AI-Native Analysis to Explain and Predict Real-Time System Behavior
Used by design and operations engineers to reduce noisy alerts, discover root cause issues and optimize system performance with AI that understands time series data.
Used by design and operations engineers to reduce noisy alerts, discover root cause issues and optimize system performance with AI that understands time series data.
%20--%3e%3csvg%20version='1.1'%20id='layer'%20xmlns='http://www.w3.org/2000/svg'%20xmlns:xlink='http://www.w3.org/1999/xlink'%20x='0px'%20y='0px'%20viewBox='-153%20-46%20652%20652'%20style='enable-background:new%20-153%20-46%20652%20652;'%20xml:space='preserve'%3e%3cstyle%20type='text/css'%3e%20.st0{fill:%23003167;}%20.st1{fill:%23BD2E2B;}%20%3c/style%3e%3cpath%20class='st0'%20d='M-19.3,271.2c-12.5-4.8-16.1-8-16.1-13.3c0-4,3.7-8,11.4-8c11.1,0,18.5,8,18.5,8L6,245.5%20c0,0-10.9-11.5-30.2-11.5c-18.8,0-31,10.8-31,25.4c0,12.9,8.9,20.8,21.3,25.7c15.9,6.4,20.3,10.5,20.3,16.2c0,5-4.6,8.8-11.4,8.8%20c-13.8,0-23.6-10-23.6-10l-11.5,12.4c0,0,11.7,13.4,34.1,13.4c19.5,0,32.3-11.9,32.3-26.8C6.3,284.6-5.9,276.4-19.3,271.2z%20M21.4,257.6h21.7V318h17.7v-60.4h21.7V242H21.4V257.6z%20M115.7,285.7h29.6v-13.3h-29.6v-12.3h34.4V246H99.6v68H151v-14h-35.3V285.7z%20M187.7,246h-16v68h48.5v-14.4h-32.5V246z%20M287.3,294.4c-0.1,0.1-5.8,8.3-17.4,8.3c-12.6,0-22.2-9.8-22.2-22.7%20c0-12.9,9.5-22.6,22.2-22.6c11,0,16.3,7.1,16.4,7.2l0.5,0.6l13-11.4l-0.4-0.5c-0.4-0.5-9.4-12.3-29.2-12.3%20c-22.5,0-40.1,17.3-40.1,39.4c0,22.1,17,38.8,39.6,38.8c20.7,0,30.3-13.5,30.4-13.6l0.4-0.5l-12.6-11.2L287.3,294.4z%20M360.5,234.1%20c-26.7,0-47.2,20.5-47.2,46.3c0,25.8,19.9,45.6,46.6,45.6c26.7,0,47.2-20.4,47.2-46.2S387.2,234.1,360.5,234.1z%20M360.3,308.1%20c-15.8,0-27.4-12.1-27.4-28.1c0-15.8,11.6-27.9,27.4-27.9c15.7,0,27.4,12.1,27.4,27.9C387.7,296,376,308.1,360.3,308.1z%20M418.5,155.5l-8.4,1.8C251.2,190.3,90.2,190-68.5,156.2l-3-0.6l-55,33v183l55,33l7.1-1.5c61.5-12.9,123.9-20.8,186.7-23.6%20c-0.5-2.8-0.7-5.6-0.7-8.4c0-2.4,0.2-4.9,0.5-7.2c-63.8,2.8-127.2,10.8-189.7,23.9l-1.1,0.2l-42.2-25.3V197.3l42.2-25.3%20c159.9,33.6,322,33.8,482,0.5l2.5-0.5l42.2,25.3v165.3L415.7,388c-62.8-13.2-126.7-21.3-190.8-24.2c0.3,2.4,0.5,4.8,0.5,7.2%20c0,2.9-0.2,5.6-0.7,8.4c63.7,2.9,127,10.9,189.4,24.2l4.4,0.9l55-33v-183L418.5,155.5z'/%3e%3cpath%20class='st1'%20d='M172.1,403.2c0.2-5,0.5-10,0.7-15.1c0.1-1.8-0.5-2.3-2.4-2c-4.5,0.7-9,1.3-13.8,1.9c0.3-1.2,0.6-2.2,1-3.1%20c0.7-1.6,0.3-2.8-1.1-3.9c-4.6-3.6-9.1-7.3-13.6-11c-0.2-0.1-0.3-0.3-0.6-0.5c1-0.5,1.9-1,2.8-1.4c1-0.4,1.3-1.1,1-2.2%20c-0.9-3-1.7-6.1-2.6-9.3c2.3,0.5,4.5,0.9,6.7,1.3c3.2,0.7,3.2,0.7,4.1-2.5l0.5-1.9c1.8,1.9,3.4,3.7,5,5.5c0.5,0.6,1,1.3,1.6,1.8%20c0.6,0.5,1.4,0.7,2.1,1c0.2-0.8,0.7-1.6,0.6-2.3c-0.4-2.7-1-5.4-1.5-8.1c-0.5-2.4-0.9-4.8-1.4-7.5c1.4,0.7,2.7,1.3,3.9,2%20c1.7,0.8,2.2,0.6,3.1-1c0.9-1.5,1.7-3.1,2.5-4.6l2.6-4.9c1.2,2.2,2.3,4.2,3.3,6.1c0.7,1.2,1.3,2.5,2,3.7c0.6,1.1,1.5,1.4,2.6,0.8%20l4.2-2.1c-0.6,3-1.1,5.9-1.6,8.8c-0.4,2.3-1,4.6-1.3,6.9c-0.1,0.7,0.4,1.5,0.7,2.3c0.7-0.3,1.5-0.5,2-1c2.2-2.3,4.3-4.8,6.6-7.4%20c0.3,1.1,0.6,1.9,0.9,2.8c0.6,1.9,0.9,2.2,2.9,1.8c2.1-0.4,4.2-0.9,6.3-1.3c0.4-0.1,0.8-0.1,1.3-0.2c-0.8,3.1-1.5,6-2.4,8.9%20c-0.4,1.4-0.2,2.2,1.2,2.7c0.8,0.3,1.5,0.7,2.5,1.2c-2.4,2-4.8,3.9-7.1,5.8c-2.4,1.9-4.7,3.9-7.2,5.8c-1.4,1-1.7,2.2-1.1,3.8%20c0.4,1,0.6,2,1,3.2l-11.2-1.6c-0.8-0.1-1.7-0.2-2.5-0.3c-1.9-0.3-2.4,0.1-2.4,2c0.1,5,0.2,10.1,0.3,15.1%20C174.5,403.2,172.1,403.2,172.1,403.2z'/%3e%3c/svg%3e)
%20--%3e%3csvg%20xmlns:dc='http://purl.org/dc/elements/1.1/'%20xmlns:cc='http://creativecommons.org/ns%23'%20xmlns:rdf='http://www.w3.org/1999/02/22-rdf-syntax-ns%23'%20xmlns:svg='http://www.w3.org/2000/svg'%20xmlns='http://www.w3.org/2000/svg'%20version='1.1'%20width='892.91339'%20height='291.25983'%20id='svg14516'%20xml:space='preserve'%3e%3cmetadata%20id='metadata14522'%3e%3crdf:RDF%3e%3ccc:Work%20rdf:about=''%3e%3cdc:format%3eimage/svg+xml%3c/dc:format%3e%3cdc:type%20rdf:resource='http://purl.org/dc/dcmitype/StillImage'%20/%3e%3cdc:title%3e%3c/dc:title%3e%3c/cc:Work%3e%3c/rdf:RDF%3e%3c/metadata%3e%3cdefs%20id='defs14520'%3e%3cclipPath%20id='clipPath14582'%3e%3cpath%20d='m%200,0%20595.276,0%200,841.89%20L%200,841.89%200,0%20z'%20id='path14584'%20/%3e%3c/clipPath%3e%3c/defs%3e%3cg%20transform='matrix(1.25,0,0,-1.25,58.550107,781.84886)'%20id='g14524'%3e%3cg%20transform='matrix(5.9268435,0,0,5.9268435,-270.54836,-4163.4738)'%20id='g14598'%3e%3cg%20transform='translate(63.1102,791.149)'%20id='g14574'%3e%3cpath%20d='m%200,0%20-24.887,-7.512%200,-14.456%2034.482,0%200,10.029'%20id='path14576'%20style='fill:%23f25c29;fill-opacity:1;fill-rule:nonzero;stroke:none'%20/%3e%3c/g%3e%3cg%20id='g14578'%3e%3cg%20clip-path='url(%23clipPath14582)'%20id='g14580'%3e%3cg%20transform='translate(75.6283,769.1807)'%20id='g14586'%3e%3cpath%20d='m%200,0%2082.163,0.003%200,38.343%20L%200,38.339%200,0%20z%20m%2046.382,6.322%20-2,0%200,12.299%202,0%200,-12.299%20z%20m%2016.801,12.3%200,-1.54%200.04,0%20c%200.599,1.318%201.644,1.782%203.082,1.782%201.121,0%202.415,-0.464%202.851,-1.584%200.688,1.318%201.811,1.584%202.932,1.584%201.298,0%203.369,-0.291%203.369,-3.192%20l%200,-9.35%20-2.002,0%200,8.519%20c%200,1.248%20-0.338,2.386%20-1.948,2.386%20-0.754,0%20-0.983,-0.02%20-1.515,-0.457%20-0.583,-0.491%20-0.679,-1.713%20-0.679,-2.179%20l%200,-8.269%20-1.984,0%200,8.519%20c%200,1.248%20-0.34,2.386%20-1.943,2.386%20-0.619,0%20-1.45,-0.36%20-1.736,-0.827%20-0.372,-0.611%20-0.467,-1.343%20-0.467,-1.809%20l%200,-8.269%20-2.002,0%200,9.738%20c%200,0.859%20-0.045,1.706%20-0.12,2.562%20l%202.122,0%20z%20m%20-13.509,0%201.97,0%200,-8.542%20c%200,-1.463%200.685,-2.466%202.11,-2.466%201.074,0%202.403,0.797%202.403,2.835%20l%200,8.173%202.009,0%200,-9.735%20c%200,-0.865%200.043,-1.717%200.123,-2.565%20l%20-2.132,0%200,1.49%20-0.073,0%20C%2055.433,6.685%2054.249,6.073%2052.97,6.073%20c%20-2.157,0%20-3.296,1.074%20-3.296,3.174%20l%200,9.375%20z%20m%20-5.418,5.283%202.242,0%200,-2.135%20-2.242,0%200,2.135%20z%20m%20-9.673,-5.283%200,-1.585%200.102,0%20c%200.782,1.38%201.709,1.827%203.298,1.827%202.64,0%203.306,-1.584%203.306,-4.023%20l%200,-8.519%20-2.007,0%200,8.519%20c%200,1.321%20-0.47,2.386%20-2.173,2.386%20-2.205,0%20-2.526,-1.946%20-2.526,-3.632%20l%200,-7.273%20-1.987,0%200,9.738%20c%200,0.859%20-0.048,1.706%20-0.11,2.562%20l%202.097,0%20z%20m%20-9.649,0%202.005,0%200,-1.852%200.048,0%20c%200.827,1.871%201.857,2.094%203.812,2.094%20l%200,-2.072%20c%20-0.171,0.024%20-0.37,0.052%20-0.547,0.075%20-0.174,0.021%20-0.334,0.052%20-0.532,0.052%20-2.227,0%20-2.781,-1.683%20-2.781,-3.351%20l%200,-7.246%20-2.005,0%200,12.3%20z%20m%20-7.046,0.242%20c%203.508,0%204.387,-1.926%204.387,-6.003%20l%200,-1.049%20-6.912,0%20c%200,-3.422%200.927,-4.101%202.474,-4.101%201.372,0%202.078,1.1%202.151,2.318%20l%202.195,0%20C%2022.154,7.321%2020.471,6.073%2017.888,6.073%20c%20-2.598,0%20-4.669,0.784%20-4.669,6.199%200,3.615%200.415,6.592%204.669,6.592%20m%20-13.961,5.041%2010.592,0%200,-1.943%20-4.186,0%200,-15.64%20-2.226,0%200,15.64%20-4.18,0%200,1.943%20z'%20id='path14588'%20style='fill:%237a7a80;fill-opacity:1;fill-rule:nonzero;stroke:none'%20/%3e%3c/g%3e%3cg%20transform='translate(93.3265,786.5298)'%20id='g14590'%3e%3cpath%20d='m%200,0%20c%20-1.268,0%20-2.314,-0.602%20-2.335,-4.026%20l%204.795,0%20C%202.46,-0.43%201.458,0%200,0'%20id='path14592'%20style='fill:%237a7a80;fill-opacity:1;fill-rule:nonzero;stroke:none'%20/%3e%3c/g%3e%3c/g%3e%3c/g%3e%3cg%20transform='translate(38.2235,786.148)'%20id='g14594'%3e%3cpath%20d='m%200,0%2013.145,13.739%2021.337,-12.03%200,19.659%20-34.482,0'%20id='path14596'%20style='fill:%23faad42;fill-opacity:1;fill-rule:nonzero;stroke:none'%20/%3e%3c/g%3e%3c/g%3e%3c/g%3e%3c/svg%3e)
'%20id='g4'%20style='fill:%23373535'%3e%3cpath%20d='m%20229.92,393.33%20v%2015.356%20C%20212.191,396.12%20169.139,393.33%20169.139,393.33%20v%2047.34%20h%2015.16%20v%20-32.34%20c%2027.683,5.43%2040.13,12.243%2045.625,17.786%20v%2014.558%20h%2016.25%20v%20-47.34%20h%20-16.25'%20id='path6'%20/%3e%3cpath%20d='m%20337.78,437.44%20v%20-43.59%20h%20-15.16%20v%2033.58%20c%200,0%20-14.584,1.368%20-24.06,1.368%20-9.48,0%20-22.5,-1.368%20-22.5,-1.368%20V%20393.85%20H%20260.9%20v%2043.59%20c%200,0%2018.594,3.441%2037.656,3.441%2019.06,0%2039.22,-3.441%2039.22,-3.441'%20id='path8'%20/%3e%3cpath%20d='m%20354.35,437.39%20c%200,0%204.635,3.281%2037.656,3.281%2033.02,0%2037.918,-3.334%2037.918,-3.334%20V%20424.63%20c%200,0%20-13.13,3.75%20-33.543,3.75%20-20.416,0%20-26.875,-1.98%20-26.875,-1.98%20v%20-18.12%20c%200,0%206.459,-1.98%2026.875,-1.98%2020.416,0%2033.543,3.75%2033.543,3.75%20v%20-12.707%20c%200,0%20-4.896,-3.334%20-37.918,-3.334%20-33.02,0%20-37.656,3.281%20-37.656,3.281%20v%2040.1'%20id='path10'%20/%3e%3cpath%20d='m%20509.03,427.2%20c%20-5.281,0.652%20-11.874,1.174%20-19.48,1.174%20-20.416,0%20-26.875,-1.98%20-26.875,-1.98%20v%20-18.12%20c%200,0%206.459,-1.98%2026.875,-1.98%207.606,0%2014.199,0.521%2019.48,1.174%20v%2019.736%20m%20-23.855,-33.2%20c%20-33.02,0%20-37.656,3.281%20-37.656,3.281%20v%2040.1%20c%200,0%204.635,3.281%2037.656,3.281%2033.02,0%2037.918,-3.334%2037.918,-3.334%20v%20-39.999%20c%200,0%20-4.896,-3.334%20-37.918,-3.334'%20id='path12'%20/%3e%3cpath%20d='m%20575.7,394.11%20c%20-33.659,0%20-38.37,2.781%20-38.37,2.781%20v%2042.531%20h%2014.06%20v%20-13.469%20c%204.715,-0.456%2015.16,-1.059%2025.531,1.031%2013.959,2.813%2021.344,7.437%2021.344,7.437%20v%206.25%20h%2015.938%20v%20-11.406%20l%20-15.438,-7.688%20c%2013.13,-0.826%2015.594,-2.219%2015.594,-2.219%20V%20396.92%20c%200,0%20-4.997,-2.813%20-38.656,-2.813%20m%200.375,10.375%20c%207.753,0%2017.429,0.447%2022.813,1%20v%208.406%20c%20-5.384,0.551%20-15.06,1%20-22.813,1%20-20.81,0%20-23.313,-1.688%20-23.313,-1.688%20v%20-7.03%20c%200,0%202.503,-1.688%2023.313,-1.688'%20id='path14'%20/%3e%3c/g%3e%3c/svg%3e)
500 errors?
Modern observability tools are great but they are costly, noisy and not intuitive. Even worse, they regurgitate data without highlighting what you want to know.
Falkonry is the most intuitive and resource efficient intelligence system for time series data whether from observability, IoT or automation.
No data wrangling or down sampling required. Instantly explore live or historical data from operations, devops, testing and simulation sources.
Leverages a private, deep neural network to economically learn multi-timescale embeddings of time series data of any domain or origin
Falkonry democratizes analytics with powerful no-code tools, enabling engineering teams to understand, investigate and improve their systems.
Trusted by the world's leading engineering teams.
Take away drudgery and toil from exploiting real-time data from sensors and telemetry. Rapidly figure out what's going on.
