SELLEST OSAST
Claira is building the intelligence data layer for private market investors — stitching together every data room, email thread, CRM entry, and meeting note a deal team produces, and making that institutional memory persistent, queryable, and actionable. In a recent episode of BUILDERS, we sat down with Eric Chang, Co-Founder and CEO of Claira, to learn how he's approaching category creation in a market where the status quo is, as he describes it, "not very different than a thousand years ago when people gathered in a room and someone presented their case."
Topics Discussed:
Why processing deals faster doesn't make a better investor — and what actually does
How Claira builds a firm's institutional deal memory through ambient capture, without changing how deal teams work
Eric's trust-first approach to demand creation in a category with no established budget line
Why AI model velocity creates a buyer paralysis problem — and how Claira's positioning addresses it
How Claira pivoted away from point-solution task automation after ChatGPT and Claude commoditized it
GTM Lessons For B2B Founders:
Speed is not a category. The dominant use of AI in investment today is task acceleration — faster memo writing, faster research. Eric's argument is that none of that improves investment outcomes because it doesn't address the underlying structural problem: deal teams can't systematically learn from their own history. "A lot of people are using AI to help specific tasks be a little bit faster... but that in of itself doesn't make you a better investor." If your product delivers organizational intelligence rather than individual productivity, that distinction has to be the center of your positioning — not a footnote. Buyers won't discover it on their own.
Design for ambient adoption to neutralize the "wait and see" objection. The single biggest category creation obstacle right now isn't competition — it's buyers stalling to see what foundational models ship next. Claira's answer is architectural: users CC Claira on emails, include it in Slack and Teams threads, and the institutional data layer builds itself through normal workflow. "You can just get started today with no change in what you're doing and you reap the benefits three months later." When your product generates value passively — without requiring behavior change — the cost of waiting becomes concrete and the cost of starting becomes nearly zero. That reframes the "wait or buy" calculus entirely.
Name the limits of your product before a skeptical buyer does. In a market flooded with AI hype, Eric's demand creation strategy is deliberately anti-hype. He describes conversations where he explicitly tells prospects what Claira won't do: "It's not going to come up with a growth assumption. It's not going to come up with an ROI return on the company." The predictive judgment stays with the investor. Claira captures and surfaces everything that informs that judgment. For buyers who've been burned by overbuilt promises, a founder who leads with product limitations is actually building a stronger buy signal than one who leads with capability demos. This is especially true in a market — private markets investing — where trust is a professional currency.
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