Seattle-based AI startup Bridgetown Research has secured $19 million in a Series A funding round led by venture capital firms Lightspeed Venture Partners and Accel. This investment values the company at $250 million.
Founded in 2024 by Harsh Sahai, a former leader of machine learning teams at Amazon and an engagement manager at McKinsey & Co., Bridgetown Research specializes in developing AI agents that autonomously conduct research and analysis. These agents gather proprietary data through expert interviews and customer surveys, providing executives and investors with actionable insights for strategic decision-making.
The newly raised funds will be utilized to expand the capabilities of these AI agents, enabling them to perform a broader range of analyses across various domains. Additionally, the company plans to strengthen industry partnerships to enhance access to sector-specific intelligence.
Commenting on the investment, Ishaan Preet Singh, an investor at Lightspeed, stated, “Companies are built on the quality of strategic decisions, and the research and analysis behind it. Bridgetown Research enables the smartest executives and investors to make these decisions with an order of magnitude more information, and at a pace that was earlier impossible.”
Anagh Prasad, an investor at Accel, added, “We are excited to partner with Harsh, who, with his background as an ace AI research scientist turned management consultant, blends a unique combination of skills and insight needed to imagine this whole new category of applied AI.”
Bridgetown Research initially focused on private equity deal screening and has since expanded to offer comprehensive market research services. The company’s AI-driven approach aims to democratize access to high-quality strategic insights, making them available to businesses of all sizes.
 uses the same natural-language approach for data-security investigations, letting teams question findings while keeping the eventual action subject to human approval. For instance, an analyst can ask how much data each computer on the organization's network transferred over the last 24 hours. The [generated query appears alongside an explanation](https://investigator.corelight.com/docs/nlq.html) of how the system interpreted the question and constructed the search. More importantly, analysts can inspect and change the query instead of treating the AI's answer as final. This makes it easier to understand how a search was constructed, an important part of making natural language network investigation auditable. Rather than relying on an opaque answer, analysts can examine the query and its results directly. So, when evaluating this capability, buyers weighing AI alert triage tools should ask to see the generated query alongside its results.](https://saastake.com/wp-content/uploads/2026/11/Auditable-AI-Investigation-Flow_11zon.png)
