Platform Engineering Insights

Build once. Reuse many times. Differentiate where it counts! For years, engineering organizations have excelled at delivering customer-specific projects. But as complexity increases and development cycles shorten, the next competitive advantage isn’t building more project or individual customized products it’s building smarter platforms and modules. The shift from project and product engineering to modular, scalable modules, reusable libraries and pre-fab modules, deployment tools, and configurable products is no longer just an engineering initiative. It’s a business strategy. By embracing the 80/20 principle, we can standardize the 80% that is common across applications while focusing engineering expertise on the 20% that truly differentiates the customer. For products the key is late customization based on standardized modular design. The benefits are compelling: • Faster time-to-market through proven, reusable building blocks. • Higher software and product quality and reliability with continuously improved modules. • Lower engineering effort and maintenance costs. • Easier scalability across products, regions, and industries. • More time spent creating customer value instead of reinventing existing solutions. Standardization should never mean commoditization. Quite the opposite. When the foundation is modular and reusable, engineering teams are free to innovate where it matters most—solving unique customer challenges and creating meaningful differentiation. This drives value creation and delivers competitive advantage! The winners in the next decade won’t be those who write the most code or customized solutions. They’ll be the organizations that build the best reusable engineering ecosystem, enabling speed, quality, and innovation at scale. AI usage is supporting this approach even stronger based on the use of tested, validated and documented software modules. #EngineeringLeadership #SoftwareEngineering #PlatformEngineering #ModularDesign #ProductDevelopment #Innovation #DigitalTransformation #IndustrialAutomation #SoftwareArchitecture #ContinuousImprovement

FINALLY. The world's first true stochastic climate physical-risk system goes OPEN SOURCE. Today RiskThinking.ai is launching the Climate Risk Commons(TM): a not-for-profit, founded and funded by RiskThinking.AI, that puts the same enterprise-grade physical climate-risk platform the world's largest banks use into your hands. Free for non-commercial research. Nothing in it is a demo. What you get: ▪ The same engine that powers the world's largest financial institutions, reached through CDTexpress: 50+ climate hazards, 241 billion geospatially aligned locations, 193 countries, all IPCC emissions scenarios, 15-time horizons to 2100. ▪ The Open Climate Risk Framework™ (OCRF): the spec, asset schema, and contracts that make results comparable across teams, firms, and vendors. ▪ Ecofusion, the open reference pipeline. It runs the entire standard end-to-end on a laptop or a cluster. Apache 2.0, with terms you can hand to your legal office. ▪ Python and R SDKs, sample notebooks, a validated impact-function library, a conformance test suite, and full documentation. ▪ An environment stocked with open datasets, plus the freedom to upload your own data, models, and damage functions. What you can do with it, often in an afternoon: ▪ Load real asset or loan-book addresses and get flood, heat, and wildfire exposure for the assets you hold, not a headline figure. ▪ Stand up a CMIP7 stress test: stochastic, multi-pathway, thousands of scenarios, in a structure a supervisor can read and an auditor can follow. ▪ Design and test new damage functions, then publish results anyone can replicate, because everyone is on the same schema, conformance suite, and engine. ▪ Screen a city, infrastructure plan, or development portfolio for priority physical risks and share the method with anyone who needs it. ▪ Be among the first to work on CMIP7, the new generation of climate science. CMIP6 data stopped at 2014. One thing you do not get, because clarity cuts both ways: our proprietary high-fidelity commercial data. That stays with paying customers, and its revenue is what sustains the open platform. Same machinery, different data. That honesty is the deal. Nineteen institutions are already on board: universities and research institutes across Canada, the UK, Germany, Switzerland, Cyprus, Israel, Sweden, and Brazil, alongside the Central Bank of Brazil. Membership is free for non-commercial research. Individual researchers apply with proof of institution and a sentence on intended use. A faculty or university can enrol everyone at once with a short letter of intent. Details: www.riskthinking.org Research access: academic@riskthinking.ai   Open-source community: opensource@riskthinking.ai

💣 In for a contrarian view: "#Insurtechs fail at scaling #microinsurance" That’s according to research by Yannick Perticone and Jean-Christophe Graz, recently published in Cambridge University Press. The authors argue that Insurtech’s promises fall short of expectations because of the contradiction between the principles of platform scalability and insurance risk pooling. ___ Here's what the authors argue in this paper: Insurance is based on pooling risks, whereas insurtech platforms are often premised on unpooling risks. These two opposing forces create a challenging case for insurtech as a viable enabler for microinsurance. The study highlights three key dimensions of digital insurance that, while central to insurtech platforms, may also hinder their success: 🔗 Interoperability Insurtechs rely on data from other parties, such as MNOs. Due to data protection regulations and practices, the data they receive is often limited, which could result in mispricing of insurance solutions. 💴Valuation Platform owners collect data for specific purposes, which may not be sufficient for accurate risk pricing in insurance. This mismatch between the data needed for risk assessment and what is actually available may lead to incorrect pricing and, ultimately, incorrect premiums. ⚛️ Aggregation Insurtechs atomise risk pools, breaking them into smaller segments to facilitate precise risk profiling. However, this reduces the pool size, undermining the fundamental principle of insurance as a solidarity risk-sharing mechanism, which may lead to higher, not lower prices. According to the authors, these three challenges make insurtechs an unlikely solution for the growth of inclusive or microinsurance. _____ ⁉️ Do you agree? This is certainly one of the more thought-provoking views I’ve shared here. While I may not fully align with the author’s conclusions, I believe it raises important points worth discussing. I’m eager to hear your thoughts and learn from your perspectives. 𝘛𝘩𝘦 𝘩𝘪𝘨𝘩𝘭𝘪𝘨𝘩𝘵𝘴 𝘪𝘯 𝘵𝘩𝘦 𝘳𝘦𝘴𝘦𝘢𝘳𝘤𝘩 𝘱𝘢𝘱𝘦𝘳 𝘢𝘳𝘦 𝘮𝘪𝘯𝘦. 𝘈 𝘭𝘪𝘯𝘬 𝘵𝘰 𝘵𝘩𝘦 𝘴𝘰𝘶𝘳𝘤𝘦 𝘪𝘴 𝘪𝘯 𝘵𝘩𝘦 𝘤𝘰𝘮𝘮𝘦𝘯𝘵𝘴. ━━━━━━━━━━━━━━━ 🤝 Hi, I'm Bert. I'm passionate about staying up-to-date with the latest in #microinsurance. I'd be delighted to connect and exchange insights with you. 🚀 With the annual Microinsurance Master Master accelerator program, we are on a mission to help organisations thrive. Join us in March 2025 to make a difference in the business of reducing the risks of low-income communities. ⠀

𝐄𝐯𝐞𝐫𝐲𝐨𝐧𝐞 𝐰𝐚𝐧𝐭𝐬 𝐀𝐈. 𝐕𝐞𝐫𝐲 𝐟𝐞𝐰 𝐢𝐧𝐬𝐮𝐫𝐞𝐫𝐬 𝐚𝐫𝐞 𝐩𝐫𝐞𝐩𝐚𝐫𝐞𝐝 𝐟𝐨𝐫 𝐰𝐡𝐚𝐭 𝐀𝐈 𝐚𝐜𝐭𝐮𝐚𝐥𝐥𝐲 𝐞𝐱𝐩𝐨𝐬𝐞𝐬. I see this pattern everywhere right now. Insurance leaders rush to deploy AI models for underwriting, claims, or pricing. The models perform well in testing. Everyone celebrates the innovation. Then reality hits. AI doesn't hide data problems. It amplifies them. That clean dataset you thought was ready? AI finds the gaps instantly. Those manual overrides your team made for years? AI reveals the inconsistencies. That tribal knowledge sitting in someone's head? AI exposes how much you've been depending on it. Here's what most insurers miss: AI implementation isn't a technology project. It's an organisational mirror. When AI starts making recommendations, it forces uncomfortable questions: • Why do we have three different definitions for the same risk factor? • Why does our data quality drop after the first renewal? • Why can't we explain this pricing exception from 2019? • Why do different teams use completely different assumptions? These questions existed before AI. We just didn't have to answer them. The insurers winning with AI in 2026 aren't the ones with the fanciest models. They're the ones willing to fix what AI reveals. They treat AI deployment as a forcing function for organisational clarity. Before launching the next AI initiative, ask yourself: • Are we ready to face what our data actually looks like? • Can we handle the transparency AI will create? • Do we have the discipline to fix foundational issues before scaling? AI won't transform your business if your business isn't ready to transform itself first. What's the hardest truth AI has revealed in your organisation? #AIinInsurance #InsuranceLeadership #InsurTech #DigitalTransformation #DataStrategy

After 20 years in insurance operations, I'm seeing a fundamental shift that most carriers are missing. The old playbook for operations was simple: offshore repetitive tasks, optimize cost-per-FTE, measure efficiency in headcount reduction. That playbook is dead. The new reality: Modern insurance operations is about identifying where to apply #AI and #automation to shift from linear to non-linear delivery models. The winners aren't competing on labor costs — they're competing on which use cases actually move the needle. Three things I'm seeing in the market: 1. #Gen AI and #Agentic AI are moving into production — selectively The best outcomes aren't "AI everywhere." They're targeted deployments in underwriting exceptions, claims triage, and policy admin workflows where AI handles volume and humans handle complexity. Companies trying to automate everything are failing. 2. Vendor AI solutions promise near-perfect accuracy. Production reality is 60-80% on average. Every vendor demo shows flawless outcomes. Then you deploy and accuracy drops because real insurance data is inconsistent, incomplete, and full of edge cases the model never saw in training. Carriers struggle to evaluate which solutions actually work vs. which just performed well on sanitized demo data. The gap isn't the technology — it's understanding your specific data quality and process reality. 3. AI companies don't factor in domain and process nuances Tech firms building AI for insurance treat underwriting, claims, and policy admin as generic document processing problems. They're not. Each has decades of business rules, regulatory requirements, and process exceptions that AI models trained on generic data completely miss. The companies winning are those that combine AI capabilities with deep insurance domain expertise. The carriers figuring this out are seeing 40%+ efficiency improvements while improving customer experience. The ones stuck in 2015 thinking are bleeding market share. What am I missing? If you're operating in insurance or building technology for insurance, what's the reality gap between vendor promises and production results? #InsuranceTechnology #AIinInsurance #InsuranceOperations #GenAI

I'm incredibly excited to finally share publicly the output of months of hard work from our entire team at Patch. We’ve been thinking deeply about what it will really take to unlock the potential of the voluntary carbon market. We know that there are billions of dollars on the sidelines, and when we talk to carbon credit buyers, we’re hearing the same three challenges: 1. Fragmentation in the market is making it way too difficult to find the right credits at a fair price. Fundamentally, this is fragmentation of data: how many credits are available for a project? What are the overall pricing trends among all suppliers of that credit or type? 2. And then there’s the fragmentation of MRV data, project data, integrity data, ratings data, etc. This makes it incredibly expensive to diligence any single project — let alone a portfolio — in a reasonable period of time. The stakes are high. Funding the wrong project can damage your sustainability program. Taking too long means you can miss out on fast-moving inventory. 3. Lastly, the entire buyer journey is profoundly inefficient. Sourcing, diligence, procurement — these are resource-intensive workflows, heavily reliant on expertise and collaboration among internal and external parties. They don’t just incrementally increase the costs of a carbon program — they can fundamentally break it. This is what our customers are telling us — Workday, Autodesk, Bain & Company, Capgemini, Deutsche Telekom, and many more. That’s why I’m so proud to launch our all-new end-to-end carbon credit platform — we’re helping them solve these big challenges. From strategy to sourcing to diligence to purchase to management, Patch brings in comprehensive data, human expertise, and AI-powered software at every key decision point and workflow of your carbon program. I wrote a blog taking you behind the new platform and explaining our choices. Link is in the comments below.

𝐆𝐞𝐫𝐦𝐚𝐧 𝐡𝐞𝐚𝐥𝐭𝐡 𝐢𝐧𝐬𝐮𝐫𝐞𝐫𝐬 𝐚𝐫𝐞 𝐬𝐢𝐭𝐭𝐢𝐧𝐠 𝐨𝐧 𝐚 𝐩𝐚𝐫𝐚𝐝𝐨𝐱. They’ve never had more data yet they’ve never been further from being able to use it. In recent discussions, and in my own years of experience in the industry, I've seen a contstant trend that doesn't seem to change. Most insurers are roughly 18-24 months behind where they need to be on data readiness. Not because of a lack of strategy, but because of a lack of execution bandwidth. 𝐓𝐡𝐞 𝐬𝐚𝐦𝐞 𝐩𝐚𝐭𝐭𝐞𝐫𝐧 𝐬𝐡𝐨𝐰𝐬 𝐮𝐩 𝐞𝐯𝐞𝐫𝐲𝐰𝐡𝐞𝐫𝐞: ▪️ Legacy systems that can’t keep up with new regulatory and reporting demands ▪️ Manual processes that create delay, cost, and risk ▪️ Fragmented data estates that make even basic analytics slow and unreliable ▪️ Modernisation initiatives that stall because internal teams are already overloaded This gap is widening. For insurers competing on product innovation, pricing accuracy, and operational efficiency, it’s becoming a strategic problem. Here’s the hopeful part: the issue is capacity, not capability. Health insurers need delivery support that can cut through the complexity and actually land the programmes that have been sitting on roadmaps for years. That’s where we come in. We build agile, insurance-specific project teams covering data engineering, analytics, platform modernisation, and programme delivery to help insurers make real progress fast. No “bench”, no bloated models, no endless steering committees. Just focused delivery. The outcome? iO Associates provides faster delivery with lower risk and cost than traditional consulting firms... that some of our customers are tired of using. #Insurance #Germany #Consulting #DataModernisation

Most of the teams I spoke with have modernized their data warehouses and deployed AI models. Very few can tell me why their engineers are still spending half their time fixing data breaks. The gap is operational. Data infrastructure modernizes for storage and analytics. AI models train and deploy. But the handoff between them stays manual; engineers discover quality issues only after dashboards misfire or models' performance gets worse. Cummins automated classification of over 1 million files using AI-powered governance in Microsoft Purview, reducing manual record work by 10–15%.    AI teams finally knew datasets marked "safe for ML" actually met quality bars, eliminating surprise incidents at model time. Three operational changes that closed the gap: - Set an MTTR target for data recovery: how long from detecting an issue to clean data flowing again. Track it alongside model performance so both teams share the same goal. - Publish contracts for your core metrics: what's included, the calculation, and time windows. Gate changes, so a metric can't ship without documentation. - Shadow test before release: run new logic quietly next to production. Set a threshold for acceptable variance and keep rollback ready. I break down where the handoff breaks and which gates prevent incidents from compounding in this week's Simform Newsletter. Link is in the bio.

KanataQ Sustainability Solutions Ecosystem I am pleased to share the first master mapping of KanataQ solutions ecosystem: ➡️ Climate Action Software and advisory solutions focused on greenhouse gas emissions management, net-zero planning, carbon accounting, carbon offsets, and climate risk analysis. ➡️ Governance & Accountability Governance-focused tools and advisory services providing support for proxy voting, compliance, ethical practices, and ESG engagement. ➡️ Social Equity & Inclusion Solutions designed to support diversity, equality, and human rights initiatives within organizations and communities, including tools for tracking inclusion metrics and implementing equitable practices. ➡️ Biodiversity & Natural Capital Advisory and software solutions that assist in evaluating and mitigating business impacts on biodiversity, enabling the sustainable management of ecosystems and natural capital. ➡️ Sustainable Finance Technology and advisory services offering ESG-integrated investment tools, green bond issuance support, and data-driven management solutions for sustainable financial decision-making. ➡️ Reporting & Disclosure Platforms and consulting services for ESG reporting and disclosures, ensuring alignment with frameworks like GRI, CSRD, SASB, CDP, IFRS/ISSB, taxonomies, and regulatory requirements. ➡️ Digital Solutions & Analytics Software solutions leveraging data-driven tools for ESG risk analysis, ratings, benchmarking, stakeholder data management, and custom analytics to improve sustainability performance. ➡️ Sustainable Operations Advisory and technology offerings that provide holistic ESG management solutions, including tools for resource efficiency (e.g., water and energy), green building management, and supplier engagement. ➡️ Education & Training Platforms and advisory programs offering targeted training, workshops, and educational content to build sustainability expertise within organizations and industries. ➡️ Sustainability Communication Tools and services that enhance the communication of sustainability efforts and initiatives ➡️ Impact & Stakeholders Advisory and software solutions for assessing and managing the social and environmental impacts of business activities, as well as tools for stakeholder engagement and collaboration. ➡️ Research & Strategy Strategic advisory services and research tools supporting the development of actionable sustainability plans, including screening, analysis, and alignment with business objectives. To enhance the utility of this mapping and improve the solutions market transparency, we've included the the average starting price for SaaS solutions and the average starting price for project-based solutions within each category. Note: This mapping serves as a category baseline rather than a rigid fencing of our solutions ecosystem. Note 2: If you would your solution to be included in our next mapping, you can join KanataQ by visiting: https://kanataq.com/

Three Norwegian founders moved into a grandmother's 160-year-old house in Stavanger in 2022. When they tried to fix a simple heat pump, the installation process was so painfully slow that they decided to build the platform they wished existed. Two years later, they're powering renewable energy deployment across Europe and setting their sights on the US's $257B opportunity. The Company: Installer.com. An infrastructure platform for renewable energy deployment - connecting manufacturers, installers, and customers in one system. Founders: Gunnar Windsand Sem, Kristoffer Gjerde, Thomas Kristiansen – together they combine renewable energy consulting, AI strategy, supply chain optimization, and hands-on installer experience. The Problem They're Solving: The energy transition is hitting a critical bottleneck. It's not the hardware anymore - it's the complete absence of scalable, digital infrastructure to manage deployment efficiently. Installer.com provides the enabling infrastructure, much like Shopify did for e-commerce. Unlike service aggregators, which require companies to outsource control, Installer.com’s platform empowers businesses to own their installation process and customer experience. Traction: Customer base manages hundreds of thousands of installations annually across 18 European markets. Now executing US market entry with early climate tech traction. Funding: $4.8M total ($4M seed led by Brighteye Ventures, with Futurum Ventures, Sondo, PT1, Startuplab) US Market Opportunity: US EV charging market alone projected at $257B by 2032 and heating, batteries and solar are booming too. The primary barrier isn't hardware—it's scalable installation infrastructure. This is a future they have already navigated. The Nordics lead the world in renewable technology adoption, from EV penetration to heat pumps. They have built and hardened their platform in these highly advanced markets, solving the exact fragmentation, labor shortage, and customer experience challenges that the US is now facing at an unprecedented scale US Entry Strategy: Establish high-profile US customers with the current $4M runway. Series A planned for 2026 with US expansion focus. Going to Houston Energy and Climate Startup Week! Who They Want to Talk To: ·    VCs with portfolio companies requiring product installation ·    ClimateTech advisors (solar, EV charging, batteries, heating) ·    Renewable asset companies (residential/commercial) Want to chat with the team? Let me know or just reach out to the directly. ---------------------------------------------------------------------------- I'm launching a weekly series highlighting Nordic companies that are planning to enter the US market, or are already making waves there. As someone with one foot in each of these ecosystems, strengthening this bridge has become a mission of mine, so I want to connect the great companies I work with everyday with my network back home. Interested in leaning in, just let me know!