Building Consistent AI for Contract Review with LegalOn's Daniel Lewis

Sep 29, 2025

Daniel Lewis, the Global CEO of LegalOn Technologies, has a rich background in legal tech from Ravel Law to LexisNexis. In the conversation, he emphasizes the challenges in contract review for in-house lawyers and how LegalOn’s hybrid AI approach combines large models with attorney-curated playbooks for more reliable results. Daniel discusses the barriers startups face in accessing legal data and the essential skills for modern legal leaders, highlighting the shift from review tasks to strategic roles enabled by AI.

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ANECDOTE

From Stanford Research To Contract Review

Daniel Lewis recounts founding Ravel from Stanford Law to make legal research more data-driven.
He later joined LexisNexis and moved toward contract review after seeing in-house counsel repeatedly cite it as a top pain point.

INSIGHT

Data Remains The Strategic Moat

Comprehensive legal datasets remain a high barrier to entry despite public digitization efforts.
Case law access improved but secondary sources, news, and full coverage still favor incumbents with massive investments.

ADVICE

Ground Reviews In Attorney-Curated Playbooks

Ground AI contract review in attorney-curated playbooks to ensure consistency and trustworthiness.
Provide ready-to-use suggested language and practice notes so teams can get value from day one.

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Daniel Lewis joins us this week to trace a path from Ravel Law to LexisNexis to LegalOn, with a throughline of data-driven thinking and practical outcomes for lawyers. Stanford roots shaped early work on judicial analytics, then a front-row view inside a global publisher broadened focus to content, guidance, and the daily reality of in-house teams. That experience pointed straight at contract review as a top pain for corporate counsel, which led to LegalOn’s product mission and global push.

Data access still shapes progress. Case law digitization advanced through projects like Harvard’s archive, yet comprehensive coverage, secondary sources, and news remain guarded by incumbents. Daniel explains why large datasets give scale, why startups face steep hurdles, and why thoughtful product scope matters. The lesson, build where data, workflow, and user value intersect.

LegalOn’s hybrid approach blends large models with attorney-built playbooks, practice notes, and suggested clause language. Consistency matters more than clever one-offs, so reviews align to standards, not model whimsy. Daniel shares a memorable demo from a rival where a phantom “California Code section 17” alert appeared, a cautionary tale that underscores the need for guardrails, verification, and explainability.

Conversation turns to multi-step agents and matter management. Picture an intake email from sales, missing key fields. An agent requests what is needed, opens a matter, applies a tailored playbook, highlights non-negotiables and fallbacks, then keeps stakeholders informed as work progresses. LegalOn also converts existing playbooks and prior redlines into AI-ready guidance, reducing setup chores while preserving organizational risk preferences.

Finally, Daniel outlines new muscles for legal teams. Daily AI usage shifts time from line-by-line edits to judgment, negotiation strategy, and process leadership. Tech fluency, business orientation, and change leadership rise in importance, along with a steady diet of outside-legal analysis from voices like Ben Thompson and Benedict Evans. The message, free lawyers from sludge, raise the ceiling on strategic work, and build for long-term improvement across the legal function.

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[Special Thanks to ⁠Legal Technology Hub⁠ for their sponsoring this episode.]

⁠⁠⁠⁠⁠Email: geekinreviewpodcast@gmail.com
Music: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Jerry David DeCicca⁠⁠⁠⁠⁠⁠⁠⁠⁠

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