[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"1bf1j0q":3,"wuvcct":73},{"common":4,"navPages":22,"productGroups":33,"seo":66,"strategies":72},{"_id":5,"_type":5,"cta_label":6,"cta_label_impact":7,"email":8,"socials":9},"settings_common","let’s talk","read impact studies",null,[10,14,18],{"_key":11,"label":12,"type":12,"url":13},"668f9a890fd5","linkedin","https:\u002F\u002Fwww.linkedin.com\u002Fcompany\u002Fsphere-energy\u002F",{"_key":15,"label":16,"type":16,"url":17},"5a142892073b","medium","https:\u002F\u002Fmedium.com\u002F@lutz.lukas88",{"_key":19,"label":20,"type":20,"url":21},"f97a157c8bf7","youtube","https:\u002F\u002Fwww.youtube.com\u002F@SphereEnergy",[23,27,30],{"_id":24,"slug":25,"title":26},"8b8dc55d-3fef-48a3-8152-48bcedea3bf6","studies ","impact studies",{"_id":28,"slug":29,"title":29},"15dbbb59-c8d8-46d2-bd7c-722ffe763ccf","about",{"_id":31,"slug":32,"title":32},"d529968f-1fdb-40a0-8c9d-f6e1ff64ef9f","vetta",[34,50],{"_id":35,"products":36,"title":49},"865c3cc9-1bd1-4eb7-9b45-997456c72b35",[37,41,45],{"_id":38,"slug":39,"title":40},"c02a84c3-1cd4-4ccf-881d-ec8ebd92a8b5","battery-life-prediction","Battery life prediction",{"_id":42,"slug":43,"title":44},"ff6446f7-e857-4ac5-9361-ef70a82e4e92","soh-estimation","SOH Estimation",{"_id":46,"slug":47,"title":48},"c1e2a74c-c4e9-4b06-80cb-19c7a9a12867","parametrisation","Parametrisation","Battery AI",{"_id":51,"products":52,"title":65},"f62d95db-3849-497c-aced-e4224dc82287",[53,57,61],{"_id":54,"slug":55,"title":56},"26365138-af07-43a8-b1f5-8b8539034357","requirements-processing","Requirements processing",{"_id":58,"slug":59,"title":60},"8a92b84d-7114-43dd-97fe-7965c6bfcaac","cad-intelligence","CAD Intelligence",{"_id":62,"slug":63,"title":64},"d5321be1-6a2e-4414-8196-88e25567ce30","crash-simulation","Crash Simulation","Engineering AI",{"description":67,"og_description":68,"og_image":69,"og_title":70,"title":71},"Sphere builds Vetta, an AI operating system for physical product development. It unifies engineering data, orchestrates AI models and supports workflows across the entire product lifecycle.","Vetta is the AI operating system for physical product development, helping engineering teams connect data, models and workflows across the full development lifecycle.","https:\u002F\u002Fcdn.sanity.io\u002Fimages\u002Fowk14ohd\u002Fproduction\u002Fa750d9adc1a676399bfcb13d2eee377f5583c9de-1200x630.jpg","Sphere | AI Operating System for Physical Product Development","Sphere Intelligence",[],{"_id":38,"_type":74,"blocks":75,"seo":8,"slug":39,"title":40},"product",[76,129,143,180,274,313,429,488],{"_key":77,"_type":78,"active":79,"description":80,"image":100,"keypoints":110,"title":128},"yeduwx4nc4","productIntroBlock",true,[81,92],{"_key":82,"_type":83,"children":84,"markDefs":90,"style":91},"a9669420374b","block",[85],{"_key":86,"_type":87,"marks":88,"text":89},"830aeec4f640","span",[],"Battery validation takes 12–18 months and costs millions annually.",[],"normal",{"_key":93,"_type":83,"children":94,"markDefs":99,"style":91},"b903a040699d",[95],{"_key":96,"_type":87,"marks":97,"text":98},"767e64e47f6c",[],"[vetta] replaces physical testing with AI-driven simulation — trained on your cell chemistry, deployed inside your infrastructure.",[],{"alt":101,"image":102,"imageMobile":107},"Battery lifetime prediction",{"_type":103,"asset":104},"image",{"_ref":105,"_type":106},"image-2d381d28722059683ebdb754e4ea9455d66a2ea6-1230x1310-jpg","reference",{"_type":103,"asset":108},{"_ref":109,"_type":106},"image-df1bf5de87b03f863f45007ec9db8b8990ef7754-686x406-jpg",[111,116,120,124],{"_key":112,"_type":113,"keypoint":114,"title":115},"OErsZqS0YWnh","keypoint","4,000+","Real-life tests",{"_key":117,"_type":113,"keypoint":118,"title":119},"nmMdgYUyTpsw",">10M","Synthetic material configurations",{"_key":121,"_type":113,"keypoint":122,"title":123},"gLSdhrMCpUXL","\u003C2%","Simulation error",{"_key":125,"_type":113,"keypoint":126,"title":127},"x6SpaU7LlqNw","30%","Test reduction","From months of testing to weeks of simulation",{"_key":130,"_type":131,"active":79,"description":132,"subtitle":141,"title":142},"lo0jifa0s5","productHeroBlock",[133],{"_key":134,"_type":83,"children":135,"markDefs":140,"style":91},"gw1k3e77jw40",[136],{"_key":137,"_type":87,"marks":138,"text":139},"f2vky0ac9tt0",[],"Teams test across temperatures, C-rates, SOC windows and usage profiles and wait. By the time the data arrives, pack geometry, cooling architecture and BMS software decisions have already been made. Millions committed downstream.",[],"This is not an engineering failure. It is a process failure.","Your best engineers are waiting on data that doesn't exist yet",{"_key":144,"_type":145,"active":79,"description":146,"tagLabel":32,"title":155,"titleMobile":164},"gu8wekf9mr","productPatternBlock",[147],{"_key":148,"_type":83,"children":149,"markDefs":154,"style":91},"9nmsdl6cswt0",[150],{"_key":151,"_type":87,"marks":152,"text":153},"qcwqxzm57i00",[],"vetta combines every test ever run, simulation data and new validation results into one continuously improving battery intelligence model. Engineers can predict cell ageing and performance using only the first weeks of cycling data.",[],[156],{"_key":157,"_type":83,"children":158,"markDefs":163,"style":91},"a1ff762e1f0d",[159],{"_key":160,"_type":87,"marks":161,"text":162},"eaf5c836d3b9",[],"One model. All battery knowledge.",[],[165,173],{"_key":166,"_type":83,"children":167,"markDefs":172,"style":91},"85a44c50e8fd",[168],{"_key":169,"_type":87,"marks":170,"text":171},"88a425b9a5fd",[],"One model.",[],{"_key":174,"_type":83,"children":175,"markDefs":179,"style":91},"55a39b091a95",[176],{"_key":169,"_type":87,"marks":177,"text":178},[],"All battery knowledge.",[],{"_key":181,"_type":182,"active":79,"cards":183,"theme":245,"title":246,"titleMobile":263},"mft08nz4h1","productCardsBlock",[184,205,225],{"_key":185,"_type":186,"description":187,"title":196},"m6ZbE2PUMu8B","productCard",[188],{"_key":189,"_type":83,"children":190,"markDefs":195,"style":91},"zehe479zx380",[191],{"_key":192,"_type":87,"marks":193,"text":194},"f5ege0w487h0",[],"Making your legacy proprietary engineering data and toolchain AI-ready is one of the toughest challenges. Most organisations haven't started.",[],[197],{"_key":198,"_type":83,"children":199,"markDefs":204,"style":91},"ofdqocium200",[200],{"_key":201,"_type":87,"marks":202,"text":203},"wnz3wr4b6sk0",[],"pre-training",[],{"_key":206,"_type":186,"description":207,"title":216},"4jIvpeVawQbB",[208],{"_key":209,"_type":83,"children":210,"markDefs":215,"style":91},"np9521x6x6r0",[211],{"_key":212,"_type":87,"marks":213,"text":214},"51nf0dqtlbr0",[],"Engineering complex physical products needs many disciplines. AI doesn't change that — it has to work within it.",[],[217],{"_key":218,"_type":83,"children":219,"markDefs":224,"style":91},"hby1hxffclo0",[220],{"_key":221,"_type":87,"marks":222,"text":223},"laio4k2r2cq0",[],"fine-tuning",[],{"_key":226,"_type":186,"description":227,"title":236},"ILekXpsAqYyg",[228],{"_key":229,"_type":83,"children":230,"markDefs":235,"style":91},"dnxk8oyh9sq0",[231],{"_key":232,"_type":87,"marks":233,"text":234},"p89co734vi00",[],"Your products and customers are unique. Real competitive advantage requires AI built around your specific process.",[],[237],{"_key":238,"_type":83,"children":239,"markDefs":244,"style":91},"vm8755er3le0",[240],{"_key":241,"_type":87,"marks":242,"text":243},"fzc73zrvl9i0",[],"inference",[],"dark",[247,255],{"_key":248,"_type":83,"children":249,"markDefs":254,"style":91},"k75108s12cc0",[250],{"_key":251,"_type":87,"marks":252,"text":253},"cdixhm8seff0",[],"Three steps from raw data",[],{"_key":256,"_type":83,"children":257,"markDefs":262,"style":91},"5eg0xfb35330",[258],{"_key":259,"_type":87,"marks":260,"text":261},"74nirfhf7rg0",[],"to battery intelligence",[],[264,269],{"_key":248,"_type":83,"children":265,"markDefs":268,"style":91},[266],{"_key":251,"_type":87,"marks":267,"text":253},[],[],{"_key":256,"_type":83,"children":270,"markDefs":273,"style":91},[271],{"_key":259,"_type":87,"marks":272,"text":261},[],[],{"_key":275,"_type":276,"active":79,"subtitle":277,"tabs":278,"title":312},"hlrho7q4hn","productTabsBlock","Simulation results",[279,291,302],{"_key":280,"_type":281,"cRate":282,"image":283,"temperature":290},"cJkvaT1z98sy","productTab","0.33C",{"image":284,"imageMobile":287},{"_type":103,"asset":285},{"_ref":286,"_type":106},"image-5560bbd722ef74c2d482b2f6b9bcde5538a9e363-1440x393-svg",{"_type":103,"asset":288},{"_ref":289,"_type":106},"image-bb086757dbd65ab650ec2cd9467faf04fa335372-375x475-svg","40°C",{"_key":292,"_type":281,"cRate":282,"image":293,"temperature":301},"azex6hisO4Ch",{"_type":294,"image":295,"imageMobile":298},"imageAlt",{"_type":103,"asset":296},{"_ref":297,"_type":106},"image-0685eb19eea071bdb97ff59acd2f4b251020dd55-1440x393-svg",{"_type":103,"asset":299},{"_ref":300,"_type":106},"image-2cca61175e2ab6981f297d91a8f456a31978b952-375x475-svg","10°C",{"_key":303,"_type":281,"cRate":304,"image":305,"temperature":301},"4i3rCdsfTd0G","1.66C",{"image":306,"imageMobile":309},{"_type":103,"asset":307},{"_ref":308,"_type":106},"image-fe047001536220603a91262dcf99b33f394352c8-1440x393-svg",{"_type":103,"asset":310},{"_ref":311,"_type":106},"image-bc014aa898cfd4d644371f2fe81fe831f0ca9e36-375x475-svg","Predict aging under various conditions",{"_key":314,"_type":182,"active":79,"cards":315,"subtitle":404,"title":405,"titleMobile":422},"wpz8ak1lbcj",[316,336,356,384],{"_key":317,"_type":186,"description":318,"title":327},"7eE7iiprpiZP",[319],{"_key":320,"_type":83,"children":321,"markDefs":326,"style":91},"r0oh6kart800",[322],{"_key":323,"_type":87,"marks":324,"text":325},"5g95m88md700",[],"Reliable lifetime predictions after weeks of cycling data. No more waiting 12+ months before making design, sourcing or certification decisions.",[],[328],{"_key":329,"_type":83,"children":330,"markDefs":335,"style":91},"f0jtfy014i70",[331],{"_key":332,"_type":87,"marks":333,"text":334},"u6az66cjeg00",[],"faster engineering decisions",[],{"_key":337,"_type":186,"description":338,"title":347},"BkaTQgrHGmWb",[339],{"_key":340,"_type":83,"children":341,"markDefs":346,"style":91},"kxz4wrm30pf0",[342],{"_key":343,"_type":87,"marks":344,"text":345},"etsw40vxitm0",[],"Simulate untested combinations of temperature, C-rate, SOC window and usage profiles. AI identifies which experiments can be skipped — typically 30% fewer physical tests.",[],[348],{"_key":349,"_type":83,"children":350,"markDefs":355,"style":91},"p2hzsx7lnz70",[351],{"_key":352,"_type":87,"marks":353,"text":354},"7gikruc2yae0",[],"reduced test matrix",[],{"_key":357,"_type":186,"description":358,"title":367},"VhQC6mnRhxHK",[359],{"_key":360,"_type":83,"children":361,"markDefs":366,"style":91},"wxjxd9278100",[362],{"_key":363,"_type":87,"marks":364,"text":365},"6kkpr5df3x00",[],"Automatically parametrise Pseudo-2D and Equivalent Circuit Models from initial test data. Production-ready physical models in a fraction of the time. Ready for Simulink, FMU or BMS-ready code.",[],[368,376],{"_key":369,"_type":83,"children":370,"markDefs":375,"style":91},"6m9btug22iu0",[371],{"_key":372,"_type":87,"marks":373,"text":374},"mrmvrs8vjc00",[],"automated",[],{"_key":377,"_type":83,"children":378,"markDefs":383,"style":91},"p1xktwz3nh00",[379],{"_key":380,"_type":87,"marks":381,"text":382},"4l8f1bi9rfg0",[],"P2D & ECM parametrisation",[],{"_key":385,"_type":186,"description":386,"title":395},"EkHtqW9XpbvW",[387],{"_key":388,"_type":83,"children":389,"markDefs":394,"style":91},"98gsg1r4a1p0",[390],{"_key":391,"_type":87,"marks":392,"text":393},"5nqtgudtr7s0",[],"Simulate end-of-life behaviour for new cell candidates before committing to full testing. Pre-screen suppliers and chemistries from minimal early-cycle data.",[],[396],{"_key":397,"_type":83,"children":398,"markDefs":403,"style":91},"95u0nkplesg0",[399],{"_key":400,"_type":87,"marks":401,"text":402},"kkan43la2l00",[],"virtual cell qualification",[],"use cases",[406,414],{"_key":407,"_type":83,"children":408,"markDefs":413,"style":91},"nn6njrf08b00",[409],{"_key":410,"_type":87,"marks":411,"text":412},"sypvo1dlzx00",[],"What your team",[],{"_key":415,"_type":83,"children":416,"markDefs":421,"style":91},"1lx2g3y0wvm0",[417],{"_key":418,"_type":87,"marks":419,"text":420},"wc78dw4n8mg0",[],"can do with it",[],[423],{"_key":407,"_type":83,"children":424,"markDefs":428,"style":91},[425],{"_key":410,"_type":87,"marks":426,"text":427},[],"What your team can do with it",[],{"_key":430,"_type":431,"active":79,"cards":432,"subtitle":470,"tagLabel":32,"title":471},"mxekhglb20h","productDeployBlock",[433,446,458],{"_key":434,"_type":435,"description":436,"title":445},"1FQlj25Z9g17","deployCard",[437],{"_key":438,"_type":83,"children":439,"markDefs":444,"style":91},"ycgpzyd6v800",[440],{"_key":441,"_type":87,"marks":442,"text":443},"hdaqvvi45o90",[],"Validate the use case using your historical engineering data and demonstrate measurable value within a focused engagement.",[],"proof",{"_key":447,"_type":435,"description":448,"title":457},"eSL24Gu0krB8",[449],{"_key":450,"_type":83,"children":451,"markDefs":456,"style":91},"elz6hde2ure0",[452],{"_key":453,"_type":87,"marks":454,"text":455},"hbpw3rel2ha0",[],"Deploy vetta inside your infrastructure and connect it to existing engineering workflows, simulation environments and data pipelines.",[],"integrate",{"_key":459,"_type":435,"description":460,"title":469},"inNK3t6ZxIxp",[461],{"_key":462,"_type":83,"children":463,"markDefs":468,"style":91},"gc0eic0h92r0",[464],{"_key":465,"_type":87,"marks":466,"text":467},"iqaldc300ul0",[],"Become part of the daily engineering workflow, continuously learning from new data and improving over time.",[],"run","how we deploy",[472,480],{"_key":473,"_type":83,"children":474,"markDefs":479,"style":91},"uf3aqt9hqyf0",[475],{"_key":476,"_type":87,"marks":477,"text":478},"l89eqpv4ak00",[],"Not a pilot. A permanent",[],{"_key":481,"_type":83,"children":482,"markDefs":487,"style":91},"7eqkc22orn40",[483],{"_key":484,"_type":87,"marks":485,"text":486},"rlc8hnezn3r0",[],"operating relationship.",[],{"_key":489,"_type":490,"active":79,"buttonLabel":491,"buttonUrl":492,"description":493,"icons":509,"title":557},"ia3c3xjdpd","productCtaBlock","run a proof of concept","https:\u002F\u002Fcalendly.com\u002Fsphere-energy\u002F30min",[494,502],{"_key":495,"_type":83,"children":496,"markDefs":501,"style":91},"i809ph38zwq0",[497],{"_key":498,"_type":87,"marks":499,"text":500},"vixq6dlehq00",[],"One unified battery intelligence model trained on your historical test data, deployed",[],{"_key":503,"_type":83,"children":504,"markDefs":508,"style":91},"0b7d2acc2a93",[505],{"_key":498,"_type":87,"marks":506,"text":507},[],"inside your own infrastructure.",[],[510,524,537],{"_key":511,"_type":512,"finalIconAnimation":513,"initialIconAnimation":514,"title":515},"Xm0XyLbc7fUj","ctaIcon","1,2,4,5,9","1,2,4",[516],{"_key":517,"_type":83,"children":518,"markDefs":523,"style":91},"4fcq0hkn91z0",[519],{"_key":520,"_type":87,"marks":521,"text":522},"n1ybnpncunn0",[],"weeks to first prediction",[],{"_key":525,"_type":512,"finalIconAnimation":526,"initialIconAnimation":527,"title":528},"rGpCzwJUGkif","4,5,6,7,8,9,10,11","6,7,8,9,10,11,13,14",[529],{"_key":530,"_type":83,"children":531,"markDefs":536,"style":91},"tkbccgp5f2s0",[532],{"_key":533,"_type":87,"marks":534,"text":535},"zbrohbb2yio0",[],"30% fewer physical tests",[],{"_key":538,"_type":512,"finalIconAnimation":539,"initialIconAnimation":540,"title":541},"K4jvZtuvqTz0","1,2,4,7,8,11,13,14","1,2,4,5,6,7,8,9,10,11,13",[542,549],{"_key":543,"_type":83,"children":544,"markDefs":548,"style":91},"6pvjdn0rbo90",[545],{"_key":546,"_type":87,"marks":547,"text":122},"muvzzn4kzdj0",[],[],{"_key":550,"_type":83,"children":551,"markDefs":556,"style":91},"w5wc34ioe700",[552],{"_key":553,"_type":87,"marks":554,"text":555},"s85wegoiti00",[],"simulation error",[],[558],{"_key":559,"_type":83,"children":560,"markDefs":565,"style":91},"0fe31af56ced",[561],{"_key":562,"_type":87,"marks":563,"text":564},"1a2aa3f6c6de",[],"Payback in months, not years",[]]