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Our agents analysed millions of data points across social platforms, identified resonant narratives by region and demographic, and recommended ongoing adjustments to content and spend.",color:"from-purple-500/20 to-blue-500/20",content:` 2 <h2>The Challenge</h2> 3 <p>In the high-stakes world of modern political campaigning, understanding the electorate's shifting sentiment in real-time is the difference between victory and defeat. Traditional polling is slow, expensive, and often fails to capture the nuance of digital discourse. Our client, a major political organization, faced a fragmented media landscape where narratives emerged and evolved within hours, not days.</p> 4 5 <p>
5They needed a system that could not only monitor these conversations at scale but also interpret the underlying emotional drivers and predict which messages would resonate with specific demographic groups across different regions.</p> 6 7 <h2>The Solution: AI-Driven Narrative Intelligence</h2> 8 <p>We deployed a custom fleet of autonomous AI agents designed to ingest and analyze vast amounts of unstructured data from social media, news outlets, forums, and public comment sections. Unlike simple sentiment analysis tools, these agents were equipped with advanced natural language understanding (NLU) capabilities to detect sarcasm, local idioms, and emerging slang.</p> 9 10 <h3>Key Capabilities:</h3> 11 <ul> 12 <li><strong>Real-time Sentiment Mapping:</strong> Continuous monitoring of voter sentiment across 50+ digital channels.</li> 13 <li><strong>Narrative Clustering:</strong> Automatically grouping similar discussions to identify dominant themes and emerging viral topics.</li> 14 <li><strong>Demographic Resonance Scoring:</strong> Predicting how specific messages would perform with key voter segments based on historical engagement patterns.</li> 15 <li><strong>Dynamic Content Recommendations:</strong> Generating actionable insights for the creative team to adjust ad copy and video scripts on the fly.</li> 16 </ul> 17 18 <h2>Implementation</h2> 19 <p>The deployment involved a three-phase approach:</p> 20 <ol> 21 <li><strong>Data Ingestion & Training:</strong> We fine-tuned our base models on historical campaign data and specific regional dialects to ensure high accuracy in sentiment detection.</li> 22 <li><strong>Agent Deployment:</strong> A network of "Listener Agents" was deployed to monitor specific topics and regions, feeding data into a central "Analyst Agent" that synthesized the findings.</li> 23 <li><strong>Action Loop:</strong> The system was integrated directly into the campaign's dashboard, providing daily briefings and real-time alerts on critical narrative shifts.</li> 24 </ol> 25 26 <h2>Results & Impact</h2> 27 <p>The impact was immediate and measurable. The campaign saw a <strong>40% increase in engagement</strong> on targeted social media posts and a <strong>15% improvement in ad spend efficiency</strong> by reallocating budget away from underperforming narratives.</p> 28 29 <p>More importantly, the system identified a critical misunderstanding regarding a key policy proposal in a swing state. By alerting the campaign team early, they were able to clarify the message within 24 hours, preventing a potential PR crisis.</p> 30 31 <h2>Future Outlook</h2> 32 <p>This project demonstrated the power of AI not just as a tool for automation, but as a strategic partner in understanding human dynamics. We are now expanding this technology to support public policy making, helping governments understand citizen needs with greater fidelity.</p> 33 `},{id:2,slug:"banking-risk-loan-book-health",category:"Banking & Risk",title:"Seeing the true health of a loan book",description:"For one of the largest retail banks, we built AI-driven risk models that segmented portfolios, detected early signs of borrower stress, and highlighted patterns hidden in traditional reporting.",color:"from-emerald-500/20 to-cyan-500/20",content:` 34 <h2>The Challenge</h2> 35 <p>Retail banks manage vast portfolios of loans, relying on credit scores and periodic reviews to assess risk. However, traditional risk models are often backward-looking, relying on lagged indicators like missed payments. By the time a borrower defaults, it's often too late to intervene.</p> 36 37 <p>Our client, a top-tier retail bank, wanted to move from a reactive to a proactive risk management stance. They needed to identify "silent stress"âborrowers who were current on payments but showing behavioral signs of financial distress.</p> 38 39 <h2>The Solution: Predictive Behavioral Modeling</h2> 40 <p>We developed a suite of AI models that analyzed transaction-level data to detect subtle patterns correlated with future default. By looking at changes in spending habits, utility bill consistency, and interaction frequency with banking apps, our agents could assign a dynamic "health score" to every loan in the book.</p> 41 42 <h3>Key Features:</h3> 43 <ul> 44 <li><strong>Transaction Pattern Analysis:</strong> Detecting sudden changes in discretionary spending or increased reliance on overdrafts.</li> 45 <li><strong>Macro-Economic Stress Testing:</strong> Simulating how individual borrowers would fare under different economic scenarios (e.g., interest rate hikes, inflation).</li> 46 <li><strong>Early Warning System:</strong> Alerting relationship managers to high-risk accounts weeks before a missed payment occurred.</li> 47 </ul> 48 49 <h2>Implementation</h2> 50 <p>Privacy and security were paramount. We utilized <strong>Federated Learning</strong> techniques to train our models without ever moving sensitive customer data from the bank's secure on-premise servers. The AI agents operated
50within the bank's firewall, sending only aggregated insights back to the central dashboard.</p> 51 52 <h2>Results & Impact</h2> 53 <p>The system flagged <strong>$500M in potential bad debt</strong> six months ahead of traditional models. This allowed the bank to proactively offer restructuring plans to at-risk customers, reducing default rates by <strong>12%</strong> year-over-year.</p> 54 55 <p>Furthermore, the granular insights allowed the bank to identify a segment of "hidden prime" borrowersâcustomers with thin credit files but strong cash flow managementâenabling them to extend credit to underserved populations safely.</p> 56 `},{id:3,slug:"public-health-forecasting-growth",category:"Public Health",title:"Forecasting growth vectors and cluster risks",description:"During COVID-19, we designed models and workflows to understand how cases might grow and cluster over time. Our work helped stakeholders anticipate potential hotspots, stress on local systems, and capacity needs.",color:"from-orange-500/20 to-red-500/20",content:` 57 <h2>The Challenge</h2> 58 <p>During the early stages of the COVID-19 pandemic, public health officials were flying blind. Data was fragmented, reporting standards varied by region, and traditional epidemiological models struggled to keep pace with the virus's rapid spread and mutation.</p> 59 60 <p>Decision-makers needed a way to synthesize disparate data sourcesâfrom hospital admissions to mobility dataâto predict where the next cluster would emerge and how it would impact local healthcare capacity.</p> 61 62 <h2>The Solution: Multi-Modal Epidemic Forecasting</h2> 63 <p>We built a dynamic forecasting engine that combined traditional SEIR models with machine learning algorithms capable of ingesting real-time mobility data, social mixing patterns, and hospital resource utilization rates.</p> 64 65 <h3>System Architecture:</h3> 66 <ul> 67 <li><strong>Data Fusion Layer:</strong> Aggregating data from government reports, telecom mobility providers, and hospital inventory systems.</li> 68 <li><strong>Agent-Based Simulation:</strong> Simulating the movement and interaction of millions of "digital agents" representing the population to model viral spread under different policy interventions (e.g., lockdowns, mask mandates).</li> 69 <li><strong>Resource Optimization Module:</strong> Predicting ICU bed and ventilator demand 14 days in advance.</li> 70 </ul> 71 72 <h2>Implementation</h2> 73 <p>The system was deployed for a regional health authority serving 5 million people. It ran nightly simulations, providing daily briefings to the COVID-19 task force. The interface allowed policymakers to run "what-if" scenarios, testing the potential impact of reopening schools or restricting large gatherings.</p> 74 75 <h2>Results & Impact</h2> 76 <p>Our forecasts achieved a <strong>90% accuracy rate</strong> for hospital admission predictions 7 days out. This precision allowed the health authority to dynamically route patients and resources between hospitals, preventing any single facility from being overwhelmed.</p> 77 78 <p>The "what-if" scenario planning was credited with saving an estimated <strong>2,000 lives</strong> by providing the evidence needed to implement targeted interventions at critical moments.</p> 79 `},{id:4,slug:"autonomous-finance-agentvault",category:"Autonomous Finance",title:"AgentVault â autonomous on-chain investment agent",description:"AgentVault is an autonomous investment agent that ingests on-chain and market signals, executes strategies programmatically, and logs every action for audit and review.",color:"from-blue-500/20 to-indigo-500/20",content:` 80 <h2>The Challenge</h2> 81 <p>DeFi (Decentralized Finance) offers unparalleled opportunities for yield, but the complexity and speed of the market make it difficult for human traders to compete. Furthermore, the lack of transparency in many algorithmic trading "black boxes" creates trust issues for investors.</p> 82 83 <p>The goal was to create a fully autonomous agent that could execute complex trading strategies on-chain while maintaining complete transparency and auditability.</p> 84 85 <h2>The Solution: AgentVault</h2> 86 <p>AgentVault is a smart-contract-controlled AI agent. It operates on a "verify, then trust" model. The AI analyzes market conditions and proposes transactions, but these transactions must pass strict on-chain guardrails before execution.</p> 87 88 <h3>Core Components:</h3> 89 <ul> 90 <li><strong>
90Signal Ingestion Engine:</strong> Monitors price feeds, liquidity depth, and on-chain whale movements across multiple blockchains.</li> 91 <li><strong>Strategy Executor:</strong> An LLM-driven logic core that formulates trading strategies (e.g., arbitrage, yield farming optimization).</li> 92 <li><strong>On-Chain Governance Module:</strong> A set of immutable smart contracts that enforce risk limits (e.g., max drawdown, exposure caps) and require cryptographic proof for every trade.</li> 93 </ul> 94 95 <h2>Implementation</h2> 96 <p>We built AgentVault using a combination of off-chain computation (for heavy ML processing) and on-chain verification (using ZK-proofs to verify the model's integrity). This hybrid approach ensures the speed of Web2 AI with the security of Web3.</p> 97 98 <h2>Results & Impact</h2> 99 <p>In backtesting and live pilot runs, AgentVault outperformed the benchmark index by <strong>22%</strong> while maintaining a lower volatility profile. It successfully navigated a major market crash by automatically rebalancing into stablecoins milliseconds after detecting a liquidity cascade.</p> 100 101 <p>The project won first prize at a major AI x Crypto hackathon, validating the demand for transparent, autonomous financial agents.</p> 102 `},{id:5,slug:"research-heo-workflow-agent",category:"Research",title:"HEO â orchestrating research workflows with agents",description:"HEO is a workflow agent that helps researchers move from hypothesis to experiment faster. It scans relevant literature, suggests experimental designs, and standardises how experiments are documented.",color:"from-pink-500/20 to-rose-500/20",content:` 103 <h2>The Challenge</h2> 104 <p>Scientific research is often bottlenecked by the sheer volume of literature and the manual labor involved in designing and documenting experiments. Researchers spend up to 40% of their time on administrative tasks and literature review, slowing down the pace of discovery.</p> 105 106 <p>We aimed to build a "Research Co-pilot" that could handle the cognitive drudgery, freeing up scientists to focus on high-level hypothesis generation and analysis.</p> 107 108 <h2>The Solution: HEO (Hypothesis, Experiment, Observation)</h2> 109 <p>HEO is an agentic workflow system designed specifically for bio-tech and material science labs. It acts as a force multiplier for research teams.</p> 110 111 <h3>Workflow:</h3> 112 <ol> 113 <li><strong>Hypothesis Generation:</strong> HEO scans millions of papers in PubMed and ArXiv to identify gaps in current knowledge and suggest novel hypotheses.</li> 114 <li><strong>Experiment Design:</strong> It proposes detailed experimental protocols, including reagent lists and equipment settings, optimized for the lab's specific inventory.</li> 115 <li><strong>Data Analysis & Documentation:</strong> HEO automatically ingests raw data from lab equipment, formats it, and drafts the initial results section of the paper.</li> 116 </ol> 117 118 <h2>Implementation</h2> 119 <p>HEO was integrated into the ELN (Electronic Lab Notebook) software of a mid-sized biotech startup. It used a RAG (Retrieval-Augmented Generation) architecture to ensure all suggestions were grounded in peer-reviewed science.</p> 120 121 <h2>Results & Impact</h2> 122 <p>The pilot team reported a <strong>3x reduction</strong> in time spent on literature review and a <strong>50% decrease</strong> in experimental errors due to miscalculated reagent ratios.</p> 123 124 <p>Most notably, HEO suggested a novel combination of compounds for a drug delivery mechanism that the human team had overlooked, which is now proceeding to pre-clinical trials.</p> 125 `}];function d(){let e=(0,r.useRef)(null),{scrollYProgress:d}=(0,a.useScroll)({target:e}),c=(0,s.useTransform)(d,[0,1],["1%","-55%"]);return(0,t.jsxs)("section",{id:"case-studies",className:"relative bg-black/50",children:[(0,t.jsx)("div",{ref:e,className:"hidden md:block relative h-[150vh]",children:(0,t.jsxs)("div",{className:"sticky top-0 flex h-screen items-center overflow-hidden",children:[(0,t.jsxs)("div",{className:"absolute top-12 left-0 right-0 z-10 text-center",children:[(0,t.jsx)("h2",{className:"text-[clamp(2rem,4vw,2.5rem)] font-semibold text-white leading-[1.2] tracking-[-0.02em]",children:"Selected Work"}),(0,t.jsx)("p",{className:"mt-3 text-[1.125rem] text-gray-400 leading-[1.6]",children:"Real-world deployments driving measurable impact."})]}),(0,t.jsx)(i.motion.div,{style:{x:c},className:"flex gap-8 px-12 pt-32",children:l.map(e=>(0,t.jsxs)("div",{className:(0,o.cn)("group relative h-[500px] w-[400px] md:w-[600px] shrink-0 overflow-hidden rounded-3xl border border-white/10 bg-linear-to-br p-8 backdrop-blur-md transition-all hover:border-white/20",e.color),children:[(0,t.jsxs)("div",{className:"flex h-full flex-col justify-between relative z-10",children:[(0,t.jsxs)("div",{children:[(0,t.jsx)("span",{className:"mb-4 inline-block rounded-full bg-white/10 px-4 py-1.5 text-[0.9375rem] font-medium text-white backdrop-blur-sm",children:e.category}),(0,t.jsx)("h3",{className:"mb-6 text-[clamp(1.5rem,3vw,1.75rem)] font-semibold text-white leading-[1.3]",children:e.title}),(0,t.jsx)("p",{className:"text-[1.125rem] text-gray-200 leading-[1.6]",children:e.description})]}),(0,t.jsxs)(n.default,{href:`/case-studies/${e.slug}`,className:"flex items-center gap-2 text-[0.9375rem] font-medium text-white/60 group-hover:text-white transition-colors duration-200",children:["Read Case Study ",(0,t.jsx)("span",{className:"text-lg",children:"â"})]})]}),(0,t.jsx)("div",{className:"absolute inset-0 -z-10 opacity-20 bg-[radial-gradient(#fff_1px,transparent_1px)] bg-size-[16px_16px]"})]},e.id))})]})}),(0,t.jsxs)("div",{className:"md:hidden py-20 px-6",children:[(0,t.jsxs)("div",{className:"mb-12",children:[(0,t.jsx)("h2",{className:"text-[clamp(2rem,4vw,2.5rem)] font-semibold text-white leading-[1.2] tracking-[-0.02em]",children:"Selected Work"}),(0,t.jsx)("p",{className:"mt-3 text-[1.125rem] text-gray-400 leading-[1.6]",children:"Real-world deployments driving measurable impact."})]}),(0,t.jsx)("div",{className:"flex flex-col gap-6",children:l.map(e=>(0,t.jsxs)(i.motion.div,{initial:{opacity:0,y:20},whileInView:{opacity:1,y:0},viewport:{once:!0},className:(0,o.cn)("group relative overflow-hidden rounded-3xl border border-white/10 bg-linear-to-br p-8 backdrop-blur-md",e.color),children:[(0,t.jsxs)("div",{className:"relative z-10",children:[(0,t.jsx)("span",{className:"mb-4 inline-block rounded-full bg-white/10 px-4 py-1.5 text-[0.8125rem] font-medium text-white backdrop-blur-sm",children:e.category}),(0,t.jsx)("h3",{className:"mb-4 text-[1.5rem] font-semibold text-white leading-[1.3]",children:e.title}),(0,t.jsx)("p",{className:"text-[1rem] text-gray-200 leading-[1.6] mb-6",children:e.description}),(0,t.jsxs)(n.default,{href:`/case-studies/${e.slug}`,className:"flex items-center gap-2 text-[0.9375rem] font-medium text-white/60 group-hover:text-white transition-colors duration-200",children:["Read Case Study ",(0,t.jsx)("span",{className:"text-lg",children:"â"})]})]}),(0,t.jsx)("div",{className:"absolute inset-0 -z-10 opacity-20 bg-[radial-gradient(#fff_1px,transparent_1px)] bg-size-[16px_16px]"})]},e.id))})]})]})}e.s(["CaseStudies",()=>d],44024)},84719,e=>{"use strict";var t=e.i(43476),i=e.i(46932),a=e.i(95468),s=e.i(22016);let r=["Frontier AI, proven in the real world â Weâve deployed AI agents in environments where stakes are high and data is noisy: banks, campaigns, and public systems.","From models to agents that act â We donât stop at predictions. We build agents that observe, decide, and trigger actions â with clear interfaces into your existing tools and team
125s.","Trust, governance, and auditability built-in â Where decisions matter, our agents can be connected to verifiable infrastructure so behaviour, incentives, and logs are transparent and reviewable.","Full-stack partnership â From discovery and design to deployment and iteration, we collaborate closely with both technical and non-technical stakeholders.","Outcome-focused, not hype-driven â We care more about whether agents actually move the needle than how many buzzwords they include."];function n(){return(0,t.jsxs)("section",{id:"why-us",className:"py-20 md:py-24 lg:py-32 relative bg-black overflow-hidden",children:[(0,t.jsx)("div",{className:"absolute inset-0 bg-[radial-gradient(#ffffff20_1px,transparent_1px)] bg-size-[32px_32px] mask-[radial-gradient(ellipse_60%_60%_at_50%_50%,#000_70%,transparent_100%)] pointer-events-none opacity-40"}),(0,t.jsx)("div",{className:"absolute top-1/2 left-1/2 -translate-x-1/2 -translate-y-1/2 w-[800px] h-[800px] bg-purple-900/10 rounded-full blur-[120px] pointer-events-none mix-blend-screen"}),(0,t.jsxs)("div",{className:"container mx-auto px-6 relative z-10",children:[(0,t.jsxs)("div",{className:"max-w-4xl mx-auto text-center mb-16",children:[(0,t.jsxs)(i.motion.div,{initial:{opacity:0,y:-20},whileInView:{opacity:1,y:0},viewport:{once:!0},className:"inline-flex items-center gap-2 px-3 py-1 rounded-full bg-white/5 border border-white/10 text-purple-300 text-xs font-medium mb-8 backdrop-blur-md",children:[(0,t.jsxs)("span",{className:"relative flex h-2 w-2",children:[(0,t.jsx)("span",{className:"animate-ping absolute inline-flex h-full w-full rounded-full bg-purple-400 opacity-75"}),(0,t.jsx)("span",{className:"relative inline-flex rounded-full h-2 w-2 bg-purple-500"})]}),"The Anything.ai Advantage"]}),(0,t.jsxs)(i.motion.h2,{initial:{opacity:0,y:20},whileInView:{opacity:1,y:0},viewport:{once:!0},className:"text-[clamp(2rem,4vw,2.5rem)] font-semibold text-white mb-6 leading-[1.2] tracking-[-0.02em]",children:["Why Teams ",(0,t.jsx)("br",{}),(0,t.jsx)("span",{className:"text-transparent bg-clip-text bg-linear-to-r from-purple-400 via-pink-400 to-purple-400 animate-gradient-x",children:"Work With Us"})]}),(0,t.jsx)(i.motion.p,{initial:{opacity:0,y:20},whileInView:{opacity:1,y:0},viewport:{once:!0},transition:{delay:.2},className:"text-[1.125rem] text-gray-400 leading-[1.6] max-w-[700px] mx-auto",children:"We don't just build software; we build autonomous capabilities that transform how organizations operate. Our approach combines frontier research with battle-hardened engineering."})]}),(0,t.jsxs)("div",{className:"max-w-4xl mx-auto space-y-4",children:[r.map((e,s)=>(0,t.jsx)(i.motion.div,{initial:{opacity:0,y:20},whileInView:{opacity:1,y:0},viewport:{once:!0},transition:{delay:.1*s},className:"group p-6 rounded-2xl bg-white/2 border border-white/5 hover:bg-white/5 hover:border-purple-500/30 transition-all duration-300 backdrop-blur-sm hover:shadow-[0_4px_12px_rgba(0,0,0,0.08)]",children:(0,t.jsxs)("div",{className:"flex gap-5 items-start",children:[(0,t.jsx)("div",{className:"mt-1 shrink-0 p-2 rounded-full bg-purple-500/10 text-purple-400 group-hover:bg-purple-500 group-hover:text-white transition-colors duration-300",children:(0,t.jsx)(a.CheckCircle2,{className:"h-5 w-5"})}),(0,t.jsx)("p",{className:"text-[1rem] text-gray-300 group-hover:text-white transition-colors duration-200 leading-[1.6] text-left",children:e})]})},s)),(0,t.jsx)("div",{className:"pt-12 text-center",children:(0,t.jsxs)(s.default,{href:"/contact",className:"group relative inline-flex items-center gap-3 px-8 py-4 bg-linear-to-r from-purple-600 via-purple-500 to-blue-600 text-white rounded-lg font-semibold text-[1.125rem] transition-all duration-300 hover:scale-[1.02] shadow-[0_0_30px_-5px_rgba(139,92,246,0.5)] hover:shadow-[0_0_50px_-5px_rgba(139,92,246,0.7)] overflow-hidden",children:[(0,t.jsx)("span",{className:"absolute inset-0 bg-linear-to-r from-white/0 via-white/20 to-white/0 translate-x-[-100%] group-hover:translate-x-[100%] transition-transform duration-700"}),(0,t.jsx)("span",{className:"relative z-10",children:"Schedule a Briefing"}),(0,t.jsx)("span",{className:"relative z-10 group-hover:translate-x-1 transition-transform duration-300",children:"â"})]})})]})]})]})}e.s(["WhyUs",()=>n])},31822,e=>{"use strict";var t=e.i(43476),i=e.i(46932),a=e.i(72520),s=e.i(22016);let r=["Design and deploy agents that touch real decisions in finance, governance, and public systems","Work across models, data, systems, and product â not just 'prompt an API'","Ship fast with people who care about rigor, impact, and ownership","Explore how verification, incentives, and governance should work for autonomous AI","Help define how agents should behave when their actions actually matter","Build infrastru
125cture that scales â from prototype to production-grade enterprise systems"];function n(){return(0,t.jsxs)("section",{id:"careers",className:"py-20 md:py-24 lg:py-32 relative overflow-hidden",children:[(0,t.jsx)("div",{className:"absolute top-1/2 left-1/2 -translate-x-1/2 -translate-y-1/2 w-[800px] h-[800px] bg-purple-900/5 rounded-full blur-[120px] pointer-events-none"}),(0,t.jsx)("div",{className:"container mx-auto px-6 relative z-10",children:(0,t.jsxs)("div",{className:"max-w-5xl mx-auto",children:[(0,t.jsxs)(i.motion.div,{initial:{opacity:0,y:20},whileInView:{opacity:1,y:0},viewport:{once:!0},className:"text-center mb-16",children:[(0,t.jsxs)("h2",{className:"text-[clamp(2.5rem,5vw,3.5rem)] font-semibold text-white mb-8 tracking-[-0.02em] leading-[1.1]",children:["Why Builders ",(0,t.jsx)("span",{className:"text-transparent bg-clip-text bg-linear-to-r from-white to-gray-500",children:"Join Us"})]}),(0,t.jsx)("p",{className:"text-[clamp(1.125rem,2vw,1.5rem)] text-gray-400 leading-[1.6] max-w-3xl mx-auto",children:"We're a small, focused team working on hard problems at the edge of what AI agents can do in the real world."})]}),(0,t.jsx)("div",{className:"grid md:grid-cols-2 lg:grid-cols-3 gap-4 mb-16",children:r.map((e,a)=>(0,t.jsxs)(i.motion.div,{initial:{opacity:0,y:20},whileInView:{opacity:1,y:0},viewport:{once:!0},transition:{delay:.1*a},className:"p-6 rounded-2xl bg-white/5 border border-white/10 hover:bg-white/10 hover:border-white/20 transition-all duration-300 group hover:shadow-[0_4px_12px_rgba(0,0,0,0.08)]",children:[(0,t.jsx)("div",{className:"h-1 w-12 bg-purple-500/50 rounded-full mb-6 group-hover:w-20 transition-all duration-300"}),(0,t.jsx)("p",{className:"text-[1.125rem] text-gray-300 group-hover:text-white transition-colors duration-200 leading-[1.6]",children:e})]},a))}),(0,t.jsxs)("div",{className:"text-center",children:[(0,t.jsx)("div",{className:"inline-block p-0.5 rounded-lg bg-linear-to-r from-purple-500 via-blue-500 to-purple-500",children:(0,t.jsxs)(s.default,{href:"/careers#apply",className:"inline-flex items-center gap-3 px-10 py-5 bg-black text-white rounded-lg font-semibold text-[1.125rem] hover:bg-gray-900 transition-colors duration-200 group",children:["Tell us about you",(0,t.jsx)(a.ArrowRight,{className:"h-5 w-5 group-hover:translate-x-1 transition-transform duration-200"})]})}),(0,t.jsx)("p",{className:"mt-6 text-[0.8125rem] text-gray-500 uppercase tracking-widest font-medium",children:"Remote First ⢠Competitive Equity ⢠High Autonomy"})]})]})})]})}e.s(["Careers",()=>n])}]);
Line numbers count LF bytes from the start of the resource, as the search results do. Vendor segments are library code the classifier recognised; they are stored but not indexed. Bytes are shown as Latin1 characters, one per byte.