Productivity Apps

𝐇𝐨𝐰 𝐭𝐨 𝐁𝐮𝐢𝐥𝐝 𝐏𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧 𝐀𝐈 𝐒𝐲𝐬𝐭𝐞𝐦𝐬 Demos are easy. Production is where AI systems break. Most teams build the model and skip the system around it. Here are 3 Pillars that separate prototypes from production and the 4 Layers where they intersect. 𝟏. 𝐑𝐞𝐥𝐢𝐚𝐛𝐢𝐥𝐢𝐭𝐲 𝐚𝐧𝐝 𝐑𝐞𝐬𝐢𝐥𝐢𝐞𝐧𝐜𝐞 • Does your system survive failure and recover without human intervention? • Fallback chains catch failures before they reach users. • Circuit breakers stop cascading failures across services. • Retry logic handles transient errors automatically. • Graceful degradation keeps the system running even when parts break. Your AI will fail. The question is whether it fails catastrophically or degrades gracefully. 𝟐. 𝐎𝐛𝐬𝐞𝐫𝐯𝐚𝐛𝐢𝐥𝐢𝐭𝐲 𝐚𝐧𝐝 𝐂𝐨𝐧𝐭𝐫𝐨𝐥 • Can you see what your system is doing and act before things break? • Latency monitoring catches slowdowns before users notice. • Token cost tracking prevents budget blowouts. • Anomaly detection flags quality drift early. • Human-in-the-loop catches the edge cases automation misses. If you can not see what your system is doing, you can not fix it before customers complain. 𝟑. 𝐒𝐜𝐚𝐥𝐚𝐛𝐢𝐥𝐢𝐭𝐲 𝐚𝐧𝐝 𝐏𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 • Can your system handle growing load without compromising quality? • Auto-scaling handles traffic spikes without manual intervention. • Async processing decouples components so one failure doesn't take down everything. • Caching layers reduce redundant LLM calls and cut latency. • Load balancing distributes traffic across instances. 𝟒. 𝐅𝐚𝐮𝐥𝐭-𝐓𝐨𝐥𝐞𝐫𝐚𝐧𝐭 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞 • Where Reliability meets Scalability. • Your system does not just handle failure it scales through it. 𝟓. 𝐈𝐧𝐜𝐢𝐝𝐞𝐧𝐭 𝐑𝐞𝐬𝐩𝐨𝐧𝐬𝐞 • Where Reliability meets Observability. • Monitoring data feeds directly into diagnosis and recovery. • Runbooks define exactly who does what when things break. 𝟔. 𝐂𝐚𝐩𝐚𝐜𝐢𝐭𝐲 𝐏𝐥𝐚𝐧𝐧𝐢𝐧𝐠 • Where Observability meets Scalability. • Know your token costs, latency budgets, and infrastructure limits before peak load hits. • Plan for 3x your expected traffic not 1x. Production-ready AI sits at the center where all three pillars intersect. It is not just a good model. It is reliability, observability, and scalability working together. 𝐖𝐡𝐢𝐜𝐡 𝐩𝐢𝐥𝐥𝐚𝐫 𝐢𝐬 𝐲𝐨𝐮𝐫 𝐛𝐢𝐠𝐠𝐞𝐬𝐭 𝐠𝐚𝐩 𝐭𝐨𝐝𝐚𝐲? ♻️ Repost this to help your network get started ➕ Follow Anurag(Anu) Karuparti for more PS: If you found this valuable, join my weekly newsletter where I document the real-world journey of AI transformation. ✉️ Free subscription: https://lnkd.in/exc4upeq #AIGovernance #ResponsibleAI #EnterpriseAI

📦 JOBS-LED PRICING CANVAS™ A 10-step framework for transforming feature-led products into monetization-ready, jobs-based pricing models. Built on 4 stages: 1. Product (Discovery Layer) 2. Value (Logic Layer) 3. Customer (Preference Layer) 4. Pricing (Monetization Layer) 🔹 STAGE 1: PRODUCT [Discovery Layer] 🔹 Step 1: Feature Inventory What it is: ▪️ List every feature, tool, and function in the product
▪️ Include hidden, premium, or internal-use features Why it matters: ▪️ Creates a complete picture of what’s being delivered
▪️ Prevents missing monetizable elements 🔹 Step 2: Feature to Plan Mapping What it is: ▪️ Show how features are bundled into pricing plans today
▪️ Expose arbitrary or legacy packaging logic Why it matters: ▪️ Reveals pricing misalignment with value
▪️ Highlights over- or under-incentivized plans 🔹 Step 3: Feature Usage Mapping What it is: ▪️ Track actual customer usage of each feature
▪️ Look for engagement patterns by segment Why it matters: ▪️ Identifies “dead weight” vs “core value” features
▪️ Helps assess ROI per feature 🧠 STAGE 2: VALUE [Logic Layer] 🔹 Step 4: Feature Valuation What it is: ▪️ Qualitatively or quantitatively assign value to each feature
▪️ Use proxies: time saved, revenue unlocked, cost reduced Why it matters: ▪️ Establishes which features are worth monetizing
▪️ Anchors the price-to-value logic 🔹 Step 5: Jobs Identification What it is: ▪️ Identify core Jobs-To-Be-Done (JTBD) your product enables
▪️ Use user interviews, surveys, task analysis Why it matters: ▪️ Shifts the model from features to outcomes
▪️ Connects monetization to customer success 🔹 Step 6: Feature–Jobs Mapping What it is: ▪️ Map each feature to one or more customer Jobs
▪️ Create a logic layer: feature → outcome → value Why it matters: ▪️ Bridges product design with pricing strategy
▪️ Enables bundling and upsell opportunities around outcomes 🎯 STAGE 3: CUSTOMER [Preference Layer] 🔹 Step 7: Rank Jobs What it is: ▪️ Prioritize Jobs by importance and frequency
▪️ Use customer feedback and behavior data Why it matters: ▪️ Surfaces which outcomes matter most
▪️ Enables tiering or segmentation logic 🔹 Step 8: Value Jobs What it is: ▪️ Quantify perceived value of each Job
▪️ Use surveys, conjoint analysis, BWS, or proxies Why it matters: ▪️ Links value perception to potential willingness to pay
▪️ Avoids feature-based pricing traps 💰 STAGE 4: PRICING [Monetization Layer] 🔹 Step 9: Value Capture [%] Analysis What it is: ▪️ Decide what % of value created you can capture
▪️ Compare to industry benchmarks or strategic posture Why it matters: ▪️ Sets pricing defensibility
▪️ Avoids overcharging or leaving money on the table 🔹 Step 10: Pricing Metric / Model What it is: ▪️ Choose pricing metric: per seat, usage, credits, % of revenue, hybrid
▪️ Align it to how value is delivered + Jobs solved Why it matters: ▪️ Ensures pricing scales with value
▪️ Sets the business up for sustainable revenue growth #Pricing

Everyone talks about scalability. Very few talk about where the latency is hiding. I once worked on a system where a single API call took ~450ms. The team kept trying to “scale the service” by adding more replicas. Pods were multiplied. Autoscaling was tuned. Dashboards were made fancier. But the request still took ~450ms. Because the problem was never about scale. It was this: - 180ms spent waiting on a downstream service. - 120ms on a database round-trip over a noisy network hop. - 80ms wasted in JSON -> DTO -> Internal Model conversions. - 40ms in logging + metrics I/O. - The actual business logic: ~15ms. We were scaling the symptom, not the cause. Optimizing that request had nothing to do with distributed systems wizardry. It was mostly about treating latency as a budget, not as a consequence. Here’s the framework we used that changed everything: - Latency Budget = Time Allowed for Request - Breakdown = Where That Time Is Actually Spent - Gap = Budget - Breakdown And then we asked just one question: “What is the single biggest chunk of time we can remove without changing the system’s behavior?” This is what we ended up doing: - Moved DB calls to a closer subnet (dropped ~60ms) - Cached the downstream call response intelligently (saved ~150ms) - Switched internal models to protobuf (saved ~40ms) - Batched our metrics (saved ~20ms) The API dropped to ~120ms. Without more servers. Without more Kubernetes magic. Just engineering clarity. 🚀 Scalability isn’t just about adding compute. It’s about understanding where the time goes. Most “slow” systems aren’t slow. They’re just unobserved.

We just set our first pricing model, and it's not the advice you've been hearing on LinkedIn. Everyone is saying you need outcome-based pricing and that seat-based models are dead. We looked at both paths and chose something different. Here's what we considered: Option 1: Traditional Seat Based Seat-based pricing with AI delivering more value per dollar. Safe, predictable, but doesn't match how AI actually works, and how the world is changing. Option 2: Usage-based/Credits Pass through AI costs with markup. Transparent but creates two problems: people hate budgeting (unpredictable) usage, and Pricing 101, day one, first lesson: never do cost-plus pricing if you can avoid it. Cost-plus binds your costs and revenue together in a spreadsheet with some multiplier—you lose the ability to create situations where customers feel they're getting amazing value while you make the math work on the backend. Option 3: Outcome-based Take a percentage of revenue or other business outcomes. Charge for "true work completed". Sounds great until you realize the gap between using a CRM and generating dollars is too big to claim ownership over. Option 4: Something else We chose what we're calling "ergonomic pricing." We took the ideal user experience and are making it our problem to make the math work. Here's how it works: We don't charge for human users. We don't charge for pure usage or outcomes. We charge for "Assistants." When you sign up, we sync and pre-process your team's email history automatically. You can access all of this CRM data for free - the best view-only seat of all time. Assistants add a powerful search, instruction, and tool-calling layer on top of that data platform. Each human can have no assistant, one assistant, or multiple assistants. Different assistant tiers offer different combinations of models, tools, and automation capabilities. This means you can buy "software" from Day.ai (a CRM assistant) and you can also buy digital labor (think a sales engineer with all your company knowledge who can join web meetings and help progress deals). The model gives teams a major wow moment at how much value is offered for free, with a clear path to connecting your human and digital team on a shared data platform. We're not trying to maximize software margins while AI costs are still high. We built 100% transparent pricing from day one. Monthly gets you list price. Annual gets you 20% off. Team growth gets you volume discounts that deepen as you scale. All visible upfront, no negotiation required. This is something I've always wanted to do - complete transparency in how we price and discount. The honest truth is that pricing AI products is impossibly difficult. We've seen the smartest people in SaaS struggle with this. We landed somewhere that feels right: predictable for customers, sustainable for us, aligned with how people actually want to buy and use AI tools. Early response suggests we're getting it right.

92.4% of AI agent companies have figured out something most enterprise software vendors haven't. They've abandoned traditional SaaS pricing entirely. Our latest Global AI Forum research analyzed 60+ Agentic AI companies serving enterprises. The findings will change how you think about AI monetization: The Death of Flat-Rate Pricing → Every AI interaction costs real compute dollars → A power user can cost 100x more to serve than a light user → Yet traditional SaaS treats them identically This is why pure subscription pricing is dying in enterprise AI. What's Actually Working (The Data) ↳ 92.4% use hybrid pricing models ↳ 85.2% pair SaaS with usage-based components ↳ Only 4.5% charge for outcomes ↳ 12.1% run multiple pricing models simultaneously The dominant combination? Subscription + Usage-Based + Freemium + Tiers This isn't experimentation. It's convergence. The Outcome-Based Opportunity Here's where it gets interesting. Intercom Fin ai → $0.99 per resolution (only when customer confirms solved) Zendesk AI → $1.50-2.00 per resolution Salesforce Agentforce → $0.10 per action These companies are betting that value alignment beats predictability. And they're winning. ↳ Intercom reports 66% average resolution rates ↳ ROI is instantly calculable ↳ Buyers pay for results, not access Yet only 4.5% of companies have made this shift. That's a massive whitespace. The Hidden Complexity What enterprise buyers miss: → Cursor's $20/month plan has a credit pool that depletes based on model costs → Windsurf charges flat-rate for their model, token-based for Claude/GPT → Fireflies.ai' "unlimited" transcription has AI credit limits that cost $5-600 extra → Salesforce Agentforce implementations run $50-150k before you pay per action The advertised price is never the real price. What This Means For AI vendors: ↳ Hybrid is table stakes, not differentiation ↳ Outcome-based is the next frontier ↳ First movers will own the narrative For enterprise buyers: ↳ Model total cost of ownership, not sticker price ↳ Push vendors toward outcome alignment ↳ Negotiate usage caps before you sign The Strategic Imperative The companies who figure out outcome-based pricing first will have a meaningful edge. Everyone else will be competing on features while leaders compete on value delivered. Scroll through the full report below Who needs to see this? Tag a founder building AI agents. Tag a CIO evaluating AI vendors. Tag anyone who's been surprised by their AI bill. ♻️ Repost if this changed how you think about AI pricing.

🗣️Not all Power Apps are built the same🙊 Just like you wouldn’t expect a weekend DIY project to follow the same engineering standards as a skyscraper, not every Power Apps should; or can; be built the same way. Small-scale Power Apps often focus on personal or departmental productivity—quick wins that solve targeted, localized problems! 🚀Quick, agile, and built by (a)developer (or even (a)business user) who’s close to the process. 🚀Perfect for solving immediate productivity gaps. 🦺But when Power Apps evolve into enterprise-grade solutions—touching multiple departments, integrating with critical systems, and used by hundreds of users; everything changes: ‼️Architecture matters ‼️ALM matters ‼️UAT matters ‼️Governance matters ‼️Support models matter ‼️Documentation matters 💡If we want low-code to scale, we need to talk seriously about the difference between “build fast” and “build to last”

How do you position and price a new AI product when you know users might be skeptical? OpenStore had created OpenDesk - an AI-powered customer support tool designed for small eCommerce brands. But they anticipated challenges: overcoming merchants' natural resistance to AI and making their value proposition immediately clear. So they asked Irrational Labs to help position and price OpenDesk for success. Through our behavioral science approach, we transformed OpenDesk from "just another support tool" into a compelling investment for eCommerce merchants. What behavioral barriers did we need to overcome? ⚠️ AI Aversion: Small business owners hesitated to trust AI with complex customer issues. ⚠️ Mental Accounting: Support tools were viewed as expenses, not investments. ⚠️ Status Quo Bias: Switching from established workflows felt risky. Our 3-step Behavioral Design process helped us address these challenges: 1️⃣ Behavioral diagnosis: We reviewed OpenDesk's prototype, analyzed competitor pricing, and conducted behaviorally-informed interviews with merchants. 2️⃣ Psychological mapping: We identified how to reframe customer support from a cost center to a revenue driver. 3️⃣ Strategic redesign: We created: 📊 A positioning strategy that emphasized customer retention over just solving support tickets 🎨 A landing page design that instantly communicated value 💰 Three transparent pricing models tailored to merchant psychology For the pricing strategy, we explored multiple pricing models and built behaviorally optimized pricing pages to play out how consumers may react and how to mitigate the pain of paying: 💲 Hybrid Pricing Model: A mix of monthly subscription fee and per-ticket charge 🔢 Usage-Based Pricing Model: A simple pay-per-ticket structure 👥 Per-Seat Pricing Model: A flat fee per user per month, offering straightforward costs that made budgeting easier Our recommendations helped OpenDesk successfully launch in a crowded market with clear positioning and a pricing structure that felt fair to merchants. Shoutout to our core team on this project Katie Dove Karl Purcell Pauline Kabitsis Lydia Trupe and also to Gigi Melrose and Eamon Davis at @OpenStore for their partnership 💪 Want to know exactly how we reframed AI tools, which pricing model worked best, and the specific techniques we used to build trust? Check out the full case study in the comments! Want help positioning or pricing your AI product? Hit me up: kristen@irrationallabs.com   #BehavioralDesign #AIStrategy #ProductPricing

The Scalability Roadmap: (8 steps to handle more traffic) Most .NET applications start simple with: - a single server, - a single database, - and a direct flow: client -> API -> database. Which works fine until traffic grows and hidden bottlenecks appear. However, most systems don't fail at scale because of missing cloud services. Those systems fail when teams add complexity too early, rather than first fixing slow queries and real performance issues. That's why scaling should follow a clear sequence, where each step removes a real bottleneck before the next one is added. Step 1 - Make the app fast for one user. - Start with the code you already have. - Improve database queries. - Filter and paginate in SQL, not in memory. - Return only required columns. - Add indexes and remove unnecessary joins. - If one user is slow, more users will make it worse. Step 2 - Add caching where it actually helps. - Cache expensive operations that are reused. - Read-heavy endpoints. - Data that rarely changes. - Start with in-memory caching. - Add Redis only when multiple instances need shared state. - HybridCache supports both. Step 3 - Move static content out of the API. - APIs should not serve images or static files. - Use a CDN and push static assets to the edge. - The API stays focused on business logic. Step 4 - Push slow work to the background. - Emails, reports. exports, notifications... - If the result is not needed immediately, it should not run in the main request. - Offload to the background jobs. Step 5 - Scale horizontally. - Add multiple API instances. - Place a load balancer in front. - Use health checks to remove unhealthy instances. - Traffic spreads across machines instead of hitting one ceiling. Step 6 - Enable autoscaling. - Too many instances waste money. - Too few hurt performance. - Autoscaling adjusts capacity based on load. Step 7 - Introduce message queues. - Separate request handling from background processing. - Scale both independently. Step 8 - Scale the database. - With multiple API instances, the database becomes the bottleneck. - Read replicas spread read traffic and keep writes centralized. This is how most scalable systems grow. Step by step. Build for today. Prepare for tomorrow.

We closed a 6-figure deal with a company that started on a $300 plan. Here’s how ⬇️ At Datagrid, we made the call early to price with a consumption model. Fixed pricing models sound nice. But here’s why they break down fast: Per-seat: x Virtually limits who can adopt the solution. x It’s a nightmare for ops. Who owns what? Who’s still active? What happens when someone leaves? Per-project: x Projects end, usage drops, revenue disappears. x It’s built-in churn. So we took a different approach. We bundle usage by project, and track everything through a transparent credit system. It’s easy to explain, easy to trust, and has incentives on both sides: - Customers can start small and scale when it works - no bloated upfront costs. One user becomes five. Five become fifty. And so on. - Every action has a known cost. Run a web search? Pull a DocuSign envelope? Every action consumes credits. It’s transparent and fair. - Pricing is tied to value, not headcount or company size. If you're using the product more, it means it's working. And that’s what you’re paying for. The bottom line: When the product delivers, the customer will want to buy more. And when your customers grow, you win too. What pricing model do you think actually scales and keeps customers happy?

If your company still charges per seat, you need a new pricing model. The math is already working against you. I've watched three pricing revolutions in enterprise software from close range. Perpetual licensing to subscription. Subscription to consumption. What's happening now is the fourth, and it's moving faster than the others. Here's what I'm seeing from the buyer side. Customers are consolidating vendors. Terminating point solutions. Using AI agents to fill the gaps that platforms don't cover. Fewer seats. Lower spend. Same or better output. As a buyer, I love it. As a vendor watching your seat count compress, it should be a wake-up call. Two models are emerging to replace seat-based. Consumption-based. Not new, but accelerating. You pay for what you actually use. Clean, defensible, scales with the value delivered. Outcomes-based. Genuinely new. The pitch is simple: "We automate ten headcounts of work. You save ~$1M. You pay us $250K." Hard to argue with the math. Much harder to operationalize the proof at scale. I'll be honest: I don't think anyone has fully figured this out yet (except potentially Manny Medina?). But the direction is clear even if the mechanics aren't. Here's the underlying problem. Seat-based pricing assumed software was the scarce resource. Your buyer is now using AI to do for $10 a month what your $1,000 seat license used to be the only way to accomplish. That's not a negotiation. That's a structural problem. If you're still charging per seat, what's your plan B? #ai #leadership #gtm #transformation