Pinegap raises $8M in Series A led by Stellaris Venture Partners
AI-driven equity research platform Pinegap said it will utilise this funding for its go-to-market and sales activities, expand the engineering team, and to set up an in-house team of former equity research analysts.
Pinegap, an AI-driven equity research platform with headquarters in New York and technology centre in Bengaluru, has raised $8 million in a Series A round of funding led by Stellaris Venture Partners.
This funding round also saw participation from existing investors Inventus, Silicon Valley Quad, and DeVC.
Pinegap said it will utilise this funding for its go-to-market and sales activities, expand the engineering team, and set up an in-house team of former equity research analysts.
According to a statement, Pinegap automates the daily workflows of institutional buy-side analysts, to scale across hedge funds, long-only mutual funds, and registered investment advisors (RIAs) in the United States.
This startup, founded in 2024 by Ankit Varmani and Deepak Sharma, works directly with fund teams to understand their research processes, investment theses, and workflows and then builds custom AI agents tuned to each fund’s investment style, private data, and output formats. It said their agents are push-based: outputs arrive in the analyst’s inbox on a schedule or triggered by market events, rather than waiting to be queried.
Pinegap said it has deployed more than 1,000 agents across 100 plus institutional clients, generating upwards of 50,000 research reports per month.
“Every workflow we automate–whether it's an earnings preview, a company primer, or a thesis tracker–is time an analyst gets back to focus on what only they can do: judgment, conviction, and decisions. Pinegap isn't a chatbot or a search tool. It's a platform built around how a fund actually operates, tuned to their data, their format, and their investment style,” said Deepak Sharma, co-founder & CEO, Pinegap.
Pinegap noted that institutional asset managers face an era defined by rising data volumes, compressed research cycles, and increasing pressure on returns and it is building the AI infrastructure that lets analysts do more with the time they have–turning the most repetitive parts of the research process into an engine that runs itself.