In such a niche AI sector overseas, it was able to raise 100 million US dollars.

This company is using AI to solve a multi-trillion dollar industry pain point: making the time of knowledge workers visible, measurable, and optimizable.

Written by: Leo, Deep Thought Circle

Have you ever wondered why the manufacturing industry can calculate the cost of producing a car down to the cent, and the retail industry can accurately track the inventory of each item, but law firms, accounting firms, and consulting companies have no idea about their most important resource—human time? This question has troubled me for a long time until I learned that Laurel has just completed a $100 million Series C financing. This company is using AI to solve a trillion-dollar industry pain point: making the time of knowledge workers visible, measurable, and optimizable.

After in-depth research, I found that Laurel is not just simply tracking time. They are building the world’s first AI time platform, attempting to solve what founder Ryan Alshak calls the “time intelligence challenge”—the inability of knowledge-based industries to accurately connect time investment with business outcomes. In the age of AI, quantifying and understanding human capital has shifted from being a “nice-to-have” to a “matter of life and death” corporate necessity. This round of financing was led by IVP, with participation from GV (Google Ventures) and 01A, with new investors including notable figures like DST Global, OpenAI’s Kevin Weil, Alexis Ohanian, GitHub CTO Vladimir Fedorov, and others.

The Pain and Awakening of Six-Minute Bookkeeping

The root of the problem can be traced back to the working methods that the professional services industry has been using for decades. Lawyers, accountants, and consultants need to record their work time in six-minute increments so that clients can be billed by the hour. Ryan Alshak deeply experienced this pain while working as a lawyer: “It’s like on a busy Saturday night, I’m a chef cooking for 500 customers, but I’m also required to record every ingredient I use; this workflow is both distracting and inhumane.”

I can understand this feeling of frustration. Imagine, you’ve just completed a complex legal analysis, and your thoughts are in their clearest state, but then you have to stop and recall: How long did I spend reviewing the materials? How many minutes did it take to write this memo? What did I discuss with the client on the call? This forced disruption in work not only affects efficiency but also makes professionals feel like they are monitored factory workers rather than experts providing intellectual services.

Alshak’s moment of realization came very simply: “Why should I tell the machine what I’ve done at work, instead of letting the machine remind me what to do?” Behind this seemingly simple question lies a counterintuitive insight: lawyers, accountants, and consultants actually face the problem of underbilling because they forget a lot of the work they’ve already completed. If we can enable buyers (businesses) to gain more profits while saving time for users (professionals), this is the perfect foundation for building a company.

This pain point is much more common than I imagined. According to data from Laurel, professionals can recover an average of over 28 minutes of billable time each day, time that was previously lost due to recording omissions. At an average billing rate of $375 per hour, this means that each professional can generate an additional $175 in revenue for the company each day. For large firms with hundreds of professionals, this figure is quite astonishing.

Four Key Aspects of AI Redefining Time Tracking

Laurel’s solution sounds very intuitive, but building it is an extremely complex technical challenge. I have learned that to truly achieve end-to-end schedule automation, four key technical issues need to be addressed, each with a considerable technical barrier.

The first challenge is tracking digital footprints. Laurel must be able to integrate with every digital application used by the user, including various work tools such as Slack, Microsoft Outlook, Zoom, and more. Only when the AI can “see” all the work activities of professionals across platforms can it accurately reconstruct their work trajectories. It’s like installing an omnipresent yet completely undetectable monitoring system in the user’s digital work environment, capable of recording every click, every document edit, and every phone call.

The second level is the deep integration of AI applications. Laurel uses various AI technologies to process these digital footprints: data clustering algorithms categorize related tasks, machine learning models assign tasks to relevant clients and projects, generative AI creates job descriptions, and finally, tasks are encoded and classified through machine learning. This is not simply applying a ChatGPT interface, but rather building a dedicated AI system aimed at optimizing professional service workflows.

The third stage is the delicate balance of human-machine collaboration. The system generates a draft schedule for users, who can add, delete, or edit content. This “human-in-the-loop” design ensures accuracy while allowing the AI to continuously learn and improve. Every interaction by the user makes the system smarter, creating a positive feedback loop.

The fourth step is the seamless integration with the existing billing system. Once the user confirms the timesheet, the system automatically pushes the data to the billing system of the firm, keeping the backend management unchanged. This way, the work experience of professionals shifts from “filling out timesheets” to “reviewing timesheets,” greatly reducing the mental burden.

The cleverness of the entire process lies in the fact that it does not force users to change their working habits, but works quietly in the background, ultimately requiring only the user’s final confirmation. This design philosophy reflects a deep product thinking: the best technology should be invisible, making complex things simpler, rather than adding new learning burdens to users.

From Legal Tech Failures to Pioneers of the AI Era

Laurel’s success has not been smooth sailing; in fact, it underwent a complete rebirth. The company was initially founded in 2016 under the name “Time by Ping”, but struggled in the first few years. Alshak candidly acknowledged two main issues: an excessive focus on a single market in the legal industry, and the natural language processing technology at the time was not mature enough.

The turning point came in 2022 when Alshak gained early access to OpenAI GPT-3 and made a bold decision: to pause all work and completely restructure the product. This is an extremely rare move in the startup world, as most people will tell you, “Never rebuild, always iterate.” But Alshak chose to go against conventional wisdom, which I believe embodies the true spirit of entrepreneurship—being willing to take significant risks for a greater vision.

When ChatGPT was launched in November 2022, the entire market’s perception of AI underwent a dramatic change. Alshak described this shift: “I went from being seen as crazy to companies actively calling for help.” This dramatic transformation led to the company’s explosive growth from zero to a contract value of $26 million in the past 24 months.

Renaming to Laurel is not just a rebranding; it represents a comprehensive update of the company’s culture and core values. The choice of this name is also quite meaningful: Alshak hopes to select a name that feels timeless, rather than the typical startup name, but one that could be applicable in the 1600s, 2000s, or 4000s. “Laurel” symbolizes achievement in poetry and sports in Ancient Greece, and he hopes that when people see their timeline, they feel proud rather than fearful or oppressed.

This story of rebirth deeply moved me. It illustrates that in a rapidly changing technological environment, sometimes the bravest choice is not to stick to the established path, but to acknowledge mistakes and completely change direction. Laurel’s example proves that true innovation often requires this determination and courage to “start over.”

Why Now is the Perfect Time for the Explosion of AI Time Management

I have been thinking about why Laurel has been able to achieve such great success at this point in time, and I believe it involves the perfect combination of three key factors: technological maturity, market education, and business urgency.

Technical breakthroughs are fundamental. In recent years, large language models have reached a level where they can accurately understand complex work contexts. This is not just about language understanding; more importantly, these models can break down high-level intentions into specific executable steps. When I say, “prepare a due diligence checklist for the merger and acquisition project of client ABC,” the AI needs to understand which legal areas are involved, what types of documents should be included, how long it will take to complete, and so on. This level of granular understanding was simply not achievable a few years ago.

The shift in market education is equally crucial. The widespread adoption of ChatGPT has led even the most conservative professional service firms to start embracing AI technology. I find an interesting phenomenon: in the past, when Alshak pitched AI to law firms in 2018 and 2019, they would say, “We are still unsure whether cloud computing is the future, let alone what AI is.” But now, the same companies proactively call to inquire about how to deploy AI solutions. This shift in market mentality has created unprecedented opportunity windows for companies like Laurel.

The urgency in business comes from changes in the economic environment. In the context of a tightening economy, professional service organizations are facing unprecedented pressure for efficiency. Clients are no longer willing to pay for inefficiency, and fixed fee pricing models are becoming more common, which requires firms to have a precise understanding of the true costs of each service. As Ajay Vashee of IVP said, “When you’re selling money in a tightening economy, you cut through a lot of noise.” Laurel is not selling functionality; it’s selling real profit growth, which is persuasive in any economic environment.

There is also one factor that I think is very important but often overlooked: the demand for measuring the return on investment in AI. Companies plan to invest more than $1 trillion in AI over the next five years, but how to measure the effectiveness of these investments remains a black box. Most companies rely on surveys or usage rates as proxy indicators, but these are not accurate enough. Laurel’s time data platform can provide quantifiable, verifiable measurements of AI effectiveness, which is extremely valuable for companies that need to demonstrate the value of AI investments to shareholders.

This convergence of multiple factors has created the perfect conditions for Laurel’s rapid growth. According to the data, their annual recurring revenue has increased by 300% over the past 12 months, usage has risen by 500%, and they are currently collaborating with over 100 leading law, accounting, and consulting firms in the United States, the United Kingdom, the European Union, Australia, and Canada. These numbers reflect a collective awakening of an industry under fundamental transformation pressure.

The Deep Value Behind Customer Success Stories

I have always believed that the best product validation comes from real feedback from customers, and Laurel’s performance in this regard is impressive. According to the investor IVP, this is the only company they have evaluated that received a perfect customer satisfaction score of 10 from every customer. But I am more interested in the story behind these numbers.

The feedback from Matt Newnes, partner and tax transformation leader at Ernst & Young, is particularly persuasive: “I have personally experienced how Laurel has transformed our time intelligence approach. The previously manual time recording and entry process has now been significantly technologized. Laurel not only helps our employees record their working hours more comprehensively but also provides us with a deeper understanding of how our team works, allowing us to identify best practices and ensure the best outcomes for our clients. This has proven to be one of our most impactful AI investments.”

This passage makes me think of a deeper question: the value of time tracking is not just in the accuracy of billing, but in the insight into work patterns. When a company can clearly see the differences between efficient and inefficient work, they can standardize best practices and enhance the performance of the entire team. The value of this organizational learning may be more important than direct revenue growth.

David Cunningham, the Chief Innovation Officer at Reed Smith, shared an insightful perspective: “As law firms assess the impact of AI and fixed fees, obtaining refined intelligence with less effort is crucial for redefining the value within the firm and for its clients.” The key term here is “refined intelligence”—not rough time tracking, but deep insights that can guide strategic decision-making.

The words of Tom Barry, the managing partner of the accounting firm GHJ, impressed me: “Do you know how much business insight we can gain from this platform? What we are seeing now is a long-term game: this is not just a tool for tracking time.” I believe this transition from a tool mindset to a platform mindset is where Laurel’s true competitive advantage lies.

From the financial data, customers using Laurel reported a profit growth of 4-11%, primarily from an additional 28 billable minutes per professional per day and an increase in realization rates of 1-4%. These figures have been validated through independent audits by the Big Four accounting firms. More importantly, professionals saved 80% of their time on manual time entry, allowing them to focus on high-value work such as business development, relationship management, and strategic thinking.

These success stories have shown me a bigger picture: Laurel is not just solving the time tracking problem, but redefining the way professional services operate. When time becomes visible and optimizable, the efficiency and value creation capability of the entire industry will fundamentally improve.

The Three-Phase Vision from Time Tracking to Time Intelligence

During my research on Laurel, I found that Alshak has a clear three-phase strategic vision, and this long-term thinking impressed me. It is not just a simple product roadmap, but a deep reflection on the future of knowledge work as a whole.

The first phase is to prove that machines can record time more efficiently and accurately than humans. The key to this phase is to select the right target market—industries that must record time in order to make money, such as legal, accounting, and consulting. These industries have existing workflows, high execution pressure (not recording time means losing jobs), and a very clear return on investment when automation is achieved. This is why Laurel chose to start with professional services instead of targeting all knowledge workers directly.

The second phase is even more ambitious: using time data generated by machines to make these industries stop charging by time and start charging by results. Alshak quoted Charlie Munger: “Show me the incentive and I will show you the behavior.” He believes that the incentive mechanisms of industries that account for 20% of the U.S. GDP can be redesigned to stop producing activities and start producing efficient results. This shift from input-oriented to output-oriented may completely change the business model of the entire professional services industry.

The third phase is the most ambitious: even in a results-based world, people still need to understand the time invested to ask themselves, “Am I spending time on leveraged activities?” The goal of this phase is to extend the value of time data to all business organizations, helping every knowledge worker understand and optimize their time allocation.

The core statistics of this vision are thought-provoking: on average, knowledge workers work 9 hours a day, but only 3 hours are spent creating leveraged value. This means that 6 hours are wasted—3 hours on tasks that should be completed by AI agents and another 3 hours on tasks that should not be done by humans at all. Based on the number of knowledge workers globally, this amounts to 6.4 billion years of time wasted on tasks that humans no longer need to perform. This is the opportunity space for Laurel.

I find this way of thinking very enlightening. Many startups focus on solving existing problems, but Laurel is also creating the infrastructure for future possibilities while addressing current issues. Time data is not just for better billing; it is the foundation for understanding and optimizing human work. In the age of AI, this understanding becomes even more important, as we need to know which tasks should be delegated to machines and which require the unique value of humans.

The Supply Chain Revolution of Professional Services in the AI Era

During my in-depth understanding of Laurel, I discovered a very interesting analogy: they are actually building “supply chain visibility” for knowledge work. This concept has given me a whole new perspective on the entire industry.

Alshak makes a thought-provoking point: “No one has really mapped the time commitment to the outcome output. Professions such as law and accounting are the best at understanding their input (time) but still struggle with pricing value. On the other hand, industries such as consulting and financial services understand value but are ignorant of the true cost of creation.” This blind spot has long been addressed in other industries, but in the field of knowledge work, which represents more than 50% of global GDP, supply chains have never really been revealed.

This analogy reminds me of the transformation process in the manufacturing industry. Toyota’s Lean Production System revolutionized manufacturing because it made the efficiency and waste of each link visible. But in knowledge work, we are still in a pre-industrial revolution state—there is a large amount of “inventory” (unfinished tasks), “waiting time” (ineffective meetings and processes), and “defects” (documents that need rework) hidden in daily work, which cannot be quantified and optimized.

Laurel’s time intelligence platform is essentially creating the first true “supply chain management system” for knowledge work. It not only tracks time but also analyzes workflows, identifies bottlenecks, predicts resource needs, and provides optimization recommendations. This capability becomes especially important in the context of large-scale AI deployment, as businesses need to know the real return on investment of AI tools rather than relying on vague satisfaction surveys.

I believe that this shift in supply chain thinking will have far-reaching effects. When professional service organizations begin to manage knowledge work like manufacturers manage production lines, they will be able to: accurately predict project costs and timelines, identify which types of work are most suitable for automation, optimize team configurations and work allocations, and monitor project health in real-time and make timely adjustments.

This also explains why Laurel is able to help clients achieve a profit growth of 4-11%. This is not only due to more accurate time tracking, but more importantly, it is the systematic efficiency improvement achieved through supply chain optimization. When you can see the “production process” of the entire knowledge work, the opportunities for optimization become clear.

From an investment perspective, the market opportunities of this supply chain revolution are enormous. Ajay Vashee of IVP points out: “Professional services represent trillions of dollars of global economic activity, yet these companies lack basic visibility into their core resource—time—during operations. By addressing the challenges of time intelligence, Laurel has created a platform for broader AI transformation.” This is not just a software tool, but the infrastructure for the digital transformation of the entire industry.

The Founder’s Philosophy of Time and Mission-Driven

Understanding Alshak’s personal story has given me a deeper understanding of Laurel’s mission. This is not just a business project; it is a mission-driven enterprise deeply rooted in personal experiences.

Alshak often contemplates the topic of death, which may sound a bit heavy, but it is this profound understanding of the finite nature of time that shapes the core philosophy of Laurel. The company’s AI chat interface is even named “Mori,” a tribute to the Latin phrase “memento mori” (remember you must die). This contemplation of death is not negative; rather, it serves as a reminder for people to cherish the value of every minute.

What touched me the most was the story about his mother shared by Alshak. The establishment of Laurel is closely tied to the end of his mother’s life—she passed away from cancer just weeks after the company secured seed funding in 2018. Alshak said, “In the final moments of life, a minute spent with her was worth a million minutes doing anything else. I realized that I was not building a time tracking company, but a company that helps people understand ‘Am I spending my time the way I want to?’”

This personal sense of mission has been transformed into the core values of the company. Alshak hopes to become a “mirror” for the world, teaching the world this lesson: “We care so much about our money, but are so casual with our time. This is a fundamentally inverted framework.” He hopes to live as if he has 78 years of life, 4000 weeks of time, making every minute meaningful.

I found that this philosophy of time has deeply influenced Laurel’s product design. The company’s Greek pun is very interesting: Alshak mentioned that there are two words for time in Greek - “chronos” (clock time) and “kairos” (perceived time). Laurel is not just tracking chronos, but is also helping people optimize kairos - allowing them to feel the fullness of time in meaningful work, rather than feeling the passage of time on unproductive tasks.

This mission-driven approach is also reflected in the company’s long-term vision. Alshak hopes that Laurel will be responsible for eliminating the concept of “nine to five from Monday to Friday” from the English vocabulary. He believes that the future world will have humans working three to four hours a day but creating two to three times more value than now. This is not a utopian fantasy, but a reasonable expectation based on the development of AI technology.

I believe this sense of mission is Laurel’s true competitive advantage. In an increasingly homogenized tech industry, real differentiation often comes from the deep motivations and values of the founders. When your company is not just about making money, but about solving a problem you deeply care about, this passion translates into every aspect of the product, team, and customer experience.

Redefining the Future of Work and Value Creation

During my research on Laurel, I constantly pondered a larger question: What does this revolution in time intelligence mean for society as a whole? I believe we are on the eve of a fundamental transformation in the way we work.

From a historical perspective, every major technological revolution has redefined the nature of work. The Industrial Revolution shifted people from agriculture to manufacturing, while the Information Revolution created the concept of knowledge work. Now, the AI revolution is redefining what constitutes truly valuable human work. The data insights provided by Laurel will help us understand this shift: which jobs should be automated and which jobs require the unique value of humans.

The future work scenario I envision might look like this: Professionals no longer need to spend time on repetitive tasks such as drafting standard contracts, organizing financial statements, or preparing routine reports. Instead, they will focus on high-value work that requires creative thinking, emotional intelligence, and strategic judgment. AI will handle information gathering and preliminary analysis, while humans will concentrate on interpretation, decision-making, and relationship-building.

This transformation will also have a profound impact on the business models of the entire professional services industry. Fixed fee pricing will replace hourly billing as the mainstream, as clients are more concerned about outcomes rather than processes. Laurel’s time data will help firms accurately predict the true costs of different types of projects, allowing them to confidently offer fixed-price services.

I also see the social significance of this transformation. When work becomes more efficient, people will have more time for personal development, family relationships, and community involvement. This is not just an improvement in work efficiency, but also an enhancement of quality of life. As Alshak said, the goal is to enable people to create more value in less time, and then use the saved time for things that truly matter.

Of course, this transformation will also bring challenges. Some traditional jobs may be replaced by automation, which requires the entire industry to rethink talent cultivation and career development paths. But I believe that this transformation will ultimately create more meaningful and valuable job opportunities. The key is to actively adapt to this change rather than passively waiting to be disrupted.

From an investment perspective, Laurel represents not just a successful software company, but also a pioneer of the digital transformation of knowledge work. The time intelligence infrastructure they have built will become a necessity for business operations in the AI era. As Frederique Dame from GV stated: “Laurel is creating an enterprise intelligence layer for knowledge work, using time tracking as a product wedge. By capturing and organizing the complete lifecycle of how professionals spend their time, Laurel unlocks a new class of data, making work itself measurable, optimizable, and automatable.”

The value of this infrastructure will continue to grow with the further development of AI technology. As more and more companies begin to deploy AI agents and automation tools, Laurel’s time data will become the gold standard for measuring the effectiveness of these investments. This is not just a product opportunity, but a platform opportunity.

I am full of expectations for Laurel’s future, not only because they address a significant market demand that truly exists, but also because they propose a profound reflection on time, work, and the value of life. In this increasingly fast-paced world, companies that help people better understand and utilize time will create social value that transcends financial returns.

Ultimately, Laurel’s story tells us that the best entrepreneurial projects often stem from the founder’s personal pain points and deep sense of mission. When technological advancements combine with personal passions, it is possible to create companies that truly change the world. In an era where AI is reshaping everything, companies like Laurel, which possess both technological depth and humanistic care, may be exactly the kind of innovative force we need.