TIME SERIES AI
Operational Improvement Data Platform.
Go from manual monitoring and data science to automated decision-making in your mission-critical systems.
Energy, Manufacturing, Vehicles, Facilities and Software.
%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)
Setting static limits on metrics or their statistics will give us enough warning to stop failures.
Are you drowning in a "tsunami of alerts" while still getting blindsided by damaging breakdowns?
Conventional observability and monitoring tools are costly, noisy, and miss too many things. They rely on static thresholds that treat every brief signal spike as an alarm, causing alert fatigue and blinding operators to subtle, emerging anomalies. Falkonry eliminates this noise. Our platform uses an advanced rules engine that applies spatial and temporal denoising to AI outputs, ensuring you only receive high-confidence alerts when a condition is persistent and correlated across multiple signals.
Our own team of data scientists can build custom ML models for all our systems and maintain them.
Are you waiting months for manual data science projects to deliver insights that your engineers need today?
Conventional approaches rely on individual use cases and data science projects, which makes it nearly impossible to scale across the sheer volume of signals (often tens of thousands) in a modern plant. Falkonry removes the data wrangling and democratizes analytics. With powerful no-code/low-code tools, we empower your process and reliability engineers to automatically discover multivariate patterns and anomalies without writing a single line of code or requiring a large team of data scientists.
A global, digital transformation project will prescribe actions by connecting all our data silos.
Did you invest in a years-long infrastructure overhaul that looks great in the lab but fails silently at the edge?
Validating analytics in the lab takes too long, and raw data often reaches the cloud stripped of its vital context. You don't need a "Big AI" overhaul to get results. Falkonry is a deploy-ready, sensor-agnostic platform that works with your current telemetry. Our Edge-to-Cloud architecture ingests, contextualizes, and analyzes data directly at the source - even in disconnected or tactical environments - delivering proven, regulatory-grade intelligence in weeks, not years.
Falkonry is the most complete and intuitive platform for turning IT, OT and IoT sensor and telemetry data into operational autonomy.
No data wrangling required. Instantly explore live or historical data from operations, 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
Democratize analytics with powerful no-code/low-code tools, enabling engineering teams to understand as well as improve their systems and operations.
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.
