About Luminary

Luminary is the AI-native system of action for wealth transfer services.

High net worth households controlling more than $90 trillion in assets are meaningfully underserved by technology that was built for the mass market. As private client investment and banking products have commoditized, these clients are looking for more tax and wealth transfer advice, advice that has previously been impossible to deliver at scale.

Luminary solves this problem by transforming complex estate planning and ownership documents into a structured, proprietary data asset that powers verticalized, agent-driven workflows for tax and wealth transfer services. Our built-in multi-firm collaboration connects a client's full advisor ecosystem, and is the core infrastructure that enables our customers to deliver a highly personalized, end-to-end wealth transfer service offering at scale.

Our 1,000+ customers are leaders across the private client services ecosystem: wealth managers from exclusive multi-family offices through top private banks and trust companies, national tax advisory practices, and law firms among the AM Law 100. Our focus and proprietary data position us to expand from a wedge in wealth transfer advice into large adjacent markets including trust administration and estate distribution.

Founded and led by CEO David Barnard, former Head of Wealth Management at AllianceBernstein, Luminary is backed by 8VC, Fin Capital, Rockefeller Capital, Focus Financial Partners, and several family offices.

How we work

Every engineer ships to production multiple times a day. We make small, reviewable changes that go out through CI as soon as they're ready.

Modern tooling. Meticulous, Amp, Honeycomb, Temporal, PostHog, Braintrust. We keep the team small and invest in tooling for leverage.

Engineers own outcomes. You'll talk to customers and feel their wins and pain points, sit with our in-house subject matter experts, and help us build a great business.

We support both remote and in-office preferences. We have an office in NYC near Bryant Park and a lively in-office culture, but if remote works better for you, you'll be in good company.

The Opportunity

The world of legal documents is perfect for AI: pages and pages of dense, high-value, unstructured data with lots of nuance. We often see our customers modeling households that have 60+ legal documents of all kinds, many of which interact with each other and have a number of amendments stacked on top of them.

We've built a very interesting pipeline to take those documents and turn them into a household-level knowledge graph: a mapping of the people, trusts, businesses, and holdings of a family represented in our domain-specific ontology. We use that knowledge graph as a contextual layer between the structured data on our platform (which we use for reporting and modeling) and the unstructured source of truth documents. It allows the various agents that we run on the platform to traverse across the whole plan and find the exact paragraph and context that they need.

This is the foundation, but it's not the end state: our customers are always asking us to do more and more – both to capture more nuance and improve our accuracy, but also to build more functionality on top of our application layer to take advantage of all the data and context we've gathered.

What you'll do

Improve accuracy on our core extractions. Documents can be long, amended in unanticipated ways, or have unclear language. You'll work with our JDs and other experts to find the failure modes, figure out what's happening, fix them.

Own our evals. The only way to maintain a system like this is through tests and evals. We have a strong foundation for evals, but think that it can be better and want to invest heavily here. You'll design and maintain the suites that tell us whether a change helped and to prevent regressions.

Improve our shared agent harness. Tooling, parallel tool calls, context strategy, and the orchestration behind our agentic advisory workflows.