Laurel Signal

Turning a firm's invisible daily work into clear, measurable intelligence.

Role
Dataviz Lead, Product Design → Product Co-Lead
Timeline
2025–2026

Professional-services firms, which make up 13% of US GDP, run on time. Traditionally, people reconstruct their day on a timesheet, and that guesswork becomes the firm's record of what got done.

In Laurel's Act 1, the company had built an agent that automatically captures work and hands people a pre-filled timesheet at the end of the day, sparing lawyers and accountants from reconstructing it by hand.

In Laurel's Act 2, we wanted to unlock for firms the ability to see where their profitability risks were, including what the ROI from their AI spend looked like, and for individual timekeepers, where their hours were spent. If those insights were compelling enough to change someone's behavior for the better (help them hit their targets, make a case for promotion, and so on) then it might be a trade even a skeptic would welcome.

Throughout, the team held to what the company saw as its fundamental mission:

How do we ensure our honesty here about genuinely wanting to “give time back” to people comes through?

This was very much a team effort. As one of its co-leads, I helped steer the product through several iterations, each one narrowing and hardening it around the use-case that mattered most: helping firm leaders see the risks to their profitability in fixed-fee and time-and-materials matters. The information architecture and the data visualizations at its heart were the part I owned most directly, built as part of a new design system that worked across a firm's various ranks.

Narrowing to the use-case. Getting there meant staying close to users—1:1s, on-site deployments, weekly prototypes, and designs we pressure-tested with users before committing engineering to them. Each iteration cut what didn't serve a firm leader's core question and sharpened what did.

Designing for agents. A design system used to exist to guarantee the quality attributes you cared about—usability, readability, consistency—by getting humans to adopt a set of patterns. The bigger shift now is that we design for agents as much as for people.

Whether it's a data visualization component or a UI pattern, it should be packaged so someone joining can point their agent at the project and spin up a new "look-alike" view or set of components. New colleagues become productive almost immediately, because the design system is tuned for the agents we all now use.

Democratizing a specialist skill. A skills-enhanced agent can bring non-experts up to speed quickly. I wrote Claude skills that let non-experts create data visualizations that are polished, on-brand, and consistent with a shared grammar of graphics, so that high-quality dataviz is no longer bottlenecked on a specialist.

12+ Bespoke views, unified into one design system
3 Persona-tuned designs for firm-leaders, managers, and individuals
1 Data visualization Claude skill written

A few examples of craft

Some publicly shared screenshots of the product, and some commentary about the visualizations in them.

The True Time chart: a double-lollipop plot pairing captured and released hours each day, with the hidden write-down drawn as a red bar between them and unreleased hours shown as gray bars

True Time

Drawing the gap instead of asking the brain to compute it.

A number that matters a lot to a firm leader isn't hours captured or hours submitted, it's the write-down that occurs as a result of the difference between the two numbers. Real work that quietly never makes it onto a bill. When that difference is the crux of the story, the visual encoding we choose ought to make it obvious how big or small these differences are, so that days with long red lines clearly indicate work that has gone unbilled.

Why a double lollipop, not two bars?
Side-by-side bars force the eye to estimate a difference from the negative space between their tops. It's a subtraction the brain has to compute, slowly and imprecisely. The double lollipop encodes that difference directly as one red segment, so the quantity you actually care about is drawn on the page rather than inferred.
The Summary view: throughput, unreleased hours, and billable-ratio stat cards above a Profitability Risks beeswarm plot, with at-risk projects highlighted in red

Profitability Risks

At-risk projects at a glance.

A first principle in information architecture is to present information as summary first, detail second. The Profitability Risks view collapses a firm's projects into a beeswarm, so a leader can see at a glance which projects warrant attention and how the whole book is distributed by billed hours. Headline metrics at the top carry sparklines for the time-series behind them. The unit here is deliberately the project, not the person: each dot is an engagement at risk, and the view stops short of ranking the individuals staffed on it, so it points a leader at problems to fix rather than people to watch.

The key decision
A beeswarm trades a precise y-axis for shape. You give up reading an exact value off an axis, but you gain an instant read on where the risk clusters, which is the decision this view exists to support. Keeping the encoding at the project level is what keeps it a tool for managing work, not one for scrutinizing individuals.
The Monthly Benchmark chart: paired bars comparing your billed hours against peers over four months

Monthly Benchmark

Comparison without a leaderboard.

Benchmarking should give a person a reason to become better, not make them feel inadequate or paranoid about it being used for micromanaging. This figure helps a person know if they're on track rather than who's winning. Each month pairs a person against an index from their peer group. Colors remain consistent across figures, with red encoding the more salient data point, and black encoding the secondary one.

The key decision
Using pairs of bars over time satisfies the goal of optimizing for readability and establishing a clear connection between a metric and an outcome (profitability). It avoids being needlessly clever with novel approaches.