Rohirrim Raises $15 Million in Series A Round Led by Insight Partners
Startup Rohirrim Inc. has announced securing $15 million to expand the adoption of its artificial intelligence-powered text generation platform.
Insight Partners and returning backer General Purpose Venture Capital led the Series A round investment. This funding comes less than a year after the company emerged from stealth mode.
Enterprises commonly employ a document known as a request for proposal (RFP) to solicit contracts from suppliers, outlining project parameters in detail. Responding to an RFP can be time-consuming, sometimes taking weeks of effort for bidders.
Rohirrim claims its AI platform streamlines this process to mere minutes. Users input a high-level description of the desired RFP response, and the platform generates it automatically. Additionally, the platform can produce other types of extensive content such as case studies.
The text generation is based on a company’s internal data, capable of ingesting various formats including Word documents, PDFs, slideshows, and more, from both on-premises and cloud-based applications, as well as employee devices.
Since its release earlier this year, Rohirrim claims to have amassed a significant customer base, including several Fortune 100 enterprises. IBM Corp. is one such customer using the platform to automate business content creation.
The latest funding will facilitate the company’s expansion of its team and customer base, with a focus on entering new markets. Rohirrim’s platform is currently utilized across multiple sectors, including commercial real estate, defense and aerospace, energy, higher education, insurance, and legal.
A portion of the funding will be allocated to product development, notably towards Anglachel 200B, a large language model. This model, based on unsupervised learning, is designed to have 200 billion parameters, nearly three times larger than Meta Platforms Inc.’s Llama 2, a prominent open-source AI system. This approach saves developers time by not requiring enriched contextual cues in the training dataset.
