Consulting

I used to think charging less would get me more clients. After my trip to the US I realised it just made them trust me less. when i was cheap, clients questioned everything. "why this approach?" "can we try something else?" "i'm not sure about this." so when i raised my rates, they trusted my decisions completely. same work. different psychology. so here's what i've basically realized about pricing: when someone sees a low price, their brain doesn't think "great deal." it thinks "what's the catch?" they start looking for problems. inexperience. desperation. corners being cut. low prices trigger fear of loss, not excitement about savings. but when they see premium pricing, something else happens. "if they can charge this much, they must deliver results." "other people are paying this, so the value must be there." "the risk of not solving this problem costs way more than the investment." premium pricing signals confidence in your work. think about it. rolex doesn't make better watches from a functionality standpoint. but the price tells you everything about what owning one means. same thing with services. a premium project isn't necessarily 10x better in execution. but the price signals experience, systems, proven results. and here's the shift that changed everything for me: i stopped anchoring clients to the price and started anchoring them to the outcome. not "this costs X" but "this will generate Y for your business, and the investment is X." when they're thinking about ROI, the price becomes secondary. your pricing isn't just a number. it's a signal to the market about who you are and what you deliver.

Let's build a Real Time ML System to fraud. Step by step 🧵↓ 𝗧𝗵𝗲 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗽𝗿𝗼𝗯𝗹𝗲𝗺 💼 Every time your credit card is used online by someone (hopefully you), your card issuer (for example Visa, Mastercard or PayPal) has to verify if it is you the person trying to pay with the card. Otherwise, the transaction is blocked. Now the question is: ““𝗛𝗼𝘄 𝗱𝗼𝗲𝘀 𝗩𝗶𝘀𝗮 𝗱𝗼 𝘁𝗵𝗮𝘁?”” And the answer is… a real time ML system! 𝗦𝘆𝘀𝘁𝗲𝗺 𝗱𝗲𝘀𝗶𝗴𝗻 📐 As any ML system that has existed, exists and will exist, this one can be broken down into 3 types pipelines 1️⃣ Feature pipelines 2️⃣ Training pipeline 3️⃣ Inference pipeline Let's go one by one 1️⃣ 𝗙𝗲𝗮𝘁𝘂𝗿𝗲 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲𝘀 💾  The feature pipelines are the Python services that produce the inputs (aka features) our ML model needs to generate its predictions. In our case, we have (and I bet Visa has) at least 3 feature pipelines: ▣ 𝗥𝗲𝗮𝗹-𝘁𝗶𝗺𝗲 feature pipeline from recent transactional data. - runs 24/7 - consumes incoming data from an internal message bus (like Kafka, Redpanda) - transforms this data on-the-fly using a real-time data processing engine - saves the the final features in a feature store, like Hopsworks. ▣ 𝗕𝗮𝘁𝗰𝗵 pipeline from historical features in the data warehouse. - runs daily - reads data from the data warehouse/lake, and - saves it into another feature group in our feature store, so it can be consumed by our ML model really fast. ▣ 𝗟𝗮𝗯𝗲𝗹𝘀 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲, so the ML model can be trained with supervised ML. Each completed transaction that is not claimed by the card owner within 6 months can be safely called non-fraudulent (class=0). We call it fraudulent (class=1) otherwise. Once we have these 3 feature pipelines up and running, we will start collecting valuable data, that we can use to train ML models. 2️⃣ ��𝗿𝗮𝗶𝗻𝗶𝗻𝗴 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲 🏋🏽 We can use a supervised ML model (a boosting tree model like XGBoost does the job in most cases) to uncover any patterns between > the features available in your Feature Store, and > the transaction class: 0 = non-fraudulent, 1 = fraudulent. The final model is pushed to the model registry (like MLflow, Comet or Weights & Biases), so it can be loaded and used by our deployed model. And this is precisely what the last pipeline in our design does. 3️⃣ 𝗜𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲 🔮 The inference pipeline is a Python streaming application, that at start up loads the model from the registry into memory and for every incoming transaction > loads the freshest features from the store for that card_id, > feeds them to the model, and > outputs the predictions to another Kafka topic. These fraud scores can be then consumed by downstream services, to > Block the card, and > Send an SMS alert to the card owner, for example. BOOM! No dark magic. Just Real World ML. Follow Pau Labarta Bajo for more Real World ML

Here's how to simplify your pitch and 10x your sales: 1. Talk less, sell more. Short sentences = more sales. Hemingway once bet he could write a story in 6 words that'd make you feel something: "For sale: baby shoes, never worn." Your pitch should pack the same punch. 2. Complexity is for people who want to feel smart, not be effective. The worst salespeople make simple things sound complicated. The best make the complex simple. 3. Complexity says, "I want to feel needed." Simplicity limits to only what is needed. 4. Read your pitch out loud. I remember when I'd asked my COO to read the manuscript of my book. He chose to do it aloud. All 258 pages. Ears catch what eyes miss. The final version reads like butter. 5. "Be good, be seen, be gone." This was the best sales advice I ever got. - Good: Deliver value - Seen: Make an impression - Gone: Don't overstay your welcome People buy from those they remember, not those who linger. 7. Speak like your customer, not a textbook. We like to sound sophisticated. "We create impactful bottom-line solutions." But we like to listen to simple. "We help small businesses explode their sales." Which one would you buy? 8. Every word earns its place. Your pitch should be lean and mean. - Be specific - Avoid cliches - Check for redundancy - If it doesn't add value, cut it out 9. Abstract concepts bore. Concrete examples excite. ❌ "We'll increase your efficiency." ✅ "We'll save you 10 hours a week." Paint a picture. 10. People buy on emotion & justify with logic So tap into their feelings: - Fear of missing out - Desire for success - Need for security Then back it up with facts. 11. The "Grandma Test" never fails. If your grandma wouldn't get your pitch, simplify it. No jargon. No buzzwords. Just plain English. 12. Benefits > features. Dreams > benefits. ❌ "Our group hosts 10+ events per year." ✅ "Our program helps you close deals." 🚀 "Let's take back Main Street through ownership." 13. Use power words: - You - Free - Because - Instantly - New These words grab attention and drive action. Two final things to keep in mind... Simplicity isn't just for sales. Apply these principles to: - your business operations - your thinking processes - your next investment - your relationships - your to do list Sales isn't just for car dealerships. You pitch when you: - Negotiate a raise - Interview for a job - Post on social media - Hire someone for a job - Talk to an owner about buying their biz If you found this useful, feel free to share for others ♻️

Consulting sells AI, but bills like 1990. Reality caught up to the narrative. Booz Allen's latest results beat expectations: → Revenue: up 12.4% to $12 billion → Adjusted EPS: up 15.5% to $6.35 → GenAI revenue: nearly $800 million, up 30% → Record backlog: $37 billion, book-to-bill of 1.39 Yet the stock crashed 20% in the last 10 days, erasing $3.5 billion in market value. Why? Because beneath strong headline numbers, Goldman Sachs' May 28 downgrade exposed a critical vulnerability: Despite claiming to be an "advanced tech company" with $800M in AI revenue, Booz Allen still derives 98% of its business as a government contractor billing by the hour. The company recently announced 2,500 job cuts (7% of their workforce) due to the Trump administration’s crackdown on federal contracting. I dug into their yearly report to learn more. How Booz Allen Actually Makes Money: The Revenue Reality: → 98% from U.S. government ($10.5B of $10.7B total) → Defense (47%), Civil (34%), Intelligence (17%) → Only 2% commercial revenue 79% of revenue ($8.4B) comes from billing hours: → 55% cost-reimbursable contracts → 24% time-and-materials → Only 21% fixed-price CFO Matt Calderone confirmed their historical growth formula on their earning call: "headcount growth plus 3%". Despite AI claims and the CEO pushing outcome-based contracts for years, only 21% of revenue is fixed-price. Government procurement keeps them billing hours. The Labor Reality: → 36,000 employees driving revenue → 2,500 layoffs (7%) announced after DOGE reviews → Revenue explicitly tied to headcount → When contracts shrink, people get fired The math doesn't lie. You can't justify tech multiples when: → Your entire business depends on one entity → Growth requires hiring more people → Government owns rights to most developed IP  → Margins collapse when contracts face pressure Every firm claiming AI transformation faces this reality: → They pitch cutting-edge technology → They showcase AI capabilities → They demand premium valuations → But their economics remain tied to billable hours When CEO Rozanski said they're "restructuring to match anticipated demand," he revealed the core problem: Revenue directly tracks headcount. Tech companies scale through IP. Traditional consulting scales through hiring - and shrinks through firing. The 20% crash wasn’t about a single quarter. It was Wall Street repricing Booz Allen’s reality - a government contractor at the mercy of federal budgets, not a tech innovator building scalable IP. First, the narrative cracks. Then, the analysts notice. Finally, the market reprices. Booz Allen completed the cycle in 10 days. For consulting firms still betting their "AI story" covers their hourly reality: You're not different. You're just next.

The AI landscape is no longer limited to just machine learning models or chatbot interfaces. It has evolved into a vast, interconnected ecosystem that spans multiple domains, each contributing to the development, deployment, and scaling of intelligence. To bring clarity to this complexity, I’ve mapped out the AI Ecosystem Landscape — a visual representation of the major domains shaping today’s intelligent systems: Artificial Intelligence: The broadest layer, covering reasoning, decision-making, robotics, and symbolic logic. Machine Learning & Deep Learning: Core algorithmic methods powering prediction, classification, and optimization tasks. Natural Language Processing (NLP): From embeddings to summarization, enabling machines to understand and generate human language. AI Agents: The most rapidly advancing layer — featuring goal-driven behavior, memory orchestration, tool use, and emerging protocols like A2A and MCP. Computer Vision: From object detection to scene understanding, giving machines the ability to interpret the visual world. Image Processing: The foundational layer that prepares visual data for analysis and decision-making. This structure not only helps teams navigate the landscape but also identifies where to focus depending on their product goals—whether it's building autonomous agents, enhancing enterprise search, or deploying real-time vision systems. Understanding the ecosystem is the first step toward building a strategic approach in AI.

How to Do Financial Due Diligence Before Selecting Stocks? Stock picking isn’t just about looking at charts and following trends—it’s about understanding the financial health of a company. Before investing, a structured Financial Due Diligence (FDD) process can help you avoid bad bets and spot strong opportunities. Here’s a framework to follow: 1. Understand the Business Model & Industry - What does the company do? - Who are its competitors? - Is it in a growing or declining industry? 2. Analyze the Financial Statements - Income Statement (Profit & Loss) – Revenue growth, profitability (Gross, Operating, Net Margins), EPS trends - Balance Sheet – Debt levels, cash reserves, working capital position - Cash Flow Statement – Operating cash flow vs. net income, free cash flow trends 3. Check Key Financial Ratios - Profitability: ROE, ROA, Gross & Operating Margins - Liquidity: Current Ratio, Quick Ratio - Leverage: Debt-to-Equity, Interest Coverage - Valuation: P/E Ratio, P/B Ratio, EV/EBITDA 4. Assess Management & Governance - Background & track record of leadership - Insider buying/selling trends - Transparency in disclosures & corporate governance 5. Review Competitive Position & Moat - Does the company have a sustainable competitive advantage (brand, network effect, patents, cost advantage)? 6. Industry Trends & Macroeconomic Factors - Economic cycles, inflation, interest rates - Global supply chain, geopolitical risks - Market trends affecting revenue streams 7. Cross-Check with Analyst Reports & News - Read Equity Research Reports, Investor Presentations, Credit Reports - Stay updated on company news, regulatory changes 8. Look at Historical Performance & Future Guidance - Compare past financials vs. projections - Evaluate management’s growth expectations 9. Risk Assessment & Downside Protection - What’s the worst-case scenario? - How resilient is the business in a downturn? 10. Compare with Peers & Make an Informed Decision No company operates in isolation—compare financials and valuations with competitors before buying. Smart investing is about discipline, not hype. By doing thorough due diligence, you increase your chances of picking winners while avoiding pitfalls. What’s your go-to method for analyzing stocks? Let’s discuss.

70% of change initiatives fail. (And it's rarely because the idea was bad.) Here's what actually kills transformation: You picked the wrong change model for the job. It's like performing surgery with a hammer. Sure, you're using a tool. But it's the wrong one. I've watched brilliant CEOs tank their companies this way: Using individual coaching (ADKAR) for company-wide transformation. Result: 200 people change. 2,000 don't. Running a massive 8-step program for a simple process fix. Result: 6 months wasted. Team exhausted. Nothing changes. Forcing top-down mandates when they needed subtle nudges. Result: Rebellion. Resentment. Resignation letters. Here's what nobody tells you about change: The size of your change determines your approach. Real examples from the field: 💡 Startup pivoting product: → Used Lewin's 3-stage (unfreeze old way, change, refreeze) → 3 months. Clean transition. Team aligned. 💡 Enterprise going digital: → Used Kotter's 8-step process → Created urgency first. Built coalition. Enabled action. → 18 months later: $50M in new revenue. 💡 Sales team adopting new CRM: → Used Nudge Theory → Made old system harder to access → Put new system as browser homepage → 95% adoption in 2 weeks. Zero complaints. The expensive truth: Wrong model = wasted months + burned budgets + broken trust Right model = faster adoption + sustained results + energized teams Warning signs you're using the wrong model: • High activity, low progress • People comply but don't commit • Changes revert within weeks • Energy drops as you push harder • "This too shall pass" becomes the motto Match your medicine to your ailment: Small behavior change? Nudge it. Individual performance? ADKAR it. Cultural shift? Influence it. Full transformation? Kotter it. Enterprise overhaul? BCG it. Stop treating every change like a nail. Start choosing the right tool for the job. Your next change initiative depends on it. Your team's trust demands it. Your company's future requires it. Save this. Share it with your leadership team. Because the next time someone says "people resist change," you'll know the truth: People don't resist change. They resist the wrong approach to change. P.S. Want a PDF of my Change Management cheat sheet? Get it free: https://lnkd.in/dv7biXUs ♻️ Repost to help a leader in your network. Follow Eric Partaker for more operational insights. — 📢 Want to lead like a world-class CEO? Join my FREE TRAINING: "The 8 Qualities That Separate World-Class CEOs From Everyone Else" Thu Jul 3rd, 12 noon Eastern / 5pm UK time https://lnkd.in/dy-6w_rx 📌 The CEO Accelerator starts July 23rd. 20+ Founders & CEOs have already enrolled. Learn more and apply: https://lnkd.in/dwndXMAk

What I Look for Before I Invest In A Business Having sat on both sides of the table, as a founder seeking funds and as an angel investor (and Shark 🦈) deploying funds: I’ve realized that when I invest in a startup, I’m evaluating far more than just the pitch deck numbers. Here’s what I really look for when a founder pitches to me: ☑️ Passion & Resilience: I know, everyone says “I’m passionate,” so how do I gauge it? By the sparkle in their eyes when they talk about their product and the honesty when discussing hurdles. I often ask, “What will you do if things don’t go as planned?” – a well-thought answer here shows me they’re in it for the long haul, not just the glory days. ☑️ Understanding of Customers: You’d be surprised how many pitches focus on market size but gloss over the actual customer. I love hearing a founder say, “I spoke to 100 potential users and here’s what they said.” It shows me they’re grounded and customer-obsessed. If you know your users deeply, you can pivot and iterate intelligently. ☑️ Coachability: No one has all the answers, and that’s okay. I actually appreciate when a founder says “I don’t know” and follows up with “…but I’m eager to learn or get help.” It tells me they’re open to mentorship and collaboration. An investee-investor relationship is like a partnership – I don’t want to just write a check; I want to add value. It’s easiest to help someone who’s receptive to feedback and new ideas. ☑️ Alignment of Values: This one is more intangible, but crucial. I check – does this founder’s ethos align with mine? For example, if a founder is willing to compromise on product safety or ethics for a quick buck, we’re probably not a fit. But if they demonstrate integrity (even in small anecdotes, like how they handled a customer complaint), that builds trust. I invested in one startup mainly because the founder said their first big purchase order was delayed and they chose to be transparent with clients rather than cover it up. That honesty won me over. In short, I invest in the person as much as the business. The right investor-founder fit is like finding a co-founder. So if you’re pitching (to me or anyone), remember: beyond the TAMs and P&Ls, we’re listening for your story, character, and vision. And as always, if you have a pitch that fits my areas (D2C, sustainability, etc.), I’m all ears. What qualities do you value most in a founder or an investor? #Leadership #StartupFunding #AngelInvestor #Entrepreneurship

New VC fund managers do not know that these things they are doing are completely ILLEGAL… ❌ There are very strict rules around fundraising. Yet many new GPs copy what they see others doing — even when it’s illegal. The risk? Trouble today, or 5–10 years down the line when regulators or LPs look closer. Sophisticated LPs know the legal lines — and crossing them exposes both liability and inexperience. Here are the 3 most common fundraising violations (and how to avoid them): 1️⃣ PERFORMANCE-BASED FUNDRAISING COMPENSATION 👩🏾⚖️ Many “Vendors” often say: - “I’ll be a venture partner — give me carry for LPs I bring.” - “We’ll raise for you — just pay a % of capital committed.” 🚫 Illegal without a broker-dealer license ($50K–$150K+ + ongoing compliance). Even employee bonuses tied to fundraising can trigger violations. ✅ Legal way: Pay fixed fees or salaries unrelated to fundraising. Compensate with cash, equity or carry — but not tied to capital raised. 👉 Reality check: As a new manager, it’s extremely unlikely that anyone else can fundraise for you without a track record. You’ll almost always need to do the hard work yourself. 2️⃣ GENERAL SOLICITATION 👨🏻⚖️ New managers assume LPs will roll in if they “go public.” Tactics include: • LinkedIn posts about fundraising • Cold DMs to people • Podcasts/webinars about your fund • “Contact us to invest” buttons on websites 🚫 All illegal — unless you’ve structured under narrow exemptions. Even cold outreach counts as solicitation. ✅ Legal way: You can only pitch people you have pre-existing relationships with who are accredited investors. Network authentically, vuild relationships, then pitch one-on-one. 👉 Reality check: Public fundraising isn’t just illegal — it looks cheap. LPs won’t trust someone blasting cold posts with no track record. VC is trust-based. Public asks scream inexperience. 3️⃣ RAISING FROM EU LPS WITHOUT COMPLIANCE 🧑🏿⚖️ Many assume: • “If a European LP wants in, I can accept the money.” • “Everyone else does it — must be fine.” 🚫 Wrong. The EU regulates under AIFMD (Alternative Investment Fund Managers Directive) and MiFID II (Markets in Financial Instruments Directive). Even one EU LP can trigger filings. Regulators act quickly. ✅ Legal way: Work with EU securities counsel. File required notifications in each jurisdiction before accepting European LPs. 👉 Reality check: European LPs expect compliance. Skip it, and you lose credibility. Worse — a violation can come back years later and jeopardize your fund. Breaking the rules — even by accident — is the fastest way to undermine your credibility. And “everyone else does it” is not a defense. The managers who win are the ones who know the rules, build real relationships, and raise the right way. ⚖️ Know the rules. Follow them. Your fund' future depends on it.

Secret for Tax Person to Influencing the CFO: Speak in Cash Impact, Not Regulations! As tax professionals, we often get caught up in quoting sections, clauses, and legal jargon. But when you're talking to the CFO, remember - cash flow speaks louder than compliance. CFOs think in numbers that impact business decisions. Instead of presenting tax issues as a regulatory challenge, frame them as a financial impact. Instead of “Non-compliance with TDS can lead to disallowance under Section 40(a)(ia).” Say “Missing TDS can hit our P&L by ₹X crore in disallowed expenses, increasing our effective tax rate.” Instead of “GST input credit restrictions under Rule 36(4).” Say “We risk losing ₹Y lakh in ITC, directly increasing operational costs and impacting margins.” Instead of “Customs duty changes under the new FTP.” Say “The increased duty rate will raise our import costs by ₹Z crore, affecting pricing strategy.” When tax teams align their messaging with business objectives, they shift from being compliance enforcers to strategic advisors. A CFO wants to know: a. How does this affect cash flow? b. Will it impact profitability? c. Can we optimize our tax position? What’s your approach to engaging finance leaders? Share your thoughts below! #TaxStrategy #CFOInsights #BusinessImpact #TaxandFinance