Akshat Singhal

Features we vibe coded P3: Codex Artifacts (Time ~ 12 Hours). Here is why we built it: Every legal team we work with drafts dozens of notices, agreements, and communications every week. The workflow was always the same. Generate a draft, copy it into Word, edit it there, come back for changes, copy again. That constant switching between tools was silently eating hours every week. The draft lived in one place, the edits happened in another, and version control was a mess. So we built Codex Artifacts. AI native side workspace inside Codex where drafts open as fully formatted, editable documents right next to the conversation. Here is how it works: 1. Enter your requirement in simple language 2. AI generates any kind of structured draft instantly (think notices, agreements, written statements & more) 3. Open it in the side workspace as a formatted document 4. Switch between View mode and Edit mode 5. Ask AI for further changes from the prompt bar 6. Every change creates a new version automatically The insight behind this feature was simple. Legal drafting is iterative. A notice might go through five rounds of edits before it is final. Every round trip to another tool breaks the flow and wastes time. The impact was immediate: - Drafting and editing in one place - Zero tool switching during the editing process - Clean version history for every document - Faster turnaround on notices, agreements, and client communications If your legal team is still copying AI drafts into Word and editing there, happy to show you how Artifacts changes that workflow. Read: help.legistify.com/en/articles/14…

3 highest paying skills in every industry right now are the same. Monitoring, deep domain knowledge, and pattern recognition. 1. Monitoring the situation is about tracking what is changing around you before it becomes a problem. In legal, that is regulatory updates, contract expirations, and compliance deadlines across jurisdictions. 2. Esoteric knowledge acquisition is the deep, specialized understanding that takes years to build. In legal, that is knowing which clause fails in which jurisdiction and which contractual language has caused disputes before. 3. Pattern recognition is connecting dots across large volumes of information to spot risk before it is obvious. In legal, that is noticing a problematic clause from three years ago reappearing in a new vendor contract. AI made execution cheap. Drafting, summarizing, extracting, reviewing. All of that can be automated now. Judgment, domain depth, and pattern recognition became the real currency. We are building for exactly this shift at Legistify. If you want to work on problems that sit at the intersection of AI, legal, and enterprise, reach out at hr@legistify.com.

Features we vibe coded: AI-powered contract metadata extraction (Time ~6 Hours). Here is why we built it: We asked 50 legal teams what slows them down the most. The answer was always the same. Manual contract data entry. In almost every team, someone was spending an average of ~30 mins, manually entering metadata from every single vendor contract. Multiply that by hundreds of contracts a month and you have an entire person's time lost to copy-pasting dates, party names, and renewal terms from PDFs. The work was repetitive, error-prone, and completely avoidable. So we built this. Here is how it works: 1. Upload a vendor contract into the repository 2. AI extracts parties, dates, renewal terms, and payment clauses instantly 3. System auto-fills all intake fields 4. User reviews and confirms 5. Contract routes for approval automatically Data entry time dropped from 20-30 minutes per contract to under a minute. Today it is one of the most widely used features across our enterprise clients, especially for vendor agreements and NDAs. If your legal team is still spending hours on contract data entry every week, I would love to show you how we solved it.

Features we vibe coded P2: Projects in Codex is now live at Legistify (Time ~24 Hours). Here is why we built it: We kept hearing the same thing from litigation teams. They were working on several related cases & notices at a time and losing context every time they switched between them. Every time they asked our AI a question, they had to reselect the cases, repeat their instructions, and restate how they wanted the response. Across dozens of cases a day, that adds up fast. So we built ‘Codex Projects’. Here is how it works: 1. Create a project and group up to 25 related cases together 2. Set custom instructions for tone, length, and focus 3. Ask anything across all cases in one go 4. AI maintains context and responds consistently every time The insight that led to this feature was simple. Lawyers think in matters, not in individual documents. Our system needed to match how they actually work. We shipped this in six weeks. The impact was immediate: - Bulk analysis across related cases in a single query - Zero repeated instructions across sessions - Consistent AI responses every time - Missing details across cases identified instantly If your legal team is juggling multiple related cases and losing time to repetitive workflows, happy to show you how Projects works.

42,655 fraudulent invoices sent in a single day. $1.7 million collected. Welcome to AI-powered fraud. AI agents just made fraud scalable, automated, and terrifyingly efficient. Thousands of invoices sent and conversion rates optimized while the person behind it sleeps. Here is what makes this dangerous. Most companies have invoice approval, contract management, and vendor verification in three separate systems. AI-powered fraud exploits exactly those gaps. A fake invoice referencing a real vendor name, a real PO format, and realistic payment terms sails through most approval workflows because the contract is sitting in a completely different tool. The fix is a single connected system where every invoice gets verified against the actual contract before payment is triggered. If this resonates with your legal or finance team, happy to share what we have learned.

67,665 open engineering roles globally right now. The highest in over 3 years. And this is after ChatGPT, Claude Code, and every AI coding tool shipped in between. This chart should end the "AI is replacing engineers" conversation for good. At Legistify, AI made our engineers significantly more productive. That did not mean we needed fewer of them. It meant we could finally build the features that were sitting in the backlog for years because we never had enough bandwidth. Productivity gains created more work. Features that used to take a quarter now take weeks. That means more modules in the pipeline, more systems to maintain, and more engineers needed to keep up with what AI made possible. This is what most people get wrong about AI and engineering headcount. Faster output per engineer expands what is worth building, and expanding what is worth building means you need more engineers. We are hiring engineers at Legistify right now. If you want to build AI-powered legal tech for 300+ enterprise clients, DM me or reach out at hr@legistify.com.

The way we hire engineers at Legistify has completely changed in the last year. We used to run traditional data structures & algorithms rounds and system design interviews. Now we give candidates a real problem from our product and one hour to solve it using any AI tool they want. What we look for now: 1. Understanding of system architecture 2. Approach to debugging AI-generated code 3. Fallback thinking when things break 4. Logical understanding & its impact at scale In legal tech, that last one matters enormously. A small logical error can cascade across hundreds of enterprise clients. The best candidates are the ones who know what to ask the AI, how to validate the output, and when to override it. Brendan Falk calls this the Composer 1 interview. We arrived at something very similar independently because the job itself changed first. Your interview process should reflect how your engineers actually work every day. Ours does now. P.S. If you share the same passion for building world class products leveraging AI, drop us a note on hr@legistify.com

Every line in this YC pocket guide sounds simple. Living them for a decade is a completely different experience. Early-stage founders read this list and nod along. Founders who have survived five or more years feel every line in their bones. The one that shaped Legistify the most was "do things that don't scale." In enterprise legal tech, it means sitting inside a client's legal department for weeks, understanding their workflows, watching how they actually use tools, before writing a single line of code. That work is slow and unglamorous. And the product knowledge it creates compounds for years. Every major product decision at Legistify traces back to something we learned by doing this early on. Most founders skip this step because it feels slow. In regulated industries, it is the single highest ROI activity a founder can do. The best startup advice fits on one page. The hard part is living it for a decade.

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AI solved code generation. AI is solving code review. The next frontier is something far harder: legal logic validation. At Legistify, our engineers vibe code daily & already started using AI for code review. Both bottlenecks are dissolving fast. What remains unsolved is whether a clause works across jurisdictions, whether a compliance rule applies to a specific industry, and whether an edge case survives a courtroom. That is where our entire focus is right now. As code generation and code review get automated, this becomes the only layer that truly matters. And it is growing in importance because more output reaching production means more surface area for legal errors. The future of legal tech belongs to the teams that solve this layer with patience and diligence. That is exactly what we are building at Legistify.

I stopped reviewing PRDs & started building prototypes myself. That one shift changed how fast Legistify moves. AI coding tools changed who drives product decisions at our company. When a founder builds the first version, it already carries domain context. That means fewer corrections, faster iteration, and a product that actually reflects how the customer works. In legal tech, being able to demo a working prototype early in the conversation changes the outcome entirely. Enterprise clients trust you more when they can see you understand their problem deeply enough to build for it, not just talk about it. The founders who combine deep domain expertise with AI tool fluency will be nearly impossible to compete with.

Brian Armstrong is spot on. Some of the best people we have hired at Legistify looked nothing like the "ideal candidate" on paper. What they had in common was simple. They were problem solvers who genuinely believed in the mission, took initiative without being asked, stayed curious, and never stopped learning. Most startups hire for speed. In enterprise legal tech, you hire for judgment, domain knowledge & deep care about the client. One wrong recommendation to a client's legal team can set the relationship back by a year. Resumes tell you where someone has been. They tell you very little about how someone thinks when the playbook does not exist yet. After a decade of building, the hires that changed Legistify were the ones who took ownership before anyone asked them to. Here is what we are looking for: ➡️ Sales / Account Executives ➡️ Customer Success / Onboarding ➡️ Applied AI Engineers ➡️ Python Developers ➡️ DevOps Engineers If this sounds like you or someone you would bet on, DM me or apply on our careers page or say hi at hr@legistify.com.

Engineers at Legistify follow this YC vibe coding playbook every day. But in legal tech, we had to add one critical step. Vibe coding has genuinely changed how fast we ship across contracts, litigation, compliance, and IP modules. But the AI has no idea when it is legally wrong. A vibe-coded feature can pass every test, ship clean, and still produce a clause that is invalid in a specific jurisdiction. The code works. The legal output does not. That is why we pair vibe coding with a domain validation layer. Every feature gets reviewed against legal accuracy before it reaches production, because a passing test suite is only half the job in legal tech. Vibe coding gave us speed. Legal domain expertise gave us the guardrails to use it responsibly. Save this if you vibe code. The YC playbook is a great starting point.

Even AI thinks you should stop making life decisions with AI. Someone put two AI models in a loop, told them to argue opposite sides, and went to sleep. They woke up to both models agreeing on exactly that. This is funny but it captures something real about where AI stands in high-stakes domains. AI can simulate reasoning and generate arguments on both sides of any question. But it cannot take responsibility for the outcome. In legal work, a contract clause, a litigation strategy, or a compliance decision needs someone who will be accountable when things go wrong. That is what we build for Legistify. AI handles the speed and scale. Humans own the decisions that carry consequences.

Anthropic's latest research shows that the widest AI adoption gap of any profession belongs to legal. 88% capability. 15% actual usage. That gap is one of the biggest opportunities in enterprise software right now. The 15% that is already adopted sits in low-liability tasks like summarisation and first drafts. The real opportunity is in high-stakes work like contract negotiation and litigation strategy, where verification is harder but the value is 10x. Once legal teams trust AI in one workflow, adjacent modules adopt significantly faster. The hardest part is earning trust the first time. At Legistify, we are seeing this shift firsthand as enterprises move from experimenting with AI to running it in production across contracts, litigation, compliance, and IP. The next few years will be defining ones for legal technology. The companies that earn trust at scale will own this space.

Founder-market fit is earned over years. There is no hack for it. Naval said it best. A product is a theory, marketing is an explanation, and a founder is uniquely qualified to build the company. The longer you stay in a problem space, the harder it becomes for someone else to catch up. Domain depth creates compounding product intuition that generalist teams cannot replicate. In regulated industries like legal, healthcare, and compliance, the cost of getting the product wrong is measured in liability. At Legistify, building for enterprise legal workflows for a decade has shaped every product decision in ways that would be nearly impossible to shortcut.

Great companies die in meeting rooms, not in the market. Steve Jobs described Apple as the biggest startup on the planet because every major function had one clear owner. The entire leadership met for three hours each week. That was the whole system. Ownership, alignment, and speed. Most companies add processes as they grow. The best ones protect clarity of ownership instead. Process becomes dangerous the moment it replaces judgment instead of supporting it. At Legistify, as we have scaled across contracts, litigation, and compliance for 300+ enterprises, keeping decisions close to the builders has been a core operating principle.

Adding color to our workday ahead of Holi 🎨 Bright colors. Big smiles. Stronger team spirit. Wishing everyone a joyful and vibrant Holi! 🌈 #Holi #TeamCulture #LifeAtLegistify

Honestly, these "AI will kill X by 20XX" tables are getting embarrassing at this point. A 2025 Bloomberg Law survey found that 75% of lawyers expected AI to automate their workflows, but only 37% actually saw it happen in practice. Automating tasks and replacing judgment are two completely different things. Law runs on accountability, context, and risk interpretation, and none of that disappears because drafting got faster. These tables confuse what is technically possible with what regulated industries will actually adopt. At Legistify, we build AI into legal workflows every day, and the closer you get to the work, the more you respect what machines still cannot do.

I think this ruling will change how every company handles AI internally. A federal judge in New York just ruled that documents a defendant created using a consumer AI tool are neither attorney-client privileged nor protected as work product. The reasoning is simple. An AI tool is not a lawyer, owes no duty of confidentiality, and its own terms of service allow data to be shared with third parties and regulators. This means anything your team types into a public AI chatbot about legal strategy, compliance risks, or contract disputes could be discoverable in court. At Legistify, we build enterprise-grade legal infrastructure specifically designed with auditability, access controls, and data isolation because legal workflows demand a different standard than consumer tools. If your legal or compliance teams are using public AI tools for sensitive work, this ruling should be the wake-up call to stop immediately.