I-Minerva, inkundla ye-AI eyenzelwe abaholi bokumaketha, isethulwe esidlangalaleni ngoxhaso lwezigidi ezingama-20 zamaRandi oluvela eqenjini elihlanganisa i-General Partnership, i-8VC, i-Lingotto Innovation, i-Topology Ventures, i-NBA Investments nabanye. Le nkampani iphinde yamemezela ukubambisana ne-OpenAI ukuze ihlanganise amamodeli we-frontier emisebenzini yokumaketha. Inkundla iklanyelwe ukuhlanganisa idatha yamakhasimende ehlukanisiwe futhi iyicebise ngomongo wabathengi otholakele, inikeze amabhrendi amandla okuqalisa imikhankaso yokumaketha ephelele ngama-AI ngaphansi kwamahora angama-24. Le ndlela ibhekene nenselelo ephikelelayo: nakuba izinkampani zinobuningi bedatha yamakhasimende, inani layo livame ukuba yisicupho ngenxa yokuhlukana nokungabi namongo wangaphandle.
Uhlanganiso Lwedatha Ngaphansi Kosuku
Emahoreni angama-24 okufika kwenkundla, abamaketha bangakwazi ukwenza imisebenzi emine ebalulekile. Lezi zinyathelo ziklanyelwe ukuguqula idatha eluhlaza nehlukanisiwe ibe umongo olungele i-AI, okusiza amabhrendi ukuthi awine amakhasimende kuzo zonke izigaba zomkhankaso.
Izinyathelo Ezine Eziyinhloko
- Bahlanganise futhi bavule idatha yamakhasimende yokuqala usebenzisa i-Agentic Data Engineer ye-Minerva
- Bacebise idatha yamakhasimende nge-identity graph ye-Minerva kanye nezici ezingaphezu kwezinkulungwane eziyishumi
- Badale, bahlaziye futhi bathuthukise imikhankaso ewina amakhasimende ngesilinganiso
- Balinganise ukusebenza futhi bakhiqize imibiko enemininingwane ngokusebenza komkhankaso
Ukusuka Ekuhlukanisweni Kuya Emfundweni Esetshenziswayo
I-Minerva yasungulwa nguJackson Engles, uDaniel Saedi noMatthew Joseph, abahlangana eNyuvesi yaseCalifornia, eBerkeley futhi baqala imisebenzi yabo kwezezimali eLazard, eBridgewater naseCitadel, ngokulandelana. Le nkampani yavela emsebenzini wangaphambili kaSaedi noJoseph wokusebenzisa idatha ehlukile ekuhwebeni ezimakethe, lapho babona khona kokubili amandla edatha yabathengi kanye nobunzima bokuguqula amasethi edatha ahlukanisiwe angathembekile abe imininingwane enokwethenjelwa. Lowo mava wabaholela ekudaleni i-Minerva njengethuluzi lokusiza izinkampani ukuthi zisebenzise i-AI ukuqonda kangcono, ukuzibandakanya nokuthola amakhasimende. Abasunguli bakholelwa ukuthi njengoba ama-AI eba namandla amakhulu, isici esikhawulelayo kumaqembu okumaketha akuseyikho ukufinyelela kumamodeli kuphela, kodwa ikhwalithi nokuhleleka komongo ama-AI angasebenza kuwo.
Imiphumela Nezibalo Ezikhombayo
Emikhankasweni yokuqala, i-Minerva isivele ikhombise umthelela obonakalayo. Inkampani ibika ukuthi inkundla yayo yasiza amabhrendi ukuthi athuthukise i-return on ad spend (ROAS) yemidiya ekhokhelwayo ngokuphindwe kathathu, futhi yakhuphula amazinga e-direct mail marketing qualified lead (MQL) ngokuphindwe kabili. Lokhu kuthuthukiswa kwavela ekwakheni kabusha indlela idatha yamakhasimende esetshenziswa ngayo ukuthola amakhasimende, kusuka ezinqubweni ezenziwa ngesandla kuya emsebenzini oqhutshwa yi-AI. I-Minerva iphinde yasayina cishe amakhasimende angamashumi amathathu nambili, okuhlanganisa amagama aziwayo afana ne-NBA, i-Juicebox, i-Luxury Presence, i-Trust & Will ne-Wander. Ngokuphawulekayo, i-Minerva isebenza ne-NBA ukuhlonza amathuba okusiza amaqembu ukuthi ajulise ukuzibandakanya kwabalandeli.
Ukubambisana Ne-OpenAI
Ngokubambisana ne-OpenAI, i-Minerva ithuthukise imisebenzi emibili eqondile isebenzisa amamodeli e-frontier afana ne-GPT-5.5. Lawa mathuluzi aklanyelwe ukufaka amandla e-AI athuthukile ezandleni zamaqembu okumaketha. Amelela ushintsho olusuka ekudingeni ubuchwepheshe uye ekusebenzeni ngolimi lwemvelo.
Amandla Amathulwa Yilawa Mathuluzi
- I-Agentic Data Engineer inciphisa amasonto omsebenzi wobunjiniyela bedatha abe amahora ngokuhlela nokuqonda ukwakheka kwedatha yamakhasimende, ukubhala i-SQL yokuguqula nokuqinisekisa umphumela
- I-Agentic Data Scientist ivumela abamaketha abangenalo ulwazi lokufunda ngomshini ukuthi basebenzise imiyalo yolimi lwemvelo — njengokuthi “thola abasebenzisi okungenzeka ukuthi babhuke indawo yokunethezeka ezinsukwini ezingama-30 ezizayo” — ukukhiqiza, ukuqinisekisa nokusabalalisa amamodeli okubikezela ngesicelo
