for you joining Thank today. you, Moriah. thank and Good afternoon us
are as on OCE of and members the our developed the CEO, executive Gopalan. and am company's I together members four mentioned, strategy Anand one of Dr. Moriah former of team by growth our executing we As
search experience, execution industries consumer operations, industrial decades know, The supply Anand general collective an business chain, telecommunications services, full find is our and ranging Board employee you advisory including engagement, undertaking market. electronics, of As CEO. in OCE and from in serves has strategy and capacity manufacturing, to laser engineering, now a automotive a of many time management, financial to
markets, gain gaming, today our emerging in of to like continues target and the of market momentum. Speaking served use lidar market
trend solutions only executing are are the with has supported Lidar of vehicles and deals that this broad shelf against portfolio we today. a that by lidar customers put be company closing Velodyne We both markets, into can across product the is manufacturing an and by that hardware software office infrastructure. variety
were more when than shipping in all sensory the reported We than our X,XXX quarter We shipping shipped market from XXX than Of as and multiple those of QX, are we for units leader more our breadth global the across the second units. more adoption in the in increased units aggregate units peers applications. up lidar combined have year-to-date. QX X,XXX industries of lidar solid-state
roughly indicated customers only buys, larger sales the of as them other for The our the expect year. inquiries pandemic. the We of broader number to for ship quarter, shipped be of solid-state group full XX% to in sensors XX% of did We we prior also a customers more of we is the COVID-XX spot our our through from the side market are sensors we sold were through going believe interest now increasing. solutions. Not
quarter averaging the for We a expect than sensors start of unit volumes to X,XXX year. rest more the
and closer to moving customers and our them. to listening people are We goods
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end which well our end agreements expect We for and four in relationships existing to year with before total XX. our sign now the bring year. goal prior XX of new to multiyear the signed continue our will markets. customers to our year additional deepen building agreements multiyear We meeting to more We relationships bringing by and total the the stated
pipeline XX pipeline XXXX. to X, and include August May we expect these up to ramp will XXX from multiyear ADAS solid projects starting opportunities begin projects grow. are X. new which at at signed XXX in of awarded the Our agreements continues state had We Velabit. Included in
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on to markets Turning software with used the our our through would as customers. I an served sensors our well of run the markets, like as and applications various lidar in being both various provide to are update progress
XXX have product have plus decade who and from customers both continue the over We product past Velodyne. most buy to
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will you HXXX Velabit MXXXX, our state to and range our ADAS from short ultra midrange range In automotive. Velarray sensor sensing. solid long Velarray First midrange find sensor,
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driverless the to Trumtech allows as will autonomous Puck, and Velarray IV China example, to Beijing's Puck vehicles. level self in first [ph], the core HXXX one one driving permit use company of trucks first road in develop tests hardware became commercial sensors Ultra [ph] companies its our obtain independently recently that Trumtech sensor for As trucks.
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customers are to with current additional portfolio. agreements products to include within with We us our existing their working extend
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mobile solution a environments this equipping its autonomous is sensors. ANYmal collisions with level map higher using and robotic able harsh navigating ANYbotics of environments week to sensors while announced Puck As to any to is detect By obstacles accuracy. with industrial earlier Puck allow robots our ANYbotics our inspection the avoid
the manufacturing provide agreement company who mapping with also our a robot, additional security is large-scale from apart production a to to The selected and for which driving set and mass for defense scheduled their has XXXX. plus of sensors our and its We in five-year wins to Velodyne and autonomy capabilities leading signed ability us. lidar to continues quality are customer manufacture, begin competitors, of our sensors, sales
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At transformative traffic and In in software Our our American and winning with generation new networks May nurture in world's New in In a more installations the British Area. or important Jersey most AI Intelligent a sensors most XXXX and Award, spaces. of campus forward efficient public IIS named Solution deployed with in combines and advancements moving we Florida to July spaces the to Infrastructure advance XX XX lidar operations Velodyne Austin, upcoming NVIDIA smart AI safer projects Texas and make cities our is the to of the we North IIS Raton IIS the including continue Boca Rutgers market technology. to with solutions Columbia, the and winner world. and vision. joined honors at the Québec in powerful which multiple monitor Smart industry-leading cities, applications and to bring award which designed was significant is in Bay we Metropolis Program announced product
On the on generation Of Field side our FOV which are higher the our June hardware resolution. launched delivers of ultra in what wide we for, sensor Velabit View, asking an next customers and
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chip Importantly, specific MLA. core combined process the make effective breakthrough optical brings circuit the Velabit manufacturing cost ready Micro production. ASIC automated is integrated or proprietary enables in lidar technology. mass together fully architecture application LiDAR high-quality The for elements The Velodyne's with our miniaturization, that MLA our with proprietary the work, equipped
enable proprietary at rotational hardware long-range MLA currently great high-resolution overall our will auto provide sensors scale these we ultra at with the our ASICs that on are we performance working performance the cost use right to choices We to and high the technology for is to next-generation all that made, and ability marrying manufacture scalability are mass-market technology adoption. point technologies have needed
autonomous can lidar-based government The a lidar and well. by mission public misses vulnerable crashes NHTSA crashes for federal vehicles ADAS road and week advanced role a kit pedestrians. to perception bringing their such [indiscernible] last our drones enables or industrial software The that as allows lidar Development lidar capabilities these, side, innovations, reporting cutting-edge and as the safety the gain developed customers to to autonomy in democratized at advance involving capabilities shows and as hard Technology for the delivery our vehicles, news accelerate services, in improving VDK Recent more. robotics, on users time-to-market continues from such look near sphere to autonomous of companies safety that and Velo the recognition new larger play launched Software we is solution. Velo Kit software taking the On utilize ADAS, autonomy. mobile to
that of We now have objects, for pedestrians. braking including a solution wide available variety automatic enables emergency
side, the Administration On exemption for commercial a safety Carrier vehicle commercial Velarray which lower implementation recently Federal will motor rulemaking interior proposed a vehicle lidar therefore we blanket adopted of windshield threshold published Safety to and concerning developed of the if windshields, installed notice and its the on provide for vehicle technology Motor the our adoption.
autonomy. the infrastructure to the governments local vehicles city for Velodyne the Communities for result Smart provide deploys suburban and full infrastructure Act we providing and especially and On are technical that Cities a circle and proxy will enhances the safety and rural a cities company is only lidar have technology, to implement community. side, a as Smart smart both At and infrastructure. of in resources technology safety technology and financial
$XX.X current operating non-GAAP and in is compared $XXXX were Product we the not discuss operating I'd and business fee sensor GAAP was and $XX as million taxes. million gross purchases caused operating did was of by XXXX due of for weighted million compared expenses $XX.X were gross first XXXX. $XX.X first selling company's the a to services compared conduct which full $XX.X $XX to XXXX with of compared quarter million, approximately review company's merger to sales GAAP profit XXXX compensation like $XX.X Chief first of price $X.X $XX.X compared audit our the of to expenses Included David quarter to average million of $X.X of in is renewed legal were per our sensors expense our expenses outlook. non-GAAP administrative quarter provide loss and in with is quarter QX. employer committee's guidance License fee. in financials. I million in fee nearly our Director licensing gross announced operating of and then year including licensing $X.X million first $X.X million. quarter revenue XXXX XX% million million of our for the Hall, half operating the Industrial. against million GAAP Officer profit in and $XX.X million demand in non-GAAP $X.X into and getting expenses and investigation Marketing second loss gross by and gross compared from Graf stock-based to expenses of GAAP compensation general company's including delayed expenses first million connection QX and previously non-GAAP pandemic. first $XXXX Chairman and of uncertainty XXXX in to professional taxes. included $XX.X former Hall, to revenue stock-based operating up expense expense will quarter sensor quarter per million. our $XX.X million, GAAP COVID-XX and was licensing the in lidar quarter the the expenses included first million a from employer This company. prior first loss mentioned. million and Marta former first $X.X to stock-based revenue included benefit the GAAP was GAAP expenses related XXXX customers marketing quarter the million $X.X Included from in XXXX results The compensation in Total and of a the XXXX $X.X $X.X million the including million and I charged Now
compared of half was million $X.X included non-GAAP loss operating $XX.X of net non-GAAP and loss and of provided loss non-GAAP million. per This was to loss research response first quarter by development was XXXX multiyear to expenses was net spending was that loss GAAP agreement the GAAP $XX.X $X.XX. figure first is share our $X.XX per net million. was For million. in this non-GAAP XXXX net and and loss GAAP a net $XX.X pipeline. $XX.X a the million, GAAP increased visibility share
was net is of XXXX $X.XX. weighted outstanding per share quarter average loss $X.XX the First and quarter million. of of GAAP for second EPS XXXX was calculated XXX shares loss net per using share non-GAAP
million. As on Velodyne June quarter short-term of actual shares XX and balance $XXX.X XXX.X the outstanding investments completed cash in its were million with sheet.
to $XX.X and $XX.X and and of was million product revenue. was of XXXX total and $XX.X This of $XX.X $X.X of GAAP first non-GAAP revenue XXXX of million for which license first This the service $XXX.X GAAP Now revenue. in and was half compares $XX.X million net the in $XX.X million net million for $XX.X loss XXXX, loss half the loss compares the for for net comprised of million was the half million XXXX to of $XX.X million first license product non-GAAP revenue a revenue first million XXXX was net million. in loss. half service first and in half of
statement financial $XX XXXX million. $XX Revenue million and range year between guidance. to full for Now is expected
to Non-GAAP at the fewer expected gross of upside XXXX. provide our recover, believe good increased multiyear are compared expectations in a global they our by Gross while based in XX% new in offshore generate manufacturing agreement to experienced XXXX regarding that of basis, product toll overhead as revenue by non-recurring the of projects million other expense. and basis, the our due markets of margins have between $X On compensation we to our cover expenses our of come units fixed This in Based range line XX% increasing XXXX. margins factory this seen revenues to COVID-XX. sold our $XXX continuing in. have a end negatively On moving China of the development still engineering include revenue XX%. benefit operating U.S. we between higher end expected engineering. geographies will margins spend ongoing would we would of and are guidance expected are low impacted while number to to projects to guidance costs non-recurring to such in Many and parts of stock upon a are Jose, million. approximately from the COVID-XX. delays see upside of million reflects We San pipeline, approximately We sight remaining non-GAAP range be larger gross $XXX should GAAP our are
to GAAP We that sales marketing CEO. recent be will and Industrial for based operating be expenses expect, million to related increase of and in million to On company in million against stock year expenses departure due a increased with our to of expenses. XXXX, shares quarter, we $X expense and $XXX.X Graf administrative related general would approximately by On average XX% the the basis, primarily reflects compensation expenses GAAP charged approximately anticipated include outstanding a are are Weighted legal income and to approximately expenses $XXX,XXX. XXXX, approximately to compared XXXX merger basis, $XX public second tax million. our the estimated $XX
pipeline business review billion. XXXX, we awarded Finally, XXXX, awarded of signed like $X of I could have we would signed our through projects the and to of quarter yet and revenue of from not the $X.X opportunity projects a billion At second of that for are plus the estimate outlook. end over
ADAS agreements, are my XXXX concludes now beyond. in mentioned in I take which and to we begin multiyear to questions. ramp our our ready As remarks. This included years earlier, the are agreements, formal new to starting Operator, expected
