From 1 April 2026, eligible working parents of Singapore Citizen children born on or after that date are entitled to 10 weeks of Shared Parental Leave (SPL). Phase 2 of the expanded SPL scheme has commenced, bringing the total pool of government-paid parental leave to 30 weeks for the first time. The change is not complex, but it has direct implications for employment contracts, leave policies, payroll configurations, and how employers handle reallocation requests. Employment lawyers advising clients on compliance need to be across the full picture.

WHAT CHANGED ON 1 APRIL 2026

Phase 2 increases the SPL pool from 6 weeks to 10 weeks. The default split remains equal between parents, now 5 weeks each rather than 3 weeks each. Everything else in the mechanics of the scheme is unchanged from Phase 1.

For context: The earlier Phase 1 change in April 2025 did more structural work. That was when Government-Paid Paternity Leave became fully mandatory at 4 weeks (up from 2 mandatory weeks) and the SPL scheme was introduced for the first time, replacing the old arrangement where mothers could transfer up to 4 weeks of maternity leave to fathers. Phase 2 is a quantum increase within the established framework.

SINGAPORE’S PARENTAL LEAVE REGIME AS AT 1 APRIL 2026

The 30-week total is built from three separate schemes under the Child Development Co-Savings Act. They have different eligibility conditions and operate sequentially in most cases.

All SPL is fully government-funded, capped at S$2,500 per week. Employers pay employees first and claim reimbursement via the Government-Paid Leave (GPL) portal. For employees earning more than approximately S$10,000 per month (prorated to the weekly cap), the difference above the cap is the employer’s cost unless the employer has agreed to top it up.

HOW SPL WORKS IN PRACTICE

The default and reallocation

By default, each parent receives 5 weeks. Parents who want a different split have a 4-week window from birth to change it via LifeSG without employer involvement. After that window closes, any reallocation requires mutual employer agreement from both parents’ employers. Employers need a documented process for both scenarios.

If parents cannot agree a leave plan with their employer, the fallback is a continuous block: SPL must be taken immediately following GPML (Government-Paid Maternity Leave), AL (Adoption Leave), or GPPL (Government-Paid Paternity Leave) (whichever applies), within the first 26 weeks of the child’s birth, on at least 4 weeks’ notice. Employers cannot refuse this fallback arrangement, but they can agree to something different if it suits both parties better.

Consumption window

All 10 weeks must be consumed within the first 12 months of the child’s birth. Unused SPL does not carry over. For employers, this means workforce planning needs to account for a 12-month window, not just the immediate post-birth period.

Eligibility conditions to watch

Three conditions may catch employers out in practice.

First, the child must be a Singapore Citizen, or become one within 12 months of birth. A child born to permanent residents who later naturalise will qualify retroactively, so employers need to keep eligibility open for the 12-month window.

Second, the father must be lawfully married to the mother at the time of birth (or before conception, or within 12 months of birth). Unmarried fathers are not eligible for GPPL and therefore not eligible for SPL.

Third, the 3-month continuous service requirement means employees on probation at the time of birth may not qualify, depending on how long their probation runs.

WHAT EMPLOYMENT LAWYERS SHOULD BE LOOKING OUT FOR

Review employment contracts

Any contract referencing specific SPL weeks (e.g., “6 weeks SPL”) should be updated to reflect 10 weeks. Contracts silent on quantum but incorporating statutory entitlements by reference are compliant without amendment.

Update staff handbooks and leave policies

Any handbook section on parental leave or SPL should reflect the new 10-week entitlement and the 5+5 default split. Update the reallocation process and notice requirements.

Update payroll systems

Payroll must be configured to calculate SPL at actual pay up to the S$2,500/week government reimbursement cap. Salary above the cap is the employer’s cost unless contractually enhanced.

Establish a reallocation process

Employers should have a documented process for handling reallocation requests: those within the first 4 weeks (employee-initiated via LifeSG) and those after 4 weeks (requiring mutual employer consent). Best to document decisions.

Prepare for reimbursement claims

SPL is fully government-reimbursed up to the cap. Employers pay first and claim via the Government-Paid Leave (GPL) portal. Check that payroll and finance teams know the claims process.

Review enhanced contractual leave

Employers who voluntarily offered enhanced SPL above the Phase 1 entitlement of 6 weeks may now find their contractual terms have been absorbed by the statutory floor. Consider whether the contractual enhancement still adds value or needs revision.

Brief managers on WFA implications

Workforce planning decisions that disadvantage employees taking parental leave will face scrutiny under the incoming Workplace Fairness Act (end-2027). Managers approving or deferring requests should document their reasoning.

The WFA forward-look

The Workplace Fairness Act, expected to commence at the end-2027, will prohibit adverse employment decisions on the basis of protected characteristics including caregiving responsibilities and pregnancy. Parental leave-taking will sit squarely within the Act’s scope. Employers who build workforce planning practices now that accommodate extended parental leave absences are better positioned when the WFA comes into force.

The Phase 2 change is operationally straightforward, but documentation and policy need to be updated. Whether there will be a Phase 3 expanding SPL further has not been formally announced, but the direction of travel in Singapore’s parental leave policy over the past two years makes continued expansion plausible.

Sources: MSF announcement – 10 weeks SPL from 1 April 2026 · Made For Families – SPL official scheme page · Strategy Group – Phase 1 commencement announcement (Apr 2025) · MOM – Paternity Leave · Lockton – Singapore enhanced parental leave update · Human Resources Online – Singapore policy changes 2026 · People Matters – SPL expansion to 10 weeks

Continuing our take on the Budget 2026, in 3 weeks, Singapore published a coordinated set of AI policy documents covering workforce training, enterprise support, physical infrastructure, and sector governance. This is a stacking of commitments across multiple agencies, all pointing in the same direction.

STRATEGIC GOVERNANCE

National AI Council

New inter-ministerial body chaired by PM Lawrence Wong, with DPM Gan Kim Yong, Minister Ong Ye Kung, Minister Josephine Teo, and Dr Tan See Leng. Provides strategic direction and coordinates the national AI agenda across sectors and agencies.

Chairing this at PM level matters signals that AI is being treated as national infrastructure. The sequencing of which sectors move first will be worth watching once the Council is operational.

No dedicated page yet — the council was announced in the Budget Statement and is expected to be formally constituted later in 2026.

Source: Budget Statement, Section C — singaporebudget.gov.sg

National AI Missions (4 sectors)

4 sector-focused AI transformation programs targeting advanced manufacturing, connectivity and logistics, finance, and healthcare. Each mission is anchored in defined objectives and measurable outcomes.

Source: Budget Statement, Section C — singaporebudget.gov.sg

Model AI Governance Framework for Agentic AI

Published in January 2026, this is the world’s first government framework for agentic AI systems. The core principle: AI can automate tasks, but humans remain meaningfully accountable for outcomes.

For lawyers, this is a formalisation of something that should already be clear. Accountability always follows the professional, not the tool. AI-assisted research, document review, or drafting still carries your name. The framework raises the expectation that practitioners understand what they’re deploying, including agentic systems that operate across multiple steps without direct human oversight.

Source: IMDA press release · MDDI announcement · Full framework PDF

INFRASTRUCTURE

Kampong AI Park at One-North

Singapore’s first purpose-built AI park, developed by JTC at LaunchPad @ One-North. Pilot phase from March 2026; completion by 2028. The AI block provides 14,500 sqm of business park space for up to 70 companies, with an adjacent residential block offering over 200 units — a live-work cluster for the AI community.

The Lorong AI co-working space (22 Cross Street and Vidacity) continues to serve enterprises during construction.

Source: JTC press release — jtc.gov.sg · LaunchPad @ One-North

ENTERPRISE SUPPORT

Champions of AI Programme

Jointly led by EnterpriseSG and DISG. End-to-end AI transformation support for selected Singapore companies, covering enterprise strategy and workforce training. Designed to set benchmarks that inspire broader sector adoption.

The selection-based model means not every firm qualifies. For those that do, the end-to-end framing might be more substantive than a standalone grant. Worth checking eligibility if your firm is mid-transformation.

Source: EnterpriseSG media release

Enterprise Innovation Scheme (EIS) expanded

400% tax deduction on qualifying AI expenditure for Years of Assessment 2027–2028, capped at S$50,000 per year. Qualifying spend may include AI software subscriptions, implementation consultancy, and staff AI training, but IRAS will provide further guidance later this year.

The cap is low for larger practices. S$50,000 of qualifying spend nets S$200,000 in deductions, a significant number for boutiques and mid-size firms that have been sitting on the fence about committing to an AI tool stack. For larger firms, this might not move the needle significantly.

Source: IRAS — Enterprise Innovation Scheme · IRAS — Budget 2026 tax changes

Productivity Solutions Grant (PSG) expanded

AI-enabled solutions in the PSG catalogue will be expanded from the present 30% to 50% of pre-approved tools. A new GenAI Navigator for SMEs has been introduced, recommending pre-approved tools matched to specific business needs, with grant support attached.

Pre-approval reduces the research burden for smaller practices without a dedicated technology function. The GenAI Navigator is a useful starting point, although it remains to be seen how the recommendations hold up in practice once the catalogue is live.

Source: IMDA — PSG programme page · NAIIP Factsheet — imda.gov.sg

WORKFORCE DEVELOPMENT

National AI Impact Programme (NAIIP)

The umbrella programme for workforce AI adoption. Targets 100,000 AI Bilingual workers and 10,000 enterprises over the next three years. Legal and accountancy are the first professions in scope under the TeSA expansion.

The legal cohort is being developed with the Singapore Academy of Law and Singapore Corporate Counsel Association. Three competency areas: redesigning workflows with AI, hands-on tool experience, and responsible AI use including bias awareness. Named use cases are research, document review, and contract management. Programme launch set for H1 2026.

Legal being named first of only two professions is the meaningful part. The profession is being treated as a pilot for what AI Bilingual looks like in a regulated services context. 100,000 workers and 10,000 enterprises in three years is an ambitious target; the legal cohort will be a signal of whether the programme can deliver depth together with volume.

Source: MDDI factsheet · IMDA factsheet

SkillsFuture AI learning pathways

Redesigned MySkillsFuture portal with clearer AI course pathways matched to proficiency level. A self-diagnostic AI Readiness Tool, developed with the Singapore Institute of Technology, allows users to assess their readiness and receive personalised course recommendations. Rolling out Q2 2026.

Source: MySkillsFuture — AI courses · COS 2026: companies and workers towards AI literacy

Six months of free premium AI tool access

Singaporeans who complete selected SkillsFuture AI courses will receive six months of free access to premium AI tools. Partners confirmed: Google, Microsoft, OpenAI, and Manus. Applies to courses completed from H2 2026.

The inclusion of Manus alongside more established names suggests the government is watching what practitioners actually use rather than defaulting to the usual list.

No dedicated page yet — expected on the SkillsFuture website from H2 2026 when the rollout begins; announced in the Budget Statement, Section C.

IHL AI literacy strengthening

AI education reinforced across all Institutes of Higher Learning, with emphasis on rigorous disciplinary thinking alongside AI fluency. The aim is graduates with disciplinary depth who can direct AI effectively — not graduates who’ve learned to use a few tools.

Source: MOE — COS 2026 announcements

MinLaw Guide for Generative AI in the Legal Sector

48-page non-binding guide published 6 March 2026, developed with over 20 stakeholders across law practices, in-house counsel, LegalTech providers, and academia. Three principles run through it: professional ethics (lawyers remain responsible for all work products regardless of AI involvement), confidentiality (understand how GenAI tools process and store client data), and transparency (consider disclosing AI use where it is substantial, affects cost, or involves client data handling).

Non-binding does not mean optional in practice. This guide shapes the expected standard of diligent practice.

Source: MinLaw announcement · Full guide PDF

12 initiatives across governance, infrastructure, enterprise support, and workforce development. The direction is consistent: AI adoption in legal practice is expected, financially supported, and must have a governance framework behind it.

If you followed the Committee of Supply debates during Budget 2026, you likely heard a new phrase entering the national conversation: the “AI bilingual” worker. Minister for Digital Development and Information Josephine Teo introduced the concept as part of the National AI Impact Programme (NAIIP), a government initiative to train 100,000 workers in artificial intelligence by 2029.

For the first time, the Infocomm Media Development Authority (IMDA) is expanding its TechSkills Accelerator (TeSA) programme beyond the tech sector, beginning directly with the legal and accountancy professions.

The message is clear: AI adoption in the legal sector has gone beyond a competitive edge into a national economic priority.

So what does it actually mean to be an “AI bilingual” lawyer, and how will it change the way you practise?

Decoding “AI Bilingualism” in the law

Being AI bilingual does not mean learning Python, understanding the mathematics of neural networks, or becoming a software engineer.

Instead, AI bilingualism sits at the intersection of deep domain expertise (your knowledge of the law) and AI fluency: knowing how to direct, verify, and apply AI tools to solve legal problems. Further, deep fluency involves orchestrating AI agents to work independently according to your specifications. It is the ability to delegate high-volume text processing to software, so that human judgment can be applied to what matters most. As Minister Teo suggested during the debates, an AI agent may organise information faster than a human, but human domain knowledge is what ensures the technology is looking in the right places and interpreting nuances correctly.

Why the legal sector is the starting point

The legal profession was selected as the entry point for the TeSA expansion because it is highly exposed to AI. The daily work of legal practice, from searching databases for cases, reading judgments, to assembling clauses from past precedents, fundamentally involves processing large amounts of text.

For decades, the standard workflow has looked like this:

1. Search: spending hours across multiple databases using rigid Boolean queries to find relevant cases.

2. Review and Extraction: reading through hundreds of pages of judgments or parliamentary records to locate a single relevant paragraph.

3. Drafting: manually assembling clauses from past precedents, hoping nothing critical is missed.

An AI bilingual lawyer might approach this differently:

1. Strategic Query: asking an AI assistant a complex legal question in plain language.

2. Synthesis and Verification: reviewing AI-generated insights, summaries, and citations back to primary sources.

3. Refinement: using AI drafting tools to generate a baseline document, allowing the lawyer to focus on tailoring the strategic argument for the specific client.

Accuracy and the governance framework

The question lawyers most often raise about AI tools is accuracy. Specifically, the risk of hallucination: citing a case that does not exist.

Singapore has recognised this risk. In January 2026, the government released the world’s first Model AI Governance Framework for Agentic AI, emphasising that while AI can automate tasks, humans remain meaningfully accountable.

Being AI bilingual means understanding these guardrails, so that the cognitive load of mechanical work can be removed without compromising accuracy.

What comes next

The government is actively rolling out subsidies, grants, and training programmes in partnership with the Law Society of Singapore and Singapore Academy of Law to support firms through this transition. Firms that move early may benefit from both the support and the practical gains in efficiency, margins, and client service.

The government’s move signals something more fundamental: AI fluency in law is shifting from competitive advantage to professional baseline. Firms and practitioners who treat this as an opportunity to sharpen their practice might be better placed to serve clients well.

How will you engage with this change?

Sources:

1. “Minister Josephine Teo, Committee of Supply 2026 Speech, Building Singapore’s Capability Advantage in a Digital Age”

https://www.csa.gov.sg/news-events/speeches/minister-josephine-teo-committee-of-supply-2026-speech-building-singapore-s-capability-advantage-in-a-digital-age/

2. “Model AI Governance Framework for Agentic AI” https://www.imda.gov.sg/-/media/imda/files/about/emerging-tech-and-research/artificial-intelligence/mgf-for-agentic-ai.pdf

No, seriously though, before ChatGPT, chatbots were hopeless. We were both working full time in innovation and transformation roles in 2022 when ChatGPT launched. As far as we know, no one saw it coming, despite the previous waves of AI.

Chatbots were pretty much thinly-veiled decision trees, whose branches were painstakingly crafted by hand. Alex, for example, had experimented with Google’s Dialogflow to try and create a chatbot that would help self-represented courts users to access basic legal information. The problem with such chatbots is that they require a clear starting point and a clear progression of the conversation - they don’t really work if users can come to the conversation from multiple angles or in multiple, unexpected ways.

But ChatGPT was just the beginning. It was the underlying advances in Large Language Models that really unlocked the potential in Elefant. The ability to read information and then render it as structured data is really, really helpful for law.

A lot of the things we had wanted to do for years were suddenly doable. For example, many years ago, Ian had experimented with knowledge graphs and how such technology might be useful for tracing the relationship between legal cases. We now had a way to make such a thing practical, with minimal manual intervention.

We implemented several new features that added value to the core search experience:

First, briefcases. Users can store the results of their search in a folder we call a “briefcase”. From there, users can open up a chat dialogue to query the results in the briefcase, helping to digest complex information by, for example, generating summaries.

Second, the knowledge graph. Building on Ian’s earlier idea, we now have a feature that allows users to render search results as a knowledge graph, rather than just a list of entries. This allows users to visualise the relationships between entities, very useful for things like cases, which often refer to other cases. Using this technology, it becomes easy to see which cases are more central, and therefore more important, to a given body of law.

Third, research memos. Users can simply tell Elegant to go and research a specific legal topic. Elefant then returns a research memo summarising its findings and including links to primary sources.

Fourth, the reading list downloader. No longer do students and lawyers have to painstakingly search for and then download cases individually. Elefant users can simply upload their reading lists to Elefant, which will recognise case names and then download the entire list of cases, packaged conveniently in a single zip folder.

We’re even working on going back to our roots, to develop a sentencing tool that can read cases, extract data from them, and structure that data. Users can then discern sentencing trends and produce useful aids like graphs, tables or charts.

“But wait!” you might say, “What about hallucinations?”

Actually, we’re well aware of the limits of generative AI. In fact, for some time, we resisted branding Elefant as an AI company - we were very focused on improving the search experience.

We’ve now reached a stage, however, where we’re confident we’ve found ways to handle the hallucination problem. In fact, we think we do it better than others:

First, we focus on primary sources like cases, statutes and parliamentary records. We don’t generate answers for you based on a training dataset that encompasses the entire internet. We search first, narrow down to relevant primary sources - and this narrowing down takes a lot more domain expertise than one would think - and only then turn the generative AI loose to do its thing. The focus on primary sources at the start, rather than at a later stage like most Retrieval Augment Generation solutions, helps to significantly reduce the risk of hallucinations.

Second, we validate the results of anything generated by AI using a form of Logic Augmented Generation. We’re working with our friends at Legalese to develop computational law technologies that map the logical relationships between legal provisions. In other words, we’re also developing a deductive logic framework to complement the inductive logic work done by generative AI. The two approaches work hand-in-hand to check each other’s results, thereby further enhancing accuracy.

Third, we’re constantly tweaking and repurposing our existing features to enhance the overall Elefant experience. For example, knowledge graphs can also be used to enhance accuracy of AI-generated answers. Further, we’re tweaking the reading list downloader so it can be used as a final layer of verification - anything Elefant cites, it should also be able to find and download for you so that you can check that source.

We’re excited about AI and the possibilities it opens up, but we’ve never forgotten our beginnings as lawyers and our commitment to the truth.

We knew that we wanted to build something together, but it wasn’t clear to us exactly how we could go about building it. Neither of us had the experience, resources, or contacts necessary to dive into a start-up. And we both had full-time jobs.

So, we decided (in retrospect somewhat naively), that we’d just bootstrap the whole thing on the side. Ian had taught himself coding during his time at the Legal Technology and Innovation Office, and Alex had learnt a bit about UI/UX design during his stint at the Office of Transformation and Innovation, Supreme Court of Singapore (in the legal profession, we do like entity names long). Therefore, while Ian began to code the back-end, Alex mocked up some of the front-end stuff.

This approach had severe limitations, and we rapidly found that working on Elefant part-time was, well, not working. A product like this needs the attention and level of skill that only full-time commitment can provide. But anyway, we thought you might enjoy some of the early, fumbling steps we took into this product.

Here’s the first attempt Alex made at designing a logo for Elefant on Canva. At the time, we didn’t know the “Elephant” domain name was taken, so we hadn’t yet gone for “Elefant”.

Remember how we said Elefant started out as a sentencing tool? Well, here is the first iteration of Elefant’s initial concept.

We then pivoted to legal search, and here you see the revised home page, which has been simplified to reflect the new focus.

It should be apparent that we’ve come a long, long way since then. In 2023, Ian decided to go full time on Elefant. We applied for, and successfully got into, Singapore Management University’s BIG incubator program. With a small grant and a bit of our own money, we could finally hire some professional help. Ian began coding at a furious pace, and we engaged some freelancers to help with both the back-end and front-end work. The results speak for themselves, we think!

P.S. The answer to the subtitle is 0: we commissioned a graphic designer for our logo, which we’ve come to love.

Several people have asked us, “why Elefant?”. There’s two answers to this: a short one and a long one.

The short answer is that the name “Elephant” was already taken when we incorporated our company in 2023. So, we went with the next best thing, which was “Elefant”. “Elefant” is, by the way, the real name of the animal in German (it’s also the name of a heavy tank-destroyer, but that’s not what we’re getting at).

That probably still doesn’t answer the question. “Alright,” you may be thinking, “but what do elephants have to do with legaltech?” This requires the long answer.

When we first started thinking about doing our own thing, we were focused on solving problems for people like us - that was, after all, what we knew. Alex had been in prosecution for several years and Ian had also done a stint as a prosecutor. One problem that we thought we could tackle was sentencing precedents - that is, finding previous similar cases to inform a court about what the punishment for a convicted offender should be.

Offences, the criminal law term for crimes, usually specify a maximum punishment term. For example, the offence of cheating under section 417 of the Penal Code 1871 is a maximum of three years’ imprisonment, a fine, or both. However, not everyone gets the maximum punishment - the maximum punishment is reserved for the worst class of offenders. Instead, most people get something closer to the average punishment, what we call the tariff. But the tariff is not defined in statute, you have to infer it by looking at previous cases and figuring out the pattern.

This isn’t easy to do, because most cases don’t actually get reported in the Law Reports. The Law Reports only report on a selection of cases, usually those in the High Court or Court of Appeal or cases with some novel point of law or high level public interest. But a huge number of average cases go unreported.

The first iteration of Elefant was conceptualised as a sentencing tool. It would help users retrieve precedent cases, compiled from various sources like newspaper reports and case reports available from open sources, like the e-Litigation website. Elefant would then help the user to download and package those sources, and also compile a table of sentencing precedents - that is, it would summarise the sources and compile them into a single table showing the facts of each case and the sentence imposed.

Hence the name, “Elefant”! As the saying goes, “an elephant never forgets”, and what we were building was a product that could help search the collective memory of the internet to unearth relevant precedents. We thought this tool could be helpful not only to lawyers, but also potentially to journalists or researchers, looking for similar cases that might not otherwise be easy to find.

But even this, rather modest, idea proved to be difficult - these were the days before ChatGPT and the generative AI revolution. It was hard to develop an automated method of reading and extracting information from reports, and then summarising and synthesizing that into a usable table.

So, we decided on something even more modest - we’d settle for simply creating a search engine that would allow users to perform legal research on cases, statutes, newspaper reports, and parliamentary records (called Hansard) simultaneously. In the current paradigm, statutes are on Singapore Statutes Online, cases on e-Litigation, newspaper reports on their own websites, and Hansard on the Parliament website. It seemed to us really ridiculous that the user journey was so fragmented.

In this way, we arrived at another meaningful connection to the name, “Elefant”. There’s a parable about a group of blind men who were asked to feel something and say what it was - the first said it was a snake, the second said it was a pillar, and the third said it was a rope. But they were all wrong - it was an elephant! The first man had felt its trunk, the second its leg, and the third its tail.

The point of the parable being that you can’t necessarily tell the truth of a thing unless you see the whole picture, not just a part. And that’s exactly what Elefant, in its current form, allows you to do - see the whole picture with a single search.

Elephants, being the largest terrestrial mammals in the world, also have the longest gestation period among mammals. A female African elephant, Loxodonta Africana, is pregnant for an average of 22 months, nearly two full years.

The same is true for Elefant. It’s been a long journey to get where we are today. It took a long time before we even revealed it to anyone. This particular Elefant had a gestation period of nearly five years (we think the wait was worth it!).

Let’s start at the very beginning: Ian and Alex met in 2015, we both joined the Attorney-General’s Chambers on the exact same day. Ian went to work in the International Affairs Division while Alex went to the Financial and Technology Crime Division. But we often crossed paths at various events and quickly became friends.

Both Ian and Alex were regularly involved in technology and innovation initiatives while in government service: Ian had already begun to pick up coding and was building some document automation tools. We also had the chance to work together for several months when we were both seconded to the Personal Data Protection Commission (“PDPC”).

It was at PDPC that we began to see the potential for legaltech to radically improve our work. One request we worked on required us to search the entire corpus of statutes in Singapore for any interactions with the Personal Data Protection Act (“PDPA”). We struggled to find an efficient way to do this within our attachment period. So, we turned to tech. We devised a plan to build a crawler that would download the entire corpus of statutes, then run another bot that would look for statutes with the highest frequency of word collisions with the PDPA – the idea was that this would give us, roughly speaking, the top few statutes that were likely to affect the PDPA and that could then be manually checked for interactions.

Eventually, we both moved into full time legal tech and innovation roles: Ian went to the newly-formed Legal Technology and Innovation Office at the Attorney-General’s Chambers, while Alex went to the likewise brand-new Office of Transformation and Innovation at the Supreme Court. For the next couple of years, we got to work on the frontlines of legal transformation efforts in our respective roles, building, picking up project management and design skills, and meeting people from all over the legaltech ecosystem.

Through our experiences and engagements in the tech space, we realised that owning a project is truly critical to driving it forward.

Ownership is really important in innovation, because to make a good product, you need a clear vision and the ability to make quick decisions when you (inevitably) run into roadblocks. The people working on innovation projects also need to have the correct incentives, otherwise things get bogged down and eventually drowned in unnecessary busywork. Nothing incentivises like potential loss. We drew inspiration from Nassim Nicholas Taleb’s views – if you want something to be good, the people working on it need to have skin in the game. There was no better way to align all of these thoughts than having our own startup.

By 2020, it became clear to us that we needed to have something to call our own. We just needed an idea.