Strategic Insight Software

I built a strategy consulting team inside Claude. Not a chatbot. Not a writing assistant. A system that pressure-tests my decisions the way a room full of senior consultants would. Here's how it actually works. Claude lets you build what they call "skills," custom instruction sets that define how the AI approaches specific types of work. I built two that changed how I make decisions. The first is a McKinsey strategy toolkit. It contains 12 consulting frameworks, MECE issue trees, Five Forces, BCG Matrix, Pyramid Principle, scenario planning, and more. When I bring it a messy business problem, it doesn't just brainstorm. It selects the right framework, structures the analysis, and delivers a bottom line, top 3 actions, and the questions I should be asking next. Last month, I used it to evaluate a new market segment. It ran Five Forces, then a competitive landscape, then stress-tested my recommendations with a "So What" framework that flagged every vague phrase and forced specificity. The whole analysis took 45 minutes. It would have taken my team a week to produce something comparable. The second skill is an LLM Council, adapted from Andrej Karpathy's open-source project. When I face a high-stakes decision, I convene five AI advisors, each with a different thinking style. A Contrarian who hunts for fatal flaws. A First Principles Thinker who questions whether I'm solving the right problem. An Expansionist looking for upside I'm missing. An Outsider with zero context who catches my blind spots. An Executor who only cares about what happens Monday morning. They analyze independently. Then they peer-review each other anonymously. Then, the Chairman synthesizes a verdict that shows where they agree, where they clash, and what everyone missed. The insight that makes this work: one AI perspective is a coin flip. Five perspectives with structured disagreement is a strategy session. I'm not replacing my team. I'm showing up to every meeting with my thinking already stress-tested. The quality of my questions went up. The speed of my decisions went up. The number of times I got surprised in a review went down. The real unlock isn't asking AI better questions. It's building systems that force AI to argue with itself before it talks to you. What's the most useful system you've built with AI that goes beyond basic prompting?

Why NotebookLM is so valuable and how you can use it. In a world full of noise, this system does one simple thing: It grounds knowledge in truth. Most models generate answers from endless data. NotebookLM works differently, it builds from what you know. Every insight comes from your own material: reports, notes, contracts, research. It connects what already exists and gives it structure, meaning, and context. The idea came from Steven Johnson, author and Editorial Director at Google Labs. He spent years searching for a tool that could help with the hardest part of thinking: researching, structuring, and making sense. In 2022, his small team turned one simple principle into code: source-grounded AI, a model that expands understanding instead of inventing. The philosophy is: augmentation. NotebookLM helps you understand your own information faster, connect ideas across silos, and extract insights that already live inside your work. How it works and how you can use it: 1. Strategic knowledge work through source-grounding: → Rather than blending probability with creativity, it works entirely inside your uploaded data. Every statement links directly back to the paragraph it came from. → Stanford AI Research measured: about 87% fewer factual errors, around 13% hallucination rate (vs ≈ 40% in open-domain LLMs) Your insights stay anchored, transparent, verifiable, and real. 2. Audit: → Each summary cites its exact line. → Legal, compliance, and due-diligence teams already use it to process sensitive data with full traceability. → Market teams synthesise hundreds of pages into a single, coherent view. 3. Building confidential knowledge capital: → Executives upload strategy decks, M&A analyses, financial reviews, HR notes and transform them into living, query-ready knowledge bases. → Instead of static archives, they gain a searchable memory: patterns, risks, and learnings, all grounded in evidence. 4. Decision quality through verifiable insight: Rather than broad summaries, it delivers evidence-based reasoning with full context. → Which strategic decisions drove the strongest ROI and why? → Which contractual clauses define how data moves across partners and products? → What recurring patterns appear across five years of board discussions — and what do they signal for future priorities? 5. Scale and multimodal depth: One of NotebookLM’s greatest strengths lies in what it can handle. You can upload and combine PDFs, Google Docs, text files, websites, and even YouTube videos, the model reads both transcripts and visual content. Each notebook holds up to 50 sources with hundreds of thousands of words. Where others stop, NotebookLM keeps analysing, connecting, and citing, keeping your knowledge base complete. If you haven’t tried NotebookLM yet, start with your own world. ••• Valuable? Share • Save • Follow Sara Kukovec 🌍🌱🏗️

We built the analytics platform we wished existed when LLMs emerged. When LLMs became viable, we saw a chance to rebuild analytics entirely. Definite understands your data, anticipates questions, and delivers instant answers. Connect Stripe, HubSpot, or Salesforce and within seconds you get automated reports on MRR, churn, and acquisition costs. We arranged it so that there’s no manual setup and or data modeling required. Once you plug in a data source, within seconds you see automatically generated reports slicing key metrics including Monthly Recurring Revenue (MRR), churn, customer acquisition costs, all customized to your context without manual setup or complex data modeling. Consider a fast-growing SaaS company that notices one sales rep with a 50% churn rate, dramatically higher than the team average of 2-3%. That kind of insight normally takes months to realize along the traditional path, which typically involves setting up multiple services like Fivetran for data pipelines, building and maintaining data models, selecting and switching between vendors and stitching it all together. These steps often drag out for six months or more before meaningful insights surface. Definite slashes that timeline down to minutes, providing early detection that prevents costly revenue leaks well before they spiral out of control. Sales teams get prioritized accounts, customer success teams get flags on at-risk customers, and marketing can act immediately on predictive scores. All of this, from onboarding to activation, takes less than 15 minutes to go live. This means you never miss critical insights just because they're sitting unused in dashboards. You get velocity and precision, empowering your teams to make decisions and act at the speed your business demands.

⚡ One of my favorite things about leading a marketing team right now is watching them build leverage. Brittany Gulla launched something that genuinely changes how PMM operates, our Competitive Intelligence Agent. It goes far beyond a static report or a basic monthly recap. Running autonomously in the background, the agent continuously monitors competitor product announcements, roadmap signals, GTM and positioning shifts, funding, M&A, analyst research, and customer momentum. It pulls in the inputs, synthesizes themes, drafts the strategic implications, and flags risk and opportunity for BlueConic. What used to take 20-40 hours each month now takes about two hours of review and refinement. More important than the time savings is the cadence shift because the insight is ongoing, not periodic. Each month the output includes:  📊 The top five market moves with clear implications 🧠 Product strategy inputs on where to close gaps or leapfrog 🎯 Positioning insights on where we are being out-framed 🎙️ A well-designed deck, internal queryable hub and competitive podcasts 🗂️ A searchable archive of all competitive insights 💬 A chat interface to pressure test implications in real time This is what leverage looks like today. Competitive intelligence as an operating system that turns speed to insight into an advantage. Brittany, thank you for the thoughtful and strategic work. 🙏

This is the most underrated way to use Claude: (and it has nothing to do with writing or coding) It's competitive intelligence. Using data that's free, public, and updated every single week. Here's my extract step by step guide: Step 1. Go to claude .ai. Step 2. Select the new Claude "Opus 4.6." Step 3. Turn on "Extended Thinking." Step 4. Use this guide: https://lnkd.in/dVDent-3 Step 5. Pick a competitor. Go to their careers page. Step 6. Copy every open job listing into one doc. (Title. Team name. Location. Full description) Step 7. Save it as one .txt or .docx file. Step 8. Search the company at EDGAR (sec .gov) Step 9. Download its recent 10-K or 10-Q filing. (Official strategy, risks, and financials - all public.) Step 10. Upload both files to Claude Opus 4.6. Step 11. Paste this exact prompt: "You are a competitive intelligence analyst at a rival company. I've uploaded [Company]'s complete current job listings and their most recent SEC filing. Perform a strategic intelligence analysis: → Cluster these roles by what they suggest is being built. Don't use the team names they've listed. Infer the actual product initiatives from the skills, tools, and responsibilities described. → Identify capabilities or teams that appear entirely new — not mentioned anywhere in the SEC filing. These are unreleased bets. → Find roles where seniority is disproportionately high for a new team. This signals executive-level priority. → Cross-reference the SEC filing's Risk Factors and Strategy sections with hiring patterns. Where are they investing against a stated risk? Where did they flag a risk but have zero hiring to address it? → Predict 3 product launches or strategic moves this company will make in the next 6-12 months. State your confidence level and cite specific job titles and filing sections as evidence. Format this as a 1-page competitive intelligence briefing for a CMO." What you'll find: → Products that don't exist yet but will in 6 months. → Priorities that contradict what the CEO said. → Risks they told the SEC but aren't addressing. This is what consulting firms charge $200K for. It took me 10 minutes. I used the new Claude 'Opus 4.6' for a reason: ✦ It read 60 job listing & a 200-page filing together. ✦ And connects dots across both. ✦ It is superior in thinking and context retrieval. That's why I didn't use ChatGPT for this.

Eight weeks ago I got curious about two things: can I codify how my brain does strategy into software? And what does it actually take to build a genuinely AI-native product? Then, I used my own strategy tool to build the strategy for itself. It's called Lunastak. It tackles a problem I see in every company I work with: strategic signal is everywhere — at 3am, between meetings, in a conversation that doesn't click until weeks later — but none of it connects on its own. So when it's time to write the strategy, leaders stare at a blank page. And that deep thinking time to work it all out? That's a myth. Nobody has that. Lunastak is modelled on how I actually do strategy. Feed it anything — docs, memos, conversations — and it extracts meaning, builds on everything it's seen, and structures it into a Decision Stack: Vision, Strategy, Objectives, Opportunities, Principles. Martin Eriksson's mental model, used with every client I've worked with. It just works. This isn't a wrapper around an LLM prompt — it's a proper system that codifies how strategic thinking actually happens. The demo is my actual strategy. I ran my own go-to-market thinking through the tool and published the real output as the public demo. Right now it's one person getting their strategic brain out of their head. When it's a whole leadership team, I think gets really interesting. No sign-up required. Have a look around. Explore Lunastak → https://app.lunastak.io I wrote about the full build — what it took, what I learned, and why craft still matters in AI. Read the full story at the link in comments.

𝙌𝙪𝙚𝙨𝙩𝙞𝙤𝙣𝙨 𝙧𝙖𝙞𝙨𝙚𝙙 𝙢𝙞𝙙-𝙢𝙚𝙚𝙩𝙞𝙣𝙜 𝙜𝙚𝙩 𝙖𝙣𝙨𝙬𝙚𝙧𝙚𝙙 𝙗𝙚𝙛𝙤𝙧𝙚 𝙩𝙝𝙚 𝙨𝙡𝙞𝙙𝙚 𝙘𝙝𝙖𝙣𝙜𝙚𝙨 𝙬𝙞𝙩𝙝 𝙩𝙝𝙞𝙨 𝘼𝙄 𝘁𝗼𝗼𝗹 If you’ve been following AI news recently—you may have noticed the term “MCP” or “Model Context Protocol,” a protocol that allows AI agents to package and use tools and systems in a way that is streamlined for AI. While a few vendors have been quick to implement it (Asana, Atlassian, and the like), the focus still remains on developers (git-MCP, Jira, AWS), and security issues have plagued public implementations of MCPs. We at Aterian have found a surprising and impactful way to use MCPs—𝗮𝗹𝗹𝗼𝘄𝗶𝗻𝗴 𝗔𝗜 𝘁𝗼 𝗰𝗼𝗻𝗻𝗲𝗰𝘁 𝘁𝗼 𝗼𝘂𝗿 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗮𝗻𝗱 𝗱𝗮𝘁𝗮 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 𝘁𝗼 𝗿𝗮𝗽𝗶𝗱𝗹𝘆 𝗮𝗻𝗮𝗹𝘆𝘇𝗲 𝗮𝗻𝗱 𝗱𝗶𝘀𝗽𝗹𝗮𝘆 𝗶𝗻𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻. 𝗪𝗮𝗻𝘁 𝘁𝗼 𝗸𝗻𝗼𝘄 𝗶𝗳 𝗮 𝗿𝗲𝗰𝗲𝗻𝘁 𝗰𝗵𝗮𝗻𝗴𝗲 𝗶𝗻 𝗮 𝗽𝗿𝗼𝗱𝘂𝗰𝘁’𝘀 𝗳𝘂𝗹𝗳𝗶𝗹𝗹𝗺𝗲𝗻𝘁 𝗺𝗲𝘁𝗵𝗼𝗱 𝗶𝗻𝗰𝗿𝗲𝗮𝘀𝗲𝗱 𝗶𝘁𝘀 𝘀𝗮𝗹𝗲𝘀? No problem. Just ask the AI, and within two minutes you’ll receive a comprehensive, ad-hoc dashboard answering your question. 𝗡𝗲𝗲𝗱 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗰 𝗶𝗻𝘀𝗶𝗴𝗵𝘁 𝗮𝗯𝗼𝘂𝘁 𝘆𝗼𝘂𝗿 𝗽𝗼𝗿𝘁𝗳𝗼𝗹𝗶𝗼 𝗼𝗳 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝘀? Ask the AI, and it will quickly analyze sales, inventory patterns, marketing performance, listing views, and even research the web to help you find new avenues to boost your portfolio’s performance. Don’t be mistaken; this is not a single-call, single-answer process. The AI goes on long tangents of analysis, issuing tens of queries to the database, organizing the data, and later writing markdown or code to visualize it for you. 𝗔 𝗽𝗲𝗿𝘀𝗼𝗻𝗮𝗹 𝗱𝗮𝘀𝗵𝗯𝗼𝗮𝗿𝗱 𝗳𝗼𝗿 𝗲𝘃𝗲𝗿𝘆 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻 𝘆𝗼𝘂 𝗮𝘀𝗸. How did we do it? 1. We developed a quick and small MCP that is deployed locally on key employees’ laptops. (We built it internally—it’s not as heavy a lift as it might sound—so we can trust the code and execution environment.). The MCP securely connects to our data platform (Snowflake) that hosts the majority of our business data. 2. We leverage a Claude Team subscription and connect the local MCP directly to Claude Desktop, skipping the need to develop an agent interface and pay for api usage. The result? 𝗧𝗶𝗺𝗲 𝘁𝗼 𝗶𝗻𝘀𝗶𝗴𝗵𝘁 𝘀𝗵𝗼𝗿𝘁𝗲𝗻𝗲𝗱 𝗳𝗿𝗼𝗺 𝗱𝗮𝘆𝘀 𝘁𝗼 𝗺𝗶𝗻𝘂𝘁𝗲𝘀 and more self-serve than ever with zero cloud footprint to manage (other than Snowflake). Just like this, we have already integrated two additional systems into Claude, and we’re working on covering the rest of our business systems the same way—first read-only and, in the future, supervised write. In the near future, 𝗰𝗵𝗮𝘁 𝘄𝗶𝗹𝗹 𝗯𝗲𝗰𝗼𝗺𝗲 𝘁𝗵𝗲 𝗽𝗿𝗶𝗺𝗮𝗿𝘆 𝗶𝗻𝘁𝗲𝗿𝗳𝗮𝗰𝗲 𝘄𝗵𝗲𝗿𝗲 𝘄𝗼𝗿𝗸 𝘄𝗶𝗹𝗹 𝗯𝗲 𝗰𝗮𝗿𝗿𝗶𝗲𝗱 𝗼𝘂𝘁. Work will be done by agents on behalf of humans (see A2A and ACP), and the Internet will become just a highway for AI agents with little human activity.

In 2025 and beyond, top companies won't outperform competitors because their analysts or strategists are smarter. They'll win because their insights leap three steps ahead—and turn into action immediately. Most Market Intelligence (MI) today is still built for reports, not rapid responses. But reports won’t win the race tomorrow. Leading organizations have already upgraded their engines to AI-powered, real-time Market Intelligence. These engines don’t just track market movements—they anticipate them: ✅ Predicting market signals before they spike. ✅ Delivering critical briefings before executives even think to ask. ✅ Embedding actionable insights directly into Slack channels, strategic dashboards, and daily decision-making. What powers this modern approach? 🔹 AI Core (LLMs + Agents): Synthesizes structured and unstructured data, summarizing insights clearly, with complete transparency. 🔹 Orchestration Layer: Triggers adaptable workflows based on roles, events, and urgency of signals. 🔹 Dynamic Feedback Loop: Users continuously refine the system simply by engaging—making insights smarter and sharper. 🔹 Unified Data Architecture: Delivers timely intelligence seamlessly to executives, product leaders, and sales teams alike. 📊 This is no ordinary upgrade. It’s the new operating system of strategic intelligence. 💬 Tell me: Does your market intelligence still live in PDFs, dashboards, or is it embedded directly in your decision-making? 🔁 Share this with someone whose business deserves smarter intelligence for 2025. #MarketIntelligence #AIinBusiness #StrategicForesight #InsightDriven #LLM #DecisionIntelligence #DigitalTransformation #FutureOfWork

𝗔𝗜 𝗮𝘀 𝗬𝗼𝘂𝗿 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗰 𝗔𝗱𝘃𝗮𝗻𝘁𝗮𝗴𝗲 𝗳𝗼𝗿 𝗦𝗺𝗮𝗿𝘁𝗲𝗿 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁💡   In recent projects with DAX40 and leading global enterprises, we have seen how AI empowers executives with a 360° view of their markets – enhancing human strategic judgment and decision-making. The key insight? Only by 𝗹𝗼𝗼𝗸𝗶𝗻𝗴 𝗯𝗲𝘆𝗼𝗻𝗱 𝗶𝗻𝘁𝗲𝗿𝗻𝗮𝗹 𝗱𝗮𝘁𝗮 a CxO can truly grasp the big picture and make the right decisions to steer their organizations toward sustainable success.   Today, I want to highlight three of the AI-powered features included in our Performance Intelligence Suite: 🔹 𝗔𝘀𝘀𝗲𝘀𝘀 𝗠𝗮𝗿𝗸𝗲𝘁 𝗗𝘆𝗻𝗮𝗺𝗶𝗰𝘀: Transforming structured and unstructured data on competition, supply, technology, regulation, and resources into actionable alerts and insights, with AI helping to connect the dots. 🔹 𝗩𝗮𝗹𝗶𝗱𝗮𝘁𝗲 𝗠𝗮𝗿𝗸𝗲𝘁 𝗔𝘀𝘀𝘂𝗺𝗽𝘁𝗶𝗼𝗻𝘀: Extracting key metrics, background information and expert judgement from reports and global data, significantly reducing manual effort, and gaining transparency on previously untapped insights. 🔹 𝗣𝗿𝗲𝗱𝗶𝗰𝘁 𝗠𝗮𝗿𝗸𝗲𝘁 𝗧𝘂𝗿𝗻𝗶𝗻𝗴 𝗣𝗼𝗶𝗻𝘁𝘀: Leveraging internal and external drivers to detect early signals, providing transparency about what drives performance through explainable Machine Learning.   AI is not replacing executives. It is the 𝘀𝗶𝗹𝗲𝗻𝘁 𝘀𝘂𝗽𝗽𝗼𝗿𝘁 that enables strategic future-securing decisions, based on insights that are comprehensive, timely, and actionable.   #HorvathPerformanceIntelligence

In the age of financial complexity, where algorithms run faster than advisors can blink and clients want answers yesterday, Conquest Planning isn’t playing catch-up. They’re dictating tempo. On June 23, the Winnipeg-based fintech dropped a cool $80M USD Series B raise like it was halftime adjustments. Led by Growth Equity at Goldman Sachs Alternatives, with heat from Canapi Ventures, BDC Capital, Citi, TIAA, and USAA, this round brings their total haul north of $100M USD. If you know Dr. Mark Evans, then you know the playbook didn’t just change. It was rebuilt, line by line. The former EISI founder and tech architect of the original NaviPlan, Evans is rewriting modern #financialplanning with the elegance of a physicist and the ruthlessness of a serial entrepreneur. Now as CEO and President of Conquest, he’s bringing the team that’s already built the largest financial planning software empire, and making sure this time, the software thinks faster than the market moves. Over 1.5 million plans created. Used by 1K+ financial institutions. Trusted by 5 of the top 10 banks in North America. And that’s infrastructure. Their proprietary #AI engine, Strategic Advice Manager (SAM), isn’t some sandbox toy. It calculates thousands of variables in real-time and adapts with your life. SAM isn’t your friendly robo, it’s your digital fiduciary, whispering actionable intelligence into advisors’ ears with voice or text. Ask it anything, and it answers like it’s already read your tax returns. And they’re not stopping at advisors with suits and #WallStreet lingo. Conquest is building for every client across the wealth continuum, from first-time homebuyers juggling student debt to UHNW families eyeing legacy structures. The U.S. is next in their crosshairs, and this funding is the rocket fuel for that expansion. SAM Bytes, SAM Insights, Ask SAM, they’re product milestones designed to keep advisors engaged, even when clients want to fly solo. Behind this is a crew with serious horsepower. Sean MacDonald, CPA CA as CFO, Brad Joudrie leading revenue, Tony Bevan driving software development, Stacie Calder, MBA, Angie Brown, Tom Burmeister, and James Teitsma rounding out the bench. With Jade Mandel of Goldman Sachs joining Stephanie Choo from Portage on the board, this is acceleration. Conquest Planning is building trust at scale. And they’re doing it without asking for applause. They know the real metric isn’t funding, it’s the future of advice itself. So yeah, this $80M might make headlines. But the real story? Conquest just turned financial planning from a static report into a living, breathing experience. The rest of the industry? They’re still reading last quarter’s projections. #Startups #StartupFunding #VentureCapital #SeriesB #Fintech #FinancialPlanning #AI #DevTools #RoboAdvisors #WealthManagement #Investment #FinancialPlanning #Technology #Innovation #TechEcosystem #StartupEcosystem If engineering peace of mind is what you crave, Vention is your zen.