Automating Repetitive Work Tasks

How we saved 10+ hours weekly by giving finance a simple interface. Our finance team was processing invoices the same way for years: 1. Email attachments → 2. Manual download → 3. Print → 4. Physical signature → 5. Scan → 6. Manual data entry The entire cycle took 3-5 days. The request to "build a proper approval system" kept getting deprioritized—it felt like a multi-month project. We reframed the problem: We didn't need a complex system. We just needed to connect two things: the data from our accounting software's API and a simple list where the right people could click "Approve" or "Reject." What actually got built: • A single-page app that pulls unpaid invoices automatically • Logic that routes invoices over $5k to directors, others to managers • A comment field for rejections • A basic audit log showing who approved what and when What changed: ✅ Approvals now happen in under 24 hours ✅ The finance team stopped chasing paper trails ✅ Vendors get paid faster ✅ Every decision is logged automatically The takeaway: Sometimes "digital transformation" isn't about big platforms. It's about giving a team one less PDF to manage by building a simple, focused tool that sits on top of the data they already use. What's the most stubborn, repetitive task in your team's workflow? Often the highest-impact tools are the smallest ones that remove a single point of friction. https://uibakery.io/ #ProcessAutomation #FinanceTech #OperationalEfficiency #DigitalTransformation

Few Lessons from Deploying and Using LLMs in Production Deploying LLMs can feel like hiring a hyperactive genius intern—they dazzle users while potentially draining your API budget. Here are some insights I’ve gathered: 1. “Cheap” is a Lie You Tell Yourself: Cloud costs per call may seem low, but the overall expense of an LLM-based system can skyrocket. Fixes: - Cache repetitive queries: Users ask the same thing at least 100x/day - Gatekeep: Use cheap classifiers (BERT) to filter “easy” requests. Let LLMs handle only the complex 10% and your current systems handle the remaining 90%. - Quantize your models: Shrink LLMs to run on cheaper hardware without massive accuracy drops - Asynchronously build your caches — Pre-generate common responses before they’re requested or gracefully fail the first time a query comes and cache for the next time. 2. Guard Against Model Hallucinations: Sometimes, models express answers with such confidence that distinguishing fact from fiction becomes challenging, even for human reviewers. Fixes: - Use RAG - Just a fancy way of saying to provide your model the knowledge it requires in the prompt itself by querying some database based on semantic matches with the query. - Guardrails: Validate outputs using regex or cross-encoders to establish a clear decision boundary between the query and the LLM’s response. 3. The best LLM is often a discriminative model: You don’t always need a full LLM. Consider knowledge distillation: use a large LLM to label your data and then train a smaller, discriminative model that performs similarly at a much lower cost. 4. It's not about the model, it is about the data on which it is trained: A smaller LLM might struggle with specialized domain data—that’s normal. Fine-tune your model on your specific data set by starting with parameter-efficient methods (like LoRA or Adapters) and using synthetic data generation to bootstrap training. 5. Prompts are the new Features: Prompts are the new features in your system. Version them, run A/B tests, and continuously refine using online experiments. Consider bandit algorithms to automatically promote the best-performing variants. What do you think? Have I missed anything? I’d love to hear your “I survived LLM prod” stories in the comments!

Your best strategist spent yesterday afternoon resetting broken mailboxes instead of building campaigns. That’s the hidden cost of scaling outbound that nobody talks about. The more clients you take on, the more your best people end up doing technical cleanup work instead of strategy. Fixing inboxes. Rebuilding domains. Checking deliverability. Resetting what should already just work. I got tired of watching high-value hours disappear into backend maintenance. So I moved the whole setup behind Claude + Maildoso MCP. Now I just describe what I want, and the system executes it. 1. Set up the infrastructure Maildoso creates cold email domains and inboxes. SPF, DKIM, and DMARC are handled automatically. Your main domain stays safe. 2. Connect it to Claude Add your API key. Plug Maildoso MCP into Claude Desktop. Now Claude can directly run your email infrastructure. 3. Tell Claude what to build Example: “Create 5 domains, set up 3 inboxes per domain, add a reply forwarding inbox, and check deliverability so only healthy inboxes are used.” Claude runs the setup through the API. The real win is monitoring. Maildoso keeps checking inbox health every few days and pauses bad ones before they hurt your campaigns. So your team stops babysitting infrastructure. And goes back to doing actual strategy and campaigns. If your team is still manually managing inboxes, you’re burning your best talent on the wrong work. Over to you: where does outbound usually break for you, data quality or sending infrastructure? #Outbound #ColdEmail #SalesAutomation #Deliverability #AItools

Building useful Knowledge Graphs will long be a Humans + AI endeavor. A recent paper lays out how best to implement automation, the specific human roles, and how these are combined. The paper, "From human experts to machines: An LLM supported approach to ontology and knowledge graph construction", provides clear lessons. These include: 🔍 Automate KG construction with targeted human oversight: Use LLMs to automate repetitive tasks like entity extraction and relationship mapping. Human experts should step in at two key points: early, to define scope and competency questions (CQs), and later, to review and fine-tune LLM outputs, focusing on complex areas where LLMs may misinterpret data. Combining automation with human-in-the-loop ensures accuracy while saving time. ❓ Guide ontology development with well-crafted Competency Questions (CQs): CQs define what the Knowledge Graph (KG) must answer, like "What preprocessing techniques were used?" Experts should create CQs to ensure domain relevance, and review LLM-generated CQs for completeness. Once validated, these CQs guide the ontology’s structure, reducing errors in later stages. 🧑⚖️ Use LLMs to evaluate outputs, with humans as quality gatekeepers: LLMs can assess KG accuracy by comparing answers to ground truth data, with humans reviewing outputs that score below a set threshold (e.g., 6/10). This setup allows LLMs to handle initial quality control while humans focus only on edge cases, improving efficiency and ensuring quality. 🌱 Leverage reusable ontologies and refine with human expertise: Start by using pre-built ontologies like PROV-O to structure the KG, then refine it with domain-specific details. Humans should guide this refinement process, ensuring that the KG remains accurate and relevant to the domain’s nuances, particularly in specialized terms and relationships. ⚙️ Optimize prompt engineering with iterative feedback: Prompts for LLMs should be carefully structured, starting simple and iterating based on feedback. Use in-context examples to reduce variability and improve consistency. Human experts should refine these prompts to ensure they lead to accurate entity and relationship extraction, combining automation with expert oversight for best results. These provide solid foundations to optimally applying human and machine capabilities to the very-important task of building robust and useful ontologies.

Everyone is talking about AI SDRs being the next hot thing in Outbound. No one’s talking about what they can actually do (and how they work). Here are the real questions: → What does an AI SDR Outbound workflow look like? → How are top sales teams using AI SDRs right now? → Can AI SDRs really replace human reps (or will they just support them?) → How do you balance AI automation with personalization in Outbound? → Which AI SDR tools are actually delivering results (and which aren’t?) → What do AI SDR's help us with? speed, accuracy, or something else? These are the typical AI SDR workflows for an Outbound motion (so far): 🔎 Find & Contact Leads → AI can build prospect lists based on your ICP. ⚡️ Enrich Data → It can verify emails, phone numbers & LinkedIn profiles. 🧑🏻💻 Scrape Websites → It can also analyze technographics, firmographics, services, and other data like hiring trends, company size etc 🚨Track Intent Signals → It can monitor funding news, job postings & social activity. ✅ Qualify Leads → It can score prospects on fit & engagement. 📧 Send Personalized Emails → It can deliver context-rich messages at scale. 💙 Categorize Responses → AI can sort replies: interested, follow-up, not now. 📅 Book Meetings → It can also schedule qualified calls. 🔂 Update CRM → And ofcourse it can ensure smooth handoff to human reps by updating everything in your CRM Just keep in mind that some AI SDRs can manage the entire workflow, while others can focus only on specific tasks (e.g RevReply). Here are some other cool 🥶 features AI SDR's have: ✅ Custom Training → AI can adapt to your ICP, messaging & CRM insights. ✅ Multi-Channel Campaigns → AI can run LinkedIn, email & Outbound at scale. ✅ Industry-Specific AI → Some AI SDRs have pre-trained models for Marketing, HR, finance, SaaS, & eCom etc ✅ Smart CRM & Slack Sync → AI can learn from interactions & adjust on the spot. And here are a few AI SDRs that the SalesCaptain team has been testing: ↪ Artisan ↪ AISDR (YC Backed) ↪ 11x ↪ Patagon AI ↪ Regie.ai ↪ RevReply ↪ Swan AI ↪ settr. ↪ Topo (YC W24) So here's my conclusion on AI SDR's: ✨ AI SDRs are not here to replace human reps (yet). ✨ They are good at automating simple, repetitive workflows. ✨ We still need reps to be on top of AI. ✨ At the end of the day, performance depends on how you are leveraging them. Have you tested any AI SDRs? What’s working (or not) for you? Drop me a comment below! 👇 #aisdr #salesautomation #outboundsales #leadgeneration

This AI Workflow Automates Networking (n8n + ChatGPT): When I was networking, I felt overwhelmed. Not only was talking to strangers way out of my comfort zone... But keeping track of new contacts, existing contacts, and previous conversations was overwhelming. Not to mention trying to figure out what the heck to even say to these people. If you're struggling with any of those things? This video is for you. I'm going to teach you how to use a combo of n8n + ChatGPT to build a workflow that automates everything outlined above. We work with hundreds of private clients every year in our job search coaching program. That experience has confirmed two things: 1. Networking is far and away the most effective way to get hired right now 2. Most people don't have a good system for networking, and don't get traction as a result This video is going to show you how to set up and automate a crazy effective networking system, including: ✅ A "Second Brian" For Your Networking Efforts You need a central hub to store all of the information from your networking - names, emails, interests, dates of last convos, notes, etc. Here's a screenshot of a version of what I used for my job search (there's a link grab a free copy of this template in the YouTube video description). This n8n workflow is going to automate everything for you after you add a new contact to your sheet. 🤖 Automated Updates The workflow is set up to scan your email at regular intervals looking for messages from your networking contacts. When it finds them? It uses that context to do all of the tracking and brainstorming for you. ✏️ Automated Conversation Notes The workflow is also going to turn emails from your contacts into short summaries so you never forget what you spoke about. The summaries will automatically update with every email your contact sends you. 🧠 Automated Next Steps (Adding Value) When a conversation is updated, the workflow will brainstorm ways that you can add value to your contact. Then it will upload those ideas in a "Next Steps" column so you can easily locate them and take action. 🗓️ Automated Follow Up Deadlines Finally, the workflow will recommend follow up deadlines that are in line with the conversation and the next steps you're taking. Did your contact ask for a PDF you mentioned? It'll tell you to send that today. But if they told you try a 2 week course on AI fundamentals? It'll recommend following in, say, 2.5 weeks. The best part? You do NOT need to be technical or no how to code to set this up. The whole thing should take you about an hour and you'll have an automated networking system to supercharge your job search. Also, the video comes with: ✅ A free copy of my Google Sheet networking track ✅ A free copy of the n8n template that you can plug and play ✅ The exact ChatGPT prompts I spent hours dialing in for this use case All for free in the video description: >> Click here to watch the full video: https://lnkd.in/eCW5EMZ8

🚀 We can now call Power Automate flows directly from Power Apps Code Apps — with input params and typed responses. Watch here 🎥 https://lnkd.in/g6Kbh2gU This was one of the most-requested capabilities in Code Apps! In my new video I cover the entire process end-to-end: ✅ Listing available flows with the new npm CLI ✅ Adding a flow to your code app (and what gets auto-generated) ✅ Writing React + TypeScript code to call the flow ✅ Passing parameters and handling responses ✅ Updating flows And to bring it all together, I demo a full Help Desk code app that triggers a real approval workflow. #PowerApps #PowerAutomate #CodeApps

Top 6 AI tools for design & workflow in 2026 👇 Yes, not all of them are “design tools.” Yes, that’s exactly the point. I spent time exploring tools beyond just UI screens… Because real product work is not just design anymore. It’s workflows. Automation. AI orchestration. Here are 6 that actually matter right now: 1. Paperclip AI https://lnkd.in/dXkCrnbe Local-first AI for organizing research, notes, and work items. But it goes deeper. It acts like an orchestration layer for AI agents. Goals. Budgets. Audit logs. Agent “heartbeats.” If you deal with messy research or multi-step thinking, this is insanely powerful. 2. Flowstep https://flowstep.ai Prompt → UI designs. It generates wireframes and full interfaces on an infinite canvas. You can iterate fast. Refine layouts. Explore ideas visually. Feels like Figma + AI had a smarter child. 3. Moonchild AI https://moonchild.ai Turn PRDs into actual UI screens. It helps with: User flows UX problem solving Moodboards Design systems This is not just generation. It’s structured product thinking. 4. Dify https://dify.ai Visual builder for AI apps. Drag. Drop. Deploy. You can create: Chat apps Text-generation tools Custom AI workflows If you ever wanted to ship your own AI product without heavy coding, start here. 5. Flowise https://www.flowise.io Low-code builder for LLM workflows. Think: Connecting multiple models Creating agent flows Shipping APIs fast Great for prototyping AI features inside real products. 6. n8n https://n8n.io Automation on steroids. Connect apps. Trigger workflows. Automate repetitive ops. Designers ignore this. Smart designers don’t. Because real impact = design + systems. Here is the shift most designers are still missing. The future is not just UI design. It’s: Design + AI Design + automation Design + systems thinking Tools like Flowstep and Moonchild help you design faster. Tools like Dify, Flowise, and n8n help you build smarter. And tools like Paperclip help you think better. AI will not replace designers. But designers who understand workflows will replace designers who only push pixels. Use these tools for: Speed Exploration Systems thinking Execution Not just aesthetics. Because in 2026… The best designers are not just designing screens. They are designing how things work. If you had to pick ONE tool to explore this week, Which one are you trying first?

Search is a crucial part of many modern internet products, as companies strive to improve the relevance of search results to enhance customer experience and increase retention. The first step in making these improvements is to measure relevance accurately. This blog, written by the data scientist team at Faire, shares their approach to using large language models (LLMs) to measure semantic relevance in search.  - To define semantic relevance, the team uses a tiered approach based on the ESCI framework, which classifies each search result as "Exact," "Substitute," "Complement," or "Irrelevant." This classification allows for flexible relevance labeling and provides flexibility in fitting various downstream application needs.  - To measure semantic relevance, the team initially relied on human annotators. However, this method was costly and slow, providing only a general measurement on a monthly cadence. With recent advancements in large language models (LLMs), the team transitioned to using these models to assess the relevance between search queries and products automatically. They fine-tuned a leading LLM model to align with the human labelers and measure agreement. The higher the agreement, the better the LLM performance. This LLM could then scale out much more effectively to provide daily evaluations of search's semantic performance.  - The team’s LLM approach underwent multiple iterations, including adopting more advanced models (e.g., LLaMA 3) and more complex techniques (like quantization and horizontal scaling). With these efforts, the solution reached reasonable accuracy with good scalability and can serve the team’s purpose of measuring semantic performance to guide their improvements. This case study highlights that successful LLM applications need clear problem definitions, high-quality labeled data, and iterative model improvements, similar to standard machine learning product integration. It also demonstrates the potential of fine-tuned LLMs in the AI era, making it a compelling read! #machinelearning #datascience #llm #ai #search #relevance – – –  Check out the "Snacks Weekly on Data Science" podcast and subscribe, where I explain in more detail the concepts discussed in this and future posts:    -- Spotify: https://lnkd.in/gKgaMvbh   -- Apple Podcast: https://lnkd.in/gj6aPBBY    -- Youtube: https://lnkd.in/gcwPeBmR https://lnkd.in/gwaxRs2r

Evaluating Retrieval-Augmented Generation (RAG) systems has long been a challenge, given the complexity and subjectivity of long-form responses. A recent collaborative research paper from institutions including the University of Waterloo, Microsoft, and Snowflake presents a promising solution: the AutoNuggetizer framework. This innovative approach leverages Large Language Models (LLMs) to automate the "nugget evaluation methodology," initially proposed by TREC in 2003 for assessing responses to complex questions. Here's a technical breakdown of how it works under the hood: 1. Nugget Creation:   - Initially, LLMs automatically extract "nuggets," or atomic pieces of essential information, from a set of related documents.   - Nuggets are classified as "vital" (must-have) or "okay" (nice-to-have) based on their importance in a comprehensive response.   - An iterative prompt-based approach using GPT-4o ensures the nuggets are diverse and cover different informational facets. 2. Nugget Assignment:   - LLMs then automatically evaluate each system-generated response, assigning nuggets as "support," "partial support," or "no support."   - This semantic evaluation allows the model to recognize supported facts even without direct lexical matching. 3. Evaluation and Correlation:   - Automated evaluation scores strongly correlated with manual evaluations, particularly at the system-run level, suggesting this methodology could scale efficiently for broad usage.   - Interestingly, the automation of nugget assignment alone significantly increased alignment with manual evaluations, highlighting its potential as a cost-effective evaluation approach. Through rigorous validation against human annotations, the AutoNuggetizer framework demonstrates a practical balance between automation and evaluation quality, providing a scalable, accurate method to advance RAG system evaluation. The research underscores not just the potential of automating complex evaluations, but also opens avenues for future improvements in RAG systems.