229 Z M 48.469 0 C 48.163 0 47.912 0.112 47.69 0.307 C 47.495 0.503 47.384 0.782 47.384 1.089 C 47.384 1.396 47.495 1.648 47.69 1.871 C 47.884 2.095 48.163 2.178 48.469 2.178 C 48.775 2.178 49.025 2.067 49.22 1.871 C 49.415 1.676 49.526 1.396 49.526 1.089 C 49.526 0.782 49.415 0.531 49.22 0.307 C 49.025 0.112 48.775 0 48.469 0 Z M 49.276 3.267 L 47.607 3.267 L 47.607 11.422 L 49.276 11.422 Z M 56.928 0 C 56.621 0 56.371 0.112 56.148 0.307 C 55.954 0.503 55.842 0.782 55.842 1.089 C 55.842 1.396 55.954 1.648 56.148 1.871 C 56.343 2.067 56.621 2.178 56.928 2.178 C 57.234 2.178 57.484 2.067 57.679 1.871 C 57.873 1.676 57.985 1.396 57.985 1.089 C 57.985 0.782 57.873 0.531 57.679 0.307 C 57.484 0.112 57.234 0 56.928 0 Z M 63.271 10.138 C 62.576 10.138 62.047 9.886 61.63 9.384 C 61.212 8.881 61.018 8.211 61.018 7.317 C 61.018 6.423 61.212 5.753 61.63 5.278 C 62.047 4.776 62.576 4.552 63.271 4.552 C 63.967 4.552 64.162 4.664 64.496 4.887 C 64.83 5.111 65.052 5.418 65.191 5.837 L 67 5.837 C 66.777 4.971 66.332 4.301 65.692 3.826 C 65.052 3.351 64.245 3.128 63.244 3.128 C 62.242 3.128 61.769 3.295 61.185 3.658 C 60.573 3.994 60.127 4.496 59.766 5.139 C 59.432 5.781 59.265 6.507 59.265 7.345 C 59.265 8.183 59.432 8.937 59.766 9.551 C 60.1 10.193 60.573 10.696 61.185 11.031 C 61.797 11.394 62.464 11.562 63.244 11.562 C 64.023 11.562 65.024 11.311 65.665 10.836 C 66.304 10.333 66.749 9.691 67 8.853 L 65.191 8.853 C 64.885 9.719 64.245 10.138 63.244 10.138 Z M 57.736 3.268 L 53.506 3.268 L 53.506 2.681 C 53.506 2.206 53.617 1.871 53.812 1.676 C 54.007 1.508 54.341 1.396 54.786 1.396 L 54.786 0 C 53.784 0 53.033 0.223 52.56 0.642 C 52.059 1.061 51.809 1.759 51.809 2.681 L 51.809 3.268 L 50.863 3.268 L 50.863 4.636 L 51.809 4.636 L 51.809 11.422 L 53.506 11.422 L 53.506 4.636 L 56.066 4.636 L 56.066 11.422 L 57.736 11.422 Z" fill="transparent" height="12px" id="T0f4ijcc7" width="67.00000047838026px"><path d="M 5.706 0.137 C 5.631 0.007 5.483 -0.031 5.353 0.025 L 2.9 1.107 C 2.881 1.107 2.862 1.107 2.844 1.107 L 0.391 0.025 C 0.261 -0.031 0.112 0.025 0.038 0.137 C -0.036 0.268 0.001 0.417 0.131 0.491 L 2.509 1.965 C 2.509 1.965 2.695 2.077 2.862 2.077 C 3.067 2.077 3.234 1.965 3.234 1.965 L 5.613 0.491 C 5.724 0.417 5.761 0.268 5.706 0.137 Z" fill="rgb(0, 0, 0)" height="2.0768733066194303px" id="D9zvikPGQ" transform="translate(2.998 0.199)" width="5.731236321640176px"/><path d="M 0.187 0.574 L 3.457 3.297 C 3.457 3.297 3.662 3.483 3.885 3.483 C 4.089 3.483 4.312 3.297 4.312 3.297 L 7.582 0.574 C 7.713 0.481 7.768 0.313 7.675 0.163 C 7.601 0.014 7.415 -0.042 7.267 0.033 L 4.182 2.103 L 3.903 2.271 C 3.885 2.271 3.847 2.271 3.81 2.271 L 3.531 2.103 L 0.447 0.033 C 0.298 -0.042 0.131 0.033 0.038 0.163 C -0.036 0.313 0.001 0.481 0.131 0.574 Z" fill="rgb(0, 0, 0)" height="3.483465559558452px" id="XHPDa9C06" transform="translate(1.975 1.124)" width="7.7215397232611345px"/><path d="M 1.933 5.727 C 2.063 5.652 2.1 5.503 2.044 5.372 L 0.966 2.91 C 0.966 2.892 0.966 2.873 0.966 2.854 L 2.044 0.392 C 2.1 0.262 2.044 0.113 1.933 0.038 C 1.803 -0.037 1.654 0.001 1.58 0.131 L 0.112 2.519 C 0.112 2.519 0 2.705 0 2.873 C 0 3.078 0.112 3.246 0.112 3.246 L 1.58 5.634 C 1.654 5.745 1.803 5.783 1.933 5.727 Z" fill="rgb(0, 0, 0)" height="5.752520201428617px" id="fSIVTyYyB" transform="translate(9.688 3.208)" width="2.0692037208998664px"/><path d="M 2.899 0.187 L 0.186 3.47 C 0.186 3.47 0 3.675 0 3.899 C 0 4.104 0.186 4.328 0.186 4.328 L 2.899 7.611 C 2.992 7.741 3.159 7.797 3.308 7.704 C 3.456 7.629 3.512 7.443 3.438 7.294 L 1.375 4.197 L 1.208 3.918 C 1.208 3.899 1.208 3.862 1.208 3.824 L 1.375 3.545 L 3.438 0.448 C 3.512 0.299 3.438 0.131 3.308 0.038 C 3.159 -0.037 2.992 0.001 2.899 0.131 Z" fill="rgb(0, 0, 0)" height="7.750209501286653px" id="XEy5_M_wF" transform="translate(7.365 2.182)" width="3.470578512630766px"/><path d="M 0.026 1.94 C 0.1 2.07 0.249 2.108 0.379 2.052 L 2.832 0.97 C 2.85 0.97 2.869 0.97 2.887 0.97 L 5.34 2.052 C 5.47 2.108 5.619 2.052 5.693 1.94 C 5.768 1.809 5.731 1.66 5.6 1.585 L 3.222 0.112 C 3.222 0.112 3.036 0 2.869 0 C 2.664 0 2.497 0.112 2.497 0.112 L 0.119 1.585 C 0.007 1.66 -0.03 1.809 0.026 1.94 Z" fill="rgb(0, 0, 0)" height="2.0768899917602535px" id="GVAyPotmc" transform="translate(3.029 9.923)" width="5.731234655014601px"/><path d="M 7.535 2.91 L 4.264 0.187 C 4.264 0.187 4.06 0 3.837 0 C 3.633 0 3.41 0.187 3.41 0.187 L 0.139 2.91 C 0.009 3.003 -0.047 3.171 0.046 3.32 C 0.12 3.469 0.306 3.525 0.455 3.451 L 3.54 1.38 L 3.818 1.212 C 3.837 1.212 3.874 1.212 3.911 1.212 L 4.19 1.38 L 7.275 3.451 C 7.423 3.525 7.591 3.451 7.684 3.32 C 7.758 3.171 7.721 3.003 7.591 2.91 Z" fill="rgb(0, 0, 0)" height="3.483478327665332px" id="UJGiliWlr" transform="translate(2.06 7.592)" width="7.721540951544936px"/><path d="M 0.137 0.026 C 0.007 0.1 -0.031 0.25 0.025 0.38 L 1.103 2.842 C 1.103 2.861 1.103 2.879 1.103 2.898 L 0.025 5.36 C -0.031 5.491 0.025 5.64 0.137 5.715 C 0.267 5.789 0.415 5.752 0.49 5.621 L 1.958 3.234 C 1.958 3.234 2.069 3.047 2.069 2.879 C 2.069 2.674 1.958 2.506 1.958 2.506 L 0.49 0.119 C 0.415 0.007 0.267 -0.03 0.137 0.026 Z" fill="rgb(0, 0, 0)" height="5.752526692867306px" id="DM37PXgJd" transform="translate(0 3.239)" width="2.0691900389284807px"/><path d="M 0.572 7.6 L 3.285 4.318 C 3.285 4.318 3.471 4.112 3.471 3.889 C 3.471 3.683 3.285 3.46 3.285 3.46 L 0.572 0.14 C 0.479 0.009 0.312 -0.047 0.163 0.046 C 0.014 0.121 -0.042 0.307 0.033 0.457 L 2.095 3.553 L 2.263 3.833 C 2.263 3.851 2.263 3.889 2.263 3.926 L 2.095 4.206 L 0.033 7.302 C -0.042 7.451 0.033 7.619 0.163 7.712 C 0.312 7.787 0.479 7.749 0.572 7.619 Z" fill="rgb(0, 0, 0)" height="7.750215230634018px" id="omz1nxiI6" transform="translate(0.922 2.23)" width="3.470588058544221px"/><path d="M 4.007 1.424 C 4.508 1.424 4.897 1.536 5.231 1.759 C 5.565 1.983 5.787 2.29 5.927 2.709 L 7.735 2.709 C 7.513 1.843 7.067 1.173 6.427 0.698 C 5.787 0.223 4.981 0 3.979 0 C 2.977 0 2.504 0.168 1.92 0.531 C 1.308 0.866 0.863 1.368 0.501 2.011 C 0.167 2.653 0 3.379 0 4.217 C 0 5.055 0.167 5.809 0.501 6.423 C 0.835 7.066 1.308 7.568 1.92 7.903 C 2.532 8.267 3.2 8.434 3.979 8.434 C 4.758 8.434 5.76 8.183 6.4 7.708 C 7.04 7.205 7.485 6.563 7.735 5.725 L 5.927 5.725 C 5.62 6.591 4.981 7.01 3.979 7.01 C 2.977 7.01 2.755 6.758 2.337 6.256 C 1.92 5.753 1.725 5.083 1.725 4.189 C 1.725 3.295 1.92 2.625 2.337 2.15 C 2.755 1.648 3.283 1.424 3.979 1.424 Z" fill="rgb(0, 0, 0)" height="8.434076339054002px" id="fGIMtJKmR" transform="translate(15.609 3.128)" width="7.735096310284753px"/><path d="M 6.093 0.503 C 5.481 0.168 4.814 0 4.034 0 C 3.255 0 2.532 0.168 1.92 0.531 C 1.308 0.866 0.835 1.368 0.501 2.011 C 0.167 2.653 0 3.379 0 4.217 C 0 5.055 0.167 5.809 0.529 6.423 C 0.89 7.066 1.363 7.568 1.976 7.903 C 2.588 8.267 3.283 8.434 4.062 8.434 C 4.841 8.434 5.815 8.183 6.455 7.708 C 7.095 7.233 7.54 6.619 7.791 5.865 L 5.982 5.865 C 5.62 6.619 4.98 7.01 4.062 7.01 C 3.144 7.01 2.894 6.814 2.476 6.423 C 2.031 6.032 1.809 5.502 1.753 4.859 L 7.958 4.859 C 7.985 4.608 8.013 4.329 8.013 4.022 C 8.013 3.24 7.846 2.541 7.512 1.927 C 7.179 1.313 6.706 0.838 6.121 0.503 Z M 1.725 3.491 C 1.809 2.849 2.059 2.346 2.476 1.983 C 2.894 1.62 3.395 1.424 3.979 1.424 C 4.563 1.424 5.147 1.62 5.593 1.983 C 6.038 2.346 6.26 2.849 6.26 3.491 L 1.753 3.491 Z" fill="rgb(0, 0, 0)" height="8.434076339054002px" id="qaL5FVZij" transform="translate(24.013 3.128)" width="8.01332295195354px"/><path d="M 5.815 0.391 C 5.314 0.112 4.73 0 4.09 0 C 3.45 0 3.144 0.084 2.727 0.279 C 2.309 0.475 1.948 0.726 1.669 1.061 L 1.669 0.14 L 0 0.14 L 0 8.294 L 1.669 8.294 L 1.669 3.742 C 1.669 3.016 1.864 2.458 2.226 2.067 C 2.587 1.676 3.088 1.48 3.729 1.48 C 4.368 1.48 4.841 1.676 5.231 2.067 C 5.592 2.458 5.787 3.016 5.787 3.742 L 5.787 8.294 L 7.457 8.294 L 7.457 3.491 C 7.457 2.737 7.318 2.122 7.04 1.592 C 6.761 1.061 6.344 0.67 5.843 0.419 Z" fill="rgb(0, 0, 0)" height="8.294435702953454px" id="WDjXqIjZT" transform="translate(33.166 3.128)" width="7.4567073744807075px"/><path d="M 2.643 0 L 0.946 0 L 0.946 2.039 L 0 2.039 L 0 3.407 L 0.946 3.407 L 0.946 7.931 C 0.946 8.741 1.141 9.3 1.558 9.663 C 1.948 10.026 2.532 10.193 3.311 10.193 L 4.647 10.193 L 4.647 8.797 L 3.617 8.797 C 3.283 8.797 3.033 8.741 2.894 8.602 C 2.755 8.462 2.671 8.239 2.671 7.931 L 2.671 3.407 L 4.647 3.407 L 4.647 2.039 L 2.671 2.039 L 2.671 0 Z" fill="rgb(0, 0, 0)" height="10.193494685087567px" id="nZ8KAi6H2" transform="translate(41.458 1.229)" width="4.646524285330045px"/><path d="M 1.085 0 C 0.779 0 0.528 0.112 0.306 0.307 C 0.111 0.503 0 0.782 0 1.089 C 0 1.396 0.111 1.648 0.306 1.871 C 0.501 2.095 0.779 2.178 1.085 2.178 C 1.391 2.178 1.642 2.067 1.836 1.871 C 2.031 1.676 2.142 1.396 2.142 1.089 C 2.142 0.782 2.031 0.531 1.836 0.307 C 1.642 0.112 1.391 0 1.085 0 Z" fill="rgb(0, 0, 0)" height="2.1783368721996954px" id="xofK7bnqV" transform="translate(47.384 0)" width="2.1424521645366994px"/><path d="M 1.669 0 L 0 0 L 0 8.155 L 1.669 8.155 Z" fill="rgb(0, 0, 0)" height="8.154788300335934px" id="owXtEW3wx" transform="translate(47.607 3.267)" width="1.6694896185170123px"/><path d="M 1.085 0 C 0.779 0 0.529 0.112 0.306 0.307 C 0.111 0.503 0 0.782 0 1.089 C 0 1.396 0.111 1.648 0.306 1.871 C 0.501 2.067 0.779 2.178 1.085 2.178 C 1.391 2.178 1.642 2.067 1.836 1.871 C 2.031 1.676 2.142 1.396 2.142 1.089 C 2.142 0.782 2.031 0.531 1.836 0.307 C 1.642 0.112 1.391 0 1.085 0 Z" fill="rgb(0, 0, 0)" height="2.1783368721996954px" id="qYXTLcef6" transform="translate(55.842 0)" width="2.1424521645367136px"/><path d="M 4.007 7.01 C 3.311 7.01 2.783 6.758 2.365 6.256 C 1.948 5.753 1.753 5.083 1.753 4.189 C 1.753 3.295 1.948 2.625 2.365 2.15 C 2.783 1.648 3.311 1.424 4.007 1.424 C 4.702 1.424 4.897 1.536 5.231 1.759 C 5.565 1.983 5.788 2.29 5.926 2.709 L 7.735 2.709 C 7.513 1.843 7.067 1.173 6.427 0.698 C 5.788 0.223 4.981 0 3.979 0 C 2.977 0 2.504 0.168 1.92 0.531 C 1.308 0.866 0.862 1.368 0.501 2.011 C 0.167 2.653 0 3.379 0 4.217 C 0 5.055 0.167 5.809 0.501 6.423 C 0.835 7.066 1.308 7.568 1.92 7.903 C 2.532 8.267 3.2 8.434 3.979 8.434 C 4.758 8.434 5.76 8.183 6.4 7.708 C 7.04 7.205 7.485 6.563 7.735 5.725 L 5.926 5.725 C 5.621 6.591 4.981 7.01 3.979 7.01 Z" fill="rgb(0, 0, 0)" height="8.434076339054002px" id="YVPoMPmqZ" transform="translate(59.265 3.128)" width="7.735226155870485px"/><path d="M 6.872 3.268 L 2.643 3.268 L 2.643 2.681 C 2.643 2.206 2.754 1.871 2.949 1.676 C 3.144 1.508 3.478 1.396 3.923 1.396 L 3.923 0 C 2.921 0 2.17 0.223 1.697 0.642 C 1.196 1.061 0.946 1.759 0.946 2.681 L 0.946 3.268 L 0 3.268 L 0 4.636 L 0.946 4.636 L 0.946 11.422 L 2.643 11.422 L 2.643 4.636 L 5.203 4.636 L 5.203 11.422 L 6.872 11.422 Z" fill="rgb(0, 0, 0)" height="11.422308710362525px" id="mR0Sii5R7" transform="translate(50.863 0)" width="6.872402238697973px"/></g></svg>)
![](data:image/svg+xml,<svg display="block" role="presentation" viewBox="0 0 67 12" xmlns="http://www.w3.org/2000/svg"><g d="M 8.704 0.336 C 8.629 0.206 8.481 0.168 8.35 0.224 L 5.898 1.306 C 5.879 1.306 5.86 1.306 5.842 1.306 L 3.389 0.224 C 3.259 0.168 3.11 0.224 3.036 0.336 C 2.961 0.467 2.999 0.616 3.129 0.691 L 5.507 2.164 C 5.507 2.164 5.693 2.276 5.86 2.276 C 6.065 2.276 6.232 2.164 6.232 2.164 L 8.611 0.691 C 8.722 0.616 8.759 0.467 8.704 0.336 Z M 2.161 1.698 L 5.432 4.421 C 5.432 4.421 5.636 4.608 5.859 4.608 C 6.064 4.608 6.287 4.421 6.287 4.421 L 9.557 1.698 C 9.688 1.605 9.743 1.437 9.65 1.288 C 9.576 1.139 9.39 1.083 9.242 1.157 L 6.157 3.228 L 5.878 3.395 C 5.859 3.395 5.822 3.395 5.785 3.395 L 5.506 3.228 L 2.422 1.157 C 2.273 1.083 2.106 1.157 2.013 1.288 C 1.939 1.437 1.976 1.605 2.106 1.698 Z M 11.621 8.935 C 11.751 8.86 11.788 8.711 11.732 8.58 L 10.654 6.118 C 10.654 6.1 10.654 6.081 10.654 6.062 L 11.732 3.6 C 11.788 3.47 11.732 3.321 11.621 3.246 C 11.491 3.171 11.342 3.209 11.268 3.339 L 9.8 5.727 C 9.8 5.727 9.688 5.913 9.688 6.081 C 9.688 6.286 9.8 6.454 9.8 6.454 L 11.268 8.842 C 11.342 8.953 11.491 8.991 11.621 8.935 Z M 10.264 2.37 L 7.551 5.652 C 7.551 5.652 7.365 5.857 7.365 6.081 C 7.365 6.286 7.551 6.51 7.551 6.51 L 10.264 9.793 C 10.357 9.924 10.524 9.979 10.673 9.886 C 10.821 9.812 10.877 9.625 10.803 9.476 L 8.74 6.38 L 8.573 6.1 C 8.573 6.081 8.573 6.044 8.573 6.007 L 8.74 5.727 L 10.803 2.631 C 10.877 2.481 10.803 2.314 10.673 2.22 C 10.524 2.146 10.357 2.183 10.264 2.314 Z M 3.054 11.863 C 3.129 11.993 3.277 12.031 3.407 11.975 L 5.86 10.893 C 5.879 10.893 5.897 10.893 5.916 10.893 L 8.369 11.975 C 8.499 12.031 8.648 11.975 8.722 11.863 C 8.796 11.732 8.759 11.583 8.629 11.509 L 6.251 10.035 C 6.251 10.035 6.065 9.923 5.897 9.923 C 5.693 9.923 5.526 10.035 5.526 10.035 L 3.147 11.509 C 3.036 11.583 2.999 11.732 3.054 11.863 Z M 9.595 10.501 L 6.324 7.778 C 6.324 7.778 6.12 7.592 5.897 7.592 C 5.692 7.592 5.469 7.778 5.469 7.778 L 2.199 10.501 C 2.069 10.594 2.013 10.762 2.106 10.912 C 2.18 11.061 2.366 11.117 2.515 11.042 L 5.599 8.972 L 5.878 8.804 C 5.897 8.804 5.934 8.804 5.971 8.804 L 6.25 8.972 L 9.334 11.042 C 9.483 11.117 9.65 11.042 9.743 10.912 C 9.818 10.762 9.78 10.594 9.65 10.501 Z M 0.137 3.265 C 0.007 3.339 -0.031 3.489 0.025 3.619 L 1.103 6.081 C 1.103 6.1 1.103 6.119 1.103 6.137 L 0.025 8.599 C -0.031 8.73 0.025 8.879 0.137 8.954 C 0.267 9.028 0.415 8.991 0.49 8.86 L 1.958 6.473 C 1.958 6.473 2.069 6.286 2.069 6.119 C 2.069 5.913 1.958 5.746 1.958 5.746 L 0.49 3.358 C 0.415 3.246 0.267 3.209 0.137 3.265 Z M 1.493 9.83 L 4.206 6.547 C 4.206 6.547 4.392 6.342 4.392 6.118 C 4.392 5.913 4.206 5.689 4.206 5.689 L 1.493 2.369 C 1.4 2.239 1.233 2.183 1.084 2.276 C 0.936 2.351 0.88 2.537 0.954 2.687 L 3.017 5.783 L 3.184 6.062 C 3.184 6.081 3.184 6.118 3.184 6.156 L 3.017 6.436 L 0.954 9.532 C 0.88 9.681 0.954 9.849 1.084 9.942 C 1.233 10.017 1.4 9.979 1.493 9.849 Z M 19.616 4.552 C 20.117 4.552 20.506 4.664 20.84 4.887 C 21.174 5.111 21.397 5.418 21.536 5.837 L 23.344 5.837 C 23.122 4.971 22.677 4.301 22.037 3.826 C 21.397 3.351 20.59 3.128 19.588 3.128 C 18.587 3.128 18.114 3.295 17.529 3.658 C 16.917 3.994 16.472 4.496 16.11 5.139 C 15.776 5.781 15.609 6.507 15.609 7.345 C 15.609 8.183 15.776 8.937 16.11 9.551 C 16.444 10.193 16.917 10.696 17.529 11.031 C 18.141 11.394 18.809 11.562 19.588 11.562 C 20.367 11.562 21.369 11.311 22.009 10.836 C 22.649 10.333 23.094 9.691 23.344 8.853 L 21.536 8.853 C 21.23 9.719 20.59 10.138 19.588 10.138 C 18.587 10.138 18.364 9.886 17.947 9.384 C 17.529 8.881 17.334 8.211 17.334 7.317 C 17.334 6.423 17.529 5.753 17.947 5.278 C 18.364 4.776 18.893 4.552 19.588 4.552 Z M 30.106 3.631 C 29.494 3.295 28.826 3.128 28.047 3.128 C 27.268 3.128 26.545 3.295 25.932 3.658 C 25.32 3.994 24.847 4.496 24.513 5.139 C 24.179 5.781 24.013 6.507 24.013 7.345 C 24.013 8.183 24.179 8.937 24.541 9.551 C 24.903 10.193 25.376 10.696 25.988 11.031 C 26.6 11.394 27.296 11.562 28.075 11.562 C 28.854 11.562 29.828 11.311 30.468 10.836 C 31.108 10.361 31.553 9.747 31.803 8.993 L 29.995 8.993 C 29.633 9.747 28.993 10.138 28.075 10.138 C 27.157 10.138 26.906 9.942 26.489 9.551 C 26.044 9.16 25.821 8.63 25.765 7.987 L 31.97 7.987 C 31.998 7.736 32.026 7.457 32.026 7.149 C 32.026 6.367 31.859 5.669 31.525 5.055 C 31.191 4.44 30.718 3.966 30.134 3.631 Z M 25.738 6.619 C 25.821 5.976 26.072 5.474 26.489 5.111 C 26.906 4.748 27.407 4.552 27.991 4.552 C 28.576 4.552 29.16 4.748 29.605 5.111 C 30.05 5.474 30.273 5.976 30.273 6.619 L 25.765 6.619 Z M 38.982 3.519 C 38.481 3.24 37.896 3.128 37.257 3.128 C 36.616 3.128 36.311 3.212 35.893 3.407 C 35.476 3.603 35.114 3.854 34.836 4.189 L 34.836 3.267 L 33.166 3.267 L 33.166 11.422 L 34.836 11.422 L 34.836 6.87 C 34.836 6.144 35.031 5.585 35.392 5.194 C 35.754 4.803 36.255 4.608 36.895 4.608 C 37.535 4.608 38.008 4.803 38.397 5.194 C 38.759 5.585 38.954 6.144 38.954 6.87 L 38.954 11.422 L 40.623 11.422 L 40.623 6.619 C 40.623 5.865 40.484 5.25 40.206 4.72 C 39.928 4.189 39.51 3.798 39.01 3.547 Z M 44.101 1.229 L 42.404 1.229 L 42.404 3.268 L 41.458 3.268 L 41.458 4.636 L 42.404 4.636 L 42.404 9.16 C 42.404 9.97 42.599 10.529 43.016 10.892 C 43.406 11.255 43.99 11.422 44.769 11.422 L 46.105 11.422 L 46.105 10.026 L 45.075 10.026 C 44.741 10.026 44.491 9.97 44.352 9.831 C 44.213 9.691 44.129 9.468 44.129 9.16 L 44.129 4.636 L 46.105 4.636 L 46.105 3.268 L 44.129 3.268 L 44.129 1.229 Z M 48.469 0 C 48.163 0 47.912 0.112 47.69 0.307 C 47.495 0.503 47.384 0.782 47.384 1.089 C 47.384 1.396 47.495 1.648 47.69 1.871 C 47.884 2.095 48.163 2.178 48.469 2.178 C 48.775 2.178 49.025 2.067 49.22 1.871 C 49.415 1.676 49.526 1.396 49.526 1.089 C 49.526 0.782 49.415 0.531 49.22 0.307 C 49.025 0.112 48.775 0 48.469 0 Z M 49.276 3.267 L 47.607 3.267 L 47.607 11.422 L 49.276 11.422 Z M 56.928 0 C 56.621 0 56.371 0.112 56.148 0.307 C 55.954 0.503 55.842 0.782 55.842 1.089 C 55.842 1.396 55.954 1.648 56.148 1.871 C 56.343 2.067 56.621 2.178 56.928 2.178 C 57.234 2.178 57.484 2.067 57.679 1.871 C 57.873 1.676 57.985 1.396 57.985 1.089 C 57.985 0.782 57.873 0.531 57.679 0.307 C 57.484 0.112 57.234 0 56.928 0 Z M 63.271 10.138 C 62.576 10.138 62.047 9.886 61.63 9.384 C 61.212 8.881 61.018 8.211 61.018 7.317 C 61.018 6.423 61.212 5.753 61.63 5.278 C 62.047 4.776 62.576 4.552 63.271 4.552 C 63.967 4.552 64.162 4.664 64.496 4.887 C 64.83 5.111 65.052 5.418 65.191 5.837 L 67 5.837 C 66.777 4.971 66.332 4.301 65.692 3.826 C 65.052 3.351 64.245 3.128 63.244 3.128 C 62.242 3.128 61.769 3.295 61.185 3.658 C 60.573 3.994 60.127 4.496 59.766 5.139 C 59.432 5.781 59.265 6.507 59.265 7.345 C 59.265 8.183 59.432 8.937 59.766 9.551 C 60.1 10.193 60.573 10.696 61.185 11.031 C 61.797 11.394 62.464 11.562 63.244 11.562 C 64.023 11.562 65.024 11.311 65.665 10.836 C 66.304 10.333 66.749 9.691 67 8.853 L 65.191 8.853 C 64.885 9.719 64.245 10.138 63.244 10.138 Z M 57.736 3.268 L 53.506 3.268 L 53.506 2.681 C 53.506 2.206 53.617 1.871 53.812 1.676 C 54.007 1.508 54.341 1.396 54.786 1.396 L 54.786 0 C 53.784 0 53.033 0.223 52.56 0.642 C 52.059 1.061 51.809 1.759 51.809 2.681 L 51.809 3.268 L 50.863 3.268 L 50.863 4.636 L 51.809 4.636 L 51.809 11.422 L 53.506 11.422 L 53.506 4.636 L 56.066 4.636 L 56.066 11.422 L 57.736 11.422 Z" fill="transparent" height="12px" id="FdZkIDqdL" width="67.00000047838026px"><path d="M 5.706 0.137 C 5.631 0.007 5.483 -0.031 5.353 0.025 L 2.9 1.107 C 2.881 1.107 2.862 1.107 2.844 1.107 L 0.391 0.025 C 0.261 -0.031 0.112 0.025 0.038 0.137 C -0.036 0.268 0.001 0.417 0.131 0.491 L 2.509 1.965 C 2.509 1.965 2.695 2.077 2.862 2.077 C 3.067 2.077 3.234 1.965 3.234 1.965 L 5.613 0.491 C 5.724 0.417 5.761 0.268 5.706 0.137 Z" fill="rgb(255, 255, 255)" height="2.0768733066194303px" id="JOqgQgeXH" transform="translate(2.998 0.199)" width="5.731236321640176px"/><path d="M 0.187 0.574 L 3.457 3.297 C 3.457 3.297 3.662 3.483 3.885 3.483 C 4.089 3.483 4.312 3.297 4.312 3.297 L 7.582 0.574 C 7.713 0.481 7.768 0.313 7.675 0.163 C 7.601 0.014 7.415 -0.042 7.267 0.033 L 4.182 2.103 L 3.903 2.271 C 3.885 2.271 3.847 2.271 3.81 2.271 L 3.531 2.103 L 0.447 0.033 C 0.298 -0.042 0.131 0.033 0.038 0.163 C -0.036 0.313 0.001 0.481 0.131 0.574 Z" fill="rgb(255, 255, 255)" height="3.483465559558452px" id="Zd5XdemVt" transform="translate(1.975 1.124)" width="7.7215397232611345px"/><path d="M 1.933 5.727 C 2.063 5.652 2.1 5.503 2.044 5.372 L 0.966 2.91 C 0.966 2.892 0.966 2.873 0.966 2.854 L 2.044 0.392 C 2.1 0.262 2.044 0.113 1.933 0.038 C 1.803 -0.037 1.654 0.001 1.58 0.131 L 0.112 2.519 C 0.112 2.519 0 2.705 0 2.873 C 0 3.078 0.112 3.246 0.112 3.246 L 1.58 5.634 C 1.654 5.745 1.803 5.783 1.933 5.727 Z" fill="rgb(255, 255, 255)" height="5.752520201428617px" id="wm4QOyIBO" transform="translate(9.688 3.208)" width="2.0692037208998664px"/><path d="M 2.899 0.187 L 0.186 3.47 C 0.186 3.47 0 3.675 0 3.899 C 0 4.104 0.186 4.328 0.186 4.328 L 2.899 7.611 C 2.992 7.741 3.159 7.797 3.308 7.704 C 3.456 7.629 3.512 7.443 3.438 7.294 L 1.375 4.197 L 1.208 3.918 C 1.208 3.899 1.208 3.862 1.208 3.824 L 1.375 3.545 L 3.438 0.448 C 3.512 0.299 3.438 0.131 3.308 0.038 C 3.159 -0.037 2.992 0.001 2.899 0.131 Z" fill="rgb(255, 255, 255)" height="7.750209501286653px" id="Tb0_95onW" transform="translate(7.365 2.182)" width="3.470578512630766px"/><path d="M 0.026 1.94 C 0.1 2.07 0.249 2.108 0.379 2.052 L 2.832 0.97 C 2.85 0.97 2.869 0.97 2.887 0.97 L 5.34 2.052 C 5.47 2.108 5.619 2.052 5.693 1.94 C 5.768 1.809 5.731 1.66 5.6 1.585 L 3.222 0.112 C 3.222 0.112 3.036 0 2.869 0 C 2.664 0 2.497 0.112 2.497 0.112 L 0.119 1.585 C 0.007 1.66 -0.03 1.809 0.026 1.94 Z" fill="rgb(255, 255, 255)" height="2.0768899917602535px" id="mLkmMllwc" transform="translate(3.029 9.923)" width="5.731234655014601px"/><path d="M 7.535 2.91 L 4.264 0.187 C 4.264 0.187 4.06 0 3.837 0 C 3.633 0 3.41 0.187 3.41 0.187 L 0.139 2.91 C 0.009 3.003 -0.047 3.171 0.046 3.32 C 0.12 3.469 0.306 3.525 0.455 3.451 L 3.54 1.38 L 3.818 1.212 C 3.837 1.212 3.874 1.212 3.911 1.212 L 4.19 1.38 L 7.275 3.451 C 7.423 3.525 7.591 3.451 7.684 3.32 C 7.758 3.171 7.721 3.003 7.591 2.91 Z" fill="rgb(255, 255, 255)" height="3.483478327665332px" id="r0d9h4JTL" transform="translate(2.06 7.592)" width="7.721540951544936px"/><path d="M 0.137 0.026 C 0.007 0.1 -0.031 0.25 0.025 0.38 L 1.103 2.842 C 1.103 2.861 1.103 2.879 1.103 2.898 L 0.025 5.36 C -0.031 5.491 0.025 5.64 0.137 5.715 C 0.267 5.789 0.415 5.752 0.49 5.621 L 1.958 3.234 C 1.958 3.234 2.069 3.047 2.069 2.879 C 2.069 2.674 1.958 2.506 1.958 2.506 L 0.49 0.119 C 0.415 0.007 0.267 -0.03 0.137 0.026 Z" fill="rgb(255, 255, 255)" height="5.752526692867306px" id="yi3WcGLKq" transform="translate(0 3.239)" width="2.0691900389284807px"/><path d="M 0.572 7.6 L 3.285 4.318 C 3.285 4.318 3.471 4.112 3.471 3.889 C 3.471 3.683 3.285 3.46 3.285 3.46 L 0.572 0.14 C 0.479 0.009 0.312 -0.047 0.163 0.046 C 0.014 0.121 -0.042 0.307 0.033 0.457 L 2.095 3.553 L 2.263 3.833 C 2.263 3.851 2.263 3.889 2.263 3.926 L 2.095 4.206 L 0.033 7.302 C -0.042 7.451 0.033 7.619 0.163 7.712 C 0.312 7.787 0.479 7.749 0.572 7.619 Z" fill="rgb(255, 255, 255)" height="7.750215230634018px" id="p6XslZnAU" transform="translate(0.922 2.23)" width="3.470588058544221px"/><path d="M 4.007 1.424 C 4.508 1.424 4.897 1.536 5.231 1.759 C 5.565 1.983 5.787 2.29 5.927 2.709 L 7.735 2.709 C 7.513 1.843 7.067 1.173 6.427 0.698 C 5.787 0.223 4.981 0 3.979 0 C 2.977 0 2.504 0.168 1.92 0.531 C 1.308 0.866 0.863 1.368 0.501 2.011 C 0.167 2.653 0 3.379 0 4.217 C 0 5.055 0.167 5.809 0.501 6.423 C 0.835 7.066 1.308 7.568 1.92 7.903 C 2.532 8.267 3.2 8.434 3.979 8.434 C 4.758 8.434 5.76 8.183 6.4 7.708 C 7.04 7.205 7.485 6.563 7.735 5.725 L 5.927 5.725 C 5.62 6.591 4.981 7.01 3.979 7.01 C 2.977 7.01 2.755 6.758 2.337 6.256 C 1.92 5.753 1.725 5.083 1.725 4.189 C 1.725 3.295 1.92 2.625 2.337 2.15 C 2.755 1.648 3.283 1.424 3.979 1.424 Z" fill="rgb(255, 255, 255)" height="8.434076339054002px" id="d2DNCsqwq" transform="translate(15.609 3.128)" width="7.735096310284753px"/><path d="M 6.093 0.503 C 5.481 0.168 4.814 0 4.034 0 C 3.255 0 2.532 0.168 1.92 0.531 C 1.308 0.866 0.835 1.368 0.501 2.011 C 0.167 2.653 0 3.379 0 4.217 C 0 5.055 0.167 5.809 0.529 6.423 C 0.89 7.066 1.363 7.568 1.976 7.903 C 2.588 8.267 3.283 8.434 4.062 8.434 C 4.841 8.434 5.815 8.183 6.455 7.708 C 7.095 7.233 7.54 6.619 7.791 5.865 L 5.982 5.865 C 5.62 6.619 4.98 7.01 4.062 7.01 C 3.144 7.01 2.894 6.814 2.476 6.423 C 2.031 6.032 1.809 5.502 1.753 4.859 L 7.958 4.859 C 7.985 4.608 8.013 4.329 8.013 4.022 C 8.013 3.24 7.846 2.541 7.512 1.927 C 7.179 1.313 6.706 0.838 6.121 0.503 Z M 1.725 3.491 C 1.809 2.849 2.059 2.346 2.476 1.983 C 2.894 1.62 3.395 1.424 3.979 1.424 C 4.563 1.424 5.147 1.62 5.593 1.983 C 6.038 2.346 6.26 2.849 6.26 3.491 L 1.753 3.491 Z" fill="rgb(255, 255, 255)" height="8.434076339054002px" id="NZQSgmK3B" transform="translate(24.013 3.128)" width="8.01332295195354px"/><path d="M 5.815 0.391 C 5.314 0.112 4.73 0 4.09 0 C 3.45 0 3.144 0.084 2.727 0.279 C 2.309 0.475 1.948 0.726 1.669 1.061 L 1.669 0.14 L 0 0.14 L 0 8.294 L 1.669 8.294 L 1.669 3.742 C 1.669 3.016 1.864 2.458 2.226 2.067 C 2.587 1.676 3.088 1.48 3.729 1.48 C 4.368 1.48 4.841 1.676 5.231 2.067 C 5.592 2.458 5.787 3.016 5.787 3.742 L 5.787 8.294 L 7.457 8.294 L 7.457 3.491 C 7.457 2.737 7.318 2.122 7.04 1.592 C 6.761 1.061 6.344 0.67 5.843 0.419 Z" fill="rgb(255, 255, 255)" height="8.294435702953454px" id="y4C5qOSA0" transform="translate(33.166 3.128)" width="7.4567073744807075px"/><path d="M 2.643 0 L 0.946 0 L 0.946 2.039 L 0 2.039 L 0 3.407 L 0.946 3.407 L 0.946 7.931 C 0.946 8.741 1.141 9.3 1.558 9.663 C 1.948 10.026 2.532 10.193 3.311 10.193 L 4.647 10.193 L 4.647 8.797 L 3.617 8.797 C 3.283 8.797 3.033 8.741 2.894 8.602 C 2.755 8.462 2.671 8.239 2.671 7.931 L 2.671 3.407 L 4.647 3.407 L 4.647 2.039 L 2.671 2.039 L 2.671 0 Z" fill="rgb(255, 255, 255)" height="10.193494685087567px" id="HConpA1k2" transform="translate(41.458 1.229)" width="4.646524285330045px"/><path d="M 1.085 0 C 0.779 0 0.528 0.112 0.306 0.307 C 0.111 0.503 0 0.782 0 1.089 C 0 1.396 0.111 1.648 0.306 1.871 C 0.501 2.095 0.779 2.178 1.085 2.178 C 1.391 2.178 1.642 2.067 1.836 1.871 C 2.031 1.676 2.142 1.396 2.142 1.089 C 2.142 0.782 2.031 0.531 1.836 0.307 C 1.642 0.112 1.391 0 1.085 0 Z" fill="rgb(255, 255, 255)" height="2.1783368721996954px" id="EuoH5GVjb" transform="translate(47.384 0)" width="2.1424521645366994px"/><path d="M 1.669 0 L 0 0 L 0 8.155 L 1.669 8.155 Z" fill="rgb(255, 255, 255)" height="8.154788300335934px" id="zg308Vtf3" transform="translate(47.607 3.267)" width="1.6694896185170123px"/><path d="M 1.085 0 C 0.779 0 0.529 0.112 0.306 0.307 C 0.111 0.503 0 0.782 0 1.089 C 0 1.396 0.111 1.648 0.306 1.871 C 0.501 2.067 0.779 2.178 1.085 2.178 C 1.391 2.178 1.642 2.067 1.836 1.871 C 2.031 1.676 2.142 1.396 2.142 1.089 C 2.142 0.782 2.031 0.531 1.836 0.307 C 1.642 0.112 1.391 0 1.085 0 Z" fill="rgb(255, 255, 255)" height="2.1783368721996954px" id="SaMxhxR35" transform="translate(55.842 0)" width="2.1424521645367136px"/><path d="M 4.007 7.01 C 3.311 7.01 2.783 6.758 2.365 6.256 C 1.948 5.753 1.753 5.083 1.753 4.189 C 1.753 3.295 1.948 2.625 2.365 2.15 C 2.783 1.648 3.311 1.424 4.007 1.424 C 4.702 1.424 4.897 1.536 5.231 1.759 C 5.565 1.983 5.788 2.29 5.926 2.709 L 7.735 2.709 C 7.513 1.843 7.067 1.173 6.427 0.698 C 5.788 0.223 4.981 0 3.979 0 C 2.977 0 2.504 0.168 1.92 0.531 C 1.308 0.866 0.862 1.368 0.501 2.011 C 0.167 2.653 0 3.379 0 4.217 C 0 5.055 0.167 5.809 0.501 6.423 C 0.835 7.066 1.308 7.568 1.92 7.903 C 2.532 8.267 3.2 8.434 3.979 8.434 C 4.758 8.434 5.76 8.183 6.4 7.708 C 7.04 7.205 7.485 6.563 7.735 5.725 L 5.926 5.725 C 5.621 6.591 4.981 7.01 3.979 7.01 Z" fill="rgb(255, 255, 255)" height="8.434076339054002px" id="HetImuuV4" transform="translate(59.265 3.128)" width="7.735226155870485px"/><path d="M 6.872 3.268 L 2.643 3.268 L 2.643 2.681 C 2.643 2.206 2.754 1.871 2.949 1.676 C 3.144 1.508 3.478 1.396 3.923 1.396 L 3.923 0 C 2.921 0 2.17 0.223 1.697 0.642 C 1.196 1.061 0.946 1.759 0.946 2.681 L 0.946 3.268 L 0 3.268 L 0 4.636 L 0.946 4.636 L 0.946 11.422 L 2.643 11.422 L 2.643 4.636 L 5.203 4.636 L 5.203 11.422 L 6.872 11.422 Z" fill="rgb(255, 255, 255)" height="11.422308710362525px" id="Xc4NeSznw" transform="translate(50.863 0)" width="6.872402238697973px"/></g></svg>)
Internationalization
Internationalization
Internationalization
Teach models to operate
Teach models to operate
Teach models to operate
across languages and cultures
across languages and cultures
across languages and cultures
Models trained primarily on English data exhibit biases and gaps when handling other languages, dialects, and cultural contexts. Internationalization ensures models understand linguistic nuance, cultural norms, and real usage patterns across global populations.
The hidden infrastructure behind world-class AI models
The hidden infrastructure behind world-class AI models
The hidden infrastructure behind world-class AI models
Overview
AI performance depends on linguistic context
AI performance depends on linguistic context
Centific helps organizations adapt models for real-world markets using AI-assisted workflows, human feedback, and scalable platforms for data curation, model tuning, and evaluation, delivering accurate, culturally aligned experiences across languages and regions.
Train With Region-Native Data Distributions
Models learn from data that reflects how users actually speak, write, and behave in each market. Training on native, in-region datasets improves accuracy, reduces bias, and prevents performance drops when models are deployed globally.
Train With Region-Native Data Distributions
Models learn from data that reflects how users actually speak, write, and behave in each market. Training on native, in-region datasets improves accuracy, reduces bias, and prevents performance drops when models are deployed globally.
Train With Region-Native Data Distributions
Models learn from data that reflects how users actually speak, write, and behave in each market. Training on native, in-region datasets improves accuracy, reduces bias, and prevents performance drops when models are deployed globally.
Train With Region-Native Data Distributions
Models learn from data that reflects how users actually speak, write, and behave in each market. Training on native, in-region datasets improves accuracy, reduces bias, and prevents performance drops when models are deployed globally.
Reduce Semantic & Behavioral Drift
Model outputs remain aligned with local language patterns, cultural norms, and user expectations, helping responses stay consistent and trustworthy across regions. This alignment reduces misinterpretation, hallucinations, and brand risk.
Reduce Semantic & Behavioral Drift
Model outputs remain aligned with local language patterns, cultural norms, and user expectations, helping responses stay consistent and trustworthy across regions. This alignment reduces misinterpretation, hallucinations, and brand risk.
Reduce Semantic & Behavioral Drift
Model outputs remain aligned with local language patterns, cultural norms, and user expectations, helping responses stay consistent and trustworthy across regions. This alignment reduces misinterpretation, hallucinations, and brand risk.
Reduce Semantic & Behavioral Drift
Model outputs remain aligned with local language patterns, cultural norms, and user expectations, helping responses stay consistent and trustworthy across regions. This alignment reduces misinterpretation, hallucinations, and brand risk.
Enforce Regional Compliance Constraints
Data privacy, sovereignty, and regulatory requirements are met by embedding region-specific controls into training and evaluation workflows, enabling confident deployment in regulated markets.
Enforce Regional Compliance Constraints
Data privacy, sovereignty, and regulatory requirements are met by embedding region-specific controls into training and evaluation workflows, enabling confident deployment in regulated markets.
Enforce Regional Compliance Constraints
Data privacy, sovereignty, and regulatory requirements are met by embedding region-specific controls into training and evaluation workflows, enabling confident deployment in regulated markets.
Enforce Regional Compliance Constraints
Data privacy, sovereignty, and regulatory requirements are met by embedding region-specific controls into training and evaluation workflows, enabling confident deployment in regulated markets.
Validate Performance In-Language
Models are evaluated by native speakers and domain experts to identify quality gaps, bias, and edge cases before users encounter them. This validation improves reliability and reduces costly post-launch fixes.
Validate Performance In-Language
Models are evaluated by native speakers and domain experts to identify quality gaps, bias, and edge cases before users encounter them. This validation improves reliability and reduces costly post-launch fixes.
Validate Performance In-Language
Models are evaluated by native speakers and domain experts to identify quality gaps, bias, and edge cases before users encounter them. This validation improves reliability and reduces costly post-launch fixes.
Validate Performance In-Language
Models are evaluated by native speakers and domain experts to identify quality gaps, bias, and edge cases before users encounter them. This validation improves reliability and reduces costly post-launch fixes.
Increase Coverage of Regional Edge Cases
Training datasets are expanded to include dialects, slang, code-switching, and region-specific scenarios that generic datasets miss, strengthening robustness in real-world environments.
Increase Coverage of Regional Edge Cases
Training datasets are expanded to include dialects, slang, code-switching, and region-specific scenarios that generic datasets miss, strengthening robustness in real-world environments.
Increase Coverage of Regional Edge Cases
Training datasets are expanded to include dialects, slang, code-switching, and region-specific scenarios that generic datasets miss, strengthening robustness in real-world environments.
Increase Coverage of Regional Edge Cases
Training datasets are expanded to include dialects, slang, code-switching, and region-specific scenarios that generic datasets miss, strengthening robustness in real-world environments.
Close the Localization Feedback Loop
Regional user feedback and performance data are continuously collected to retrain and fine-tune models over time, allowing global performance to improve with each market rollout.
Close the Localization Feedback Loop
Regional user feedback and performance data are continuously collected to retrain and fine-tune models over time, allowing global performance to improve with each market rollout.
Close the Localization Feedback Loop
Regional user feedback and performance data are continuously collected to retrain and fine-tune models over time, allowing global performance to improve with each market rollout.
Close the Localization Feedback Loop
Regional user feedback and performance data are continuously collected to retrain and fine-tune models over time, allowing global performance to improve with each market rollout.
Product Features
Product Features
Product Features
Product Features
Language Is Infrastructure
Language Is Infrastructure
Global is the Baseline
Global is the Baseline
Rapid Global Workforce Scaling for AI Programs
Quickly onboard, train, and certify thousands of multilingual, domain-specific resources across regions; enabling large-scale AI initiatives to launch and ramp without delays.
Rapid Global Workforce Scaling for AI Programs
Quickly onboard, train, and certify thousands of multilingual, domain-specific resources across regions; enabling large-scale AI initiatives to launch and ramp without delays.
High-Quality Multilingual Data at Scale
Centific provides deep AI consulting expertise to design, execute and automate robust annotation frameworks, quality rubrics, and evaluation standards; delivering high-volume, high-accuracy multilingual data for LLMs and real-world AI applications.
High-Quality Multilingual Data at Scale
Centific provides deep AI consulting expertise to design, execute and automate robust annotation frameworks, quality rubrics, and evaluation standards; delivering high-volume, high-accuracy multilingual data for LLMs and real-world AI applications.
Model Enablement for Real-World Applications
Support LLM and SLM development with supervised fine-tuning, RLHF, prompt authoring, and multi-turn conversation design; driving production-ready AI experiences for customers.
Model Enablement for Real-World Applications
Support LLM and SLM development with supervised fine-tuning, RLHF, prompt authoring, and multi-turn conversation design; driving production-ready AI experiences for customers.
Centific Ecosystem
The Complete AI Stack
Built to advance, deploy, and govern intelligence
Build & Train AI
Platforms
Verticals
Centific Ecosystem
The Complete AI Stack
Built to advance, deploy, and govern intelligence
Build & Train AI
Platforms
Verticals
Centific Ecosystem
The Complete AI Stack
Built to advance, deploy, and govern intelligence
Build & Train AI
Platforms
Verticals
Centific Ecosystem
The Complete AI Stack
Built to advance, deploy, and govern intelligence
Build & Train AI
Platforms
Verticals
Blog
Research, insights, and updates
from the front lines of AI.
From applied research to real-world deployments, explore how Centific advances AI through data, evaluation, and expert-led execution.
Research, insights, and updates
from the front lines of AI.
From applied research to real-world deployments, explore how Centific advances AI through data, evaluation, and expert-led execution.
Research, insights, and updates
from the front lines of AI.
From applied research to real-world deployments, explore how Centific advances AI through data, evaluation, and expert-led execution.
Customer Stories
Proven results
with leading AI teams.
See how organizations use Centific’s data and expert services to build, deploy, and scale production-ready AI.
Connect with Centific
Stay ahead of what’s next
Stay ahead
Updates from the frontier of AI data.
Receive updates on platform improvements, new workflows, evaluation capabilities, data quality enhancements, and best practices for enterprise AI teams.