CIENCE Technologies, Inc. is a global B2B lead generation and go-to-market (GTM) services company founded in 2015 and headquartered in Denver, Colorado. The company’s core mission is to help organizations consistently generate qualified opportunities and hit revenue targets by pairing human SDR expertise with a proprietary AI-driven sales and marketing platform (now operated under the graph8 brand). CIENCE positions itself as a “machine-powered, human-driven” partner that can both design and execute complex outbound and inbound programs.
Over time, CIENCE has evolved from a pure SDR-as-a-Service shop into a hybrid services-and-software provider. Its offering now includes GTM system design, outbound and inbound SDR teams, large-scale outbound programs, and a robust data engine that powers precise targeting, enrichment, and intent-based engagement. The technology stack behind CIENCE includes components originally marketed as the CIENCE GO Platform—GO Data, GO Show, GO Flow, GO Schedule, and GO Digital—now consolidated into the broader graph8 AI front-office platform that handles data, campaigns, visitor identification, and AI-powered conversations across channels.
CIENCE serves more than 2,500 customers across 200+ B2B industries, from early-stage startups to large enterprises. Its campaigns typically blend multi-channel outreach (email, phone, chat, SMS, social, and ads) with rigorous research, ICP design, and ongoing experimentation to refine messaging and targeting. The company has been recognized on lists such as the Financial Times Americas’ Fastest Growing Companies and Inc. 5000, and has accumulated a large number of third-party reviews across G2, Capterra, TrustRadius, and other directories.
In the market, CIENCE is often compared to other outsourced SDR and lead-generation firms but differentiates itself with its emphasis on performance-based pricing, a large proprietary contact and intent database, and tight integration with graph8’s AI automation. At the same time, online reviews for CIENCE are highly polarized—some customers highlight strong results and responsive teams, while others report poor lead quality and misaligned expectations—making vendor fit and engagement model design critical considerations.