Law-firm partnership on active litigation
Partnered with law firms on active-litigation needs, coordinating courtroom-sketch services and improving how visual assets are commissioned, managed, and delivered.
From concept to a secure, scalable product strategy
LegalTech Product & AI Strategy
Brought in to transform a LegalTech concept into a secure, scalable product strategy, covering the AI roadmap, future-state architecture, engineering standards, security, governance, integration proofs of concept, and platform selection.
2
Document-management integration proofs of concept
6+
Legal platforms evaluated for fit and integration
MVP to production
A controlled path with defined decision gates
Context
Legal work sets the constraints before the product does
- Client-confidential and privileged material means access decisions, permissions, and retention need to be settled before a feature ships, not after.
- Every firm runs a different document-management and matter stack, so integration assumptions have to be proven with real metadata and permission models.
- AI capability only earns trust when its output is reviewable and attributable, which shapes both the roadmap sequence and the architecture.
Solution
What I led
Strategy and architecture first, then proof of the riskiest integration assumptions, then a governed path toward production.
AI roadmap and future-state architecture
Defined the AI roadmap and future-state architecture, sequencing capability by capability so each step could be validated before the next investment.
Engineering standards, security, and governance
Established engineering standards, security expectations, and a governance model that created a controlled path from MVP validation to production.
Integration proofs of concept
Reduced law-firm integration uncertainty by building iManage and NetDocuments proofs of concept and mapping matter metadata, permissions, authentication, and data flows.
End-to-end legal workflow design
Designed end-to-end legal workflows so intake, matter context, review, and delivery formed one traceable path rather than disconnected manual handoffs.
Platform evaluation and selection strategy
Evaluated Relativity, Elite 3E, Intapp, Litera, Harvey, and CLM platforms to guide platform selection and integration strategy.
Future-state capabilities
What the strategy enables
These describe planned capabilities of the target model rather than measured production results.
- Matter-aware integration
- Matter metadata, permissions, and authentication mapped across document-management systems so context follows the work.
- Governed AI adoption
- A roadmap that introduces AI capability behind review and validation gates rather than as an unbounded feature.
- Secure product foundation
- Engineering standards, security requirements, and governance defined ahead of production scale.
- Informed platform strategy
- Platform evaluation that ties selection decisions to workflow fit, integration effort, and firm expectations.
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