How to Automate Document Workflows

𝗜 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗲𝗱 𝗠𝘆 𝗣𝗠 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄 𝗶𝗻 10 𝗠𝗶𝗻𝘂𝘁𝗲𝘀 - 𝗛𝗲𝗿𝗲’𝘀 𝗘𝘅𝗮𝗰𝘁𝗹𝘆 𝗛𝗼𝘄 👇 Product teams waste 17% of their time on documentation and comms (McKinsey). And I automated the most tedious part for me using Lovable - with Zero code. 👉 Turning detailed PRDs into internal launch comms, Notion posts, stakeholder briefs, and checklists. It was eating up hours every week, across product and growth team. So I thought, what if we just automated it? 🤔 𝗜 𝗯𝘂𝗶𝗹𝘁 𝗮 𝘀𝗶𝗺𝗽𝗹𝗲 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁 𝘁𝗵𝗮𝘁: ✅ Reads our PRDs ✅ Extracts the key details ✅ Generates a Notion-ready launch post ✅ And even creates a structured PM checklist No code. Just a few smart prompt blocks. Now this agent saves our team 4–6 hours per launch, and keeps everyone aligned without the usual back-and-forth. In this post, I’m breaking down exactly how I built it step by step: - My exact 10-step framework. - Battle-tested prompts you can copy. - Common pitfalls (and how to avoid them). 👉 Swipe through to see how you can build your own AI teammate too. P.S. Should product teams have an "AI Agent Manager" role by 2025?

Automating Autodesk Construction Cloud with Open-Source Low-Code Agents 🤖   Connecting directly to LLMs is powerful, but a dynamic AI agent needs more than just raw intelligence.   It needs to decide how to act and respond based on context and broader goals, without requiring us to explicitly program every step.   n8n is an open-source, low-code workflow automation tool with built in AI features, reducing a lot of the complexity involved in controlling agents.   It also makes it easier to apply Retrieval-Augmented Generation (RAG), which improves AI responses by finding relevant data from external sources before generating text.   RAG uses AI-generated embeddings to classify and search documents based on semantic meaning rather than just keywords, ensuring more relevant and context aware results.   This means: ✅ Smarter AI decisions based on contextual rather than keyword-based searches ✅ More accurate and meaningful interactions with project data ✅ A straightforward way to leverage AI without complex custom development   While hallucinations still happen, tools like n8n are great for quickly prototyping with out of the box AI functionality, with the ability to scale when needed.   With ACC's open APIs, we can easily bring project data into the AI knowledgebase. Here I'm extracting data from ACC Issues and RFIs, querying it using AI, and updating records where needed.   To take this further, n8n could be configured to run scheduled updates to keep the RAG databases in sync with the ACC project.   This excites me because with a solid foundation it's possible to automate entire processes rather than individual tasks. For example drafting an RFI response by reviewing specifications, assigning the correct person to review, flagging critical issues and escalating if not closed out.   🏁 Getting Started n8n is free to run locally. You can follow this guide to set it up: https://lnkd.in/gYwm5sSD   🔗Also check out the ACC APIs: https://lnkd.in/g_TSeUYA   🔗My previous post on Copilot Studio with ACC: https://lnkd.in/g4jWacuK   #Autodesk #n8n #Automation #OpenAI

If you're a researcher who spends hours manually copying and pasting data from PDFs and web sources into spreadsheets before you can even begin analysis, this one's for you. Traditional research workflows are painfully manual: Find a document → download it → extract text → copy relevant data → paste into spreadsheet → clean and organize → finally start analyzing. Sound familiar? 😅 Last weekend, I created a streamlined system that automates this process: ✅ Step 1: Built a Hypermode agent that scrapes and performs OCR/text extraction from a given URL ✅ Step 2: Agent identifies entities and relationships ✅ Step 3: Agent creates structured database tables with a proper schema based on discovered relationships ✅ Step 4: Connected Anthropic's Claude Desktop to the database via MotherDuck's DuckDB MCP Server for querying and visualization What's so special? I gave the agent a URL and it was able to process multiple linked PDFs from that page about Iranian sanctions (my first test use-case for a project that John Doyle and a few others are working on). No manual downloads and no file uploads. The agent identified key entities, mapped their relationships, and populated a queryable database. Then using Claude Desktop, connected to that database through the MCP server, I was able to ask questions about the data, generate force graphs, and create infographics or dashboards that can be shared. For anyone drowning in manual research processes, this combination of automated data extraction + Claude's analytical capabilities through MCP servers isn't just a productivity boost...it's a fundamental shift in how people of all technical backgrounds can approach data-intensive research. What research workflows are you still doing manually that could benefit from this kind of automation? My next test use-case? Contracts, of course! 📄 #DataVisualization #CTI #OSINT #LegalTech #MCP

Nobody talks about this side of automation… But it’s the silent killer of 90% of systems that break. Why? Because most automations don’t fail from being slow. They fail because nobody on the team wants to use them. -- If your system spits out: → Messy Google Docs → Unstructured text → Walls of unusable AI output… You didn’t build automation. You built a new manual task. Real automation empowers your team: → Clean, structured outputs → Ready-to-use docs → Clear insights, no cleanup needed And the best part? You don’t need another $49/month SaaS tool. Just structure from day one. -- Here’s the trick we use all the time: Markdown → HTML → Google Docs This tiny formatting chain makes a massive difference: → Auto-generate titles, headers & bullet points → Add spacing for clean reading → Bold key info for faster scanning → Visually break up sections so docs feel human-made No more dumping AI output into Docs and hoping it looks clean. We use this to generate: → Client-ready content briefs → Sales call summaries → Repurposing blueprints → Reports that don’t need reformatting — Structured formatting = team adoption. Team adoption = scalable systems. Because the best systems don’t just automate work — They remove friction across your entire workflow.

Our journey at Docsumo has been transformative. In 2020, we needed 1000+ document annotations to achieve 80% accuracy. Today? We hit 90%+ accuracy with just a single sample. Here's what this evolution taught us: 2020-2021: The Manual Era Remember spending weeks annotating documents for basic invoice processing? We did too. Our breakthrough moment came when we hit 80% accuracy, but the cost was steep: 1000 annotations per document type. Not scalable. 2022: The Automation Shift We rebuilt our entire approach. Reduced required samples from 1000 to 20. Added continuous training. The result? An intern successfully built a driving license model just by uploading and annotating a few samples. This wasn't just an improvement - it was a paradigm shift. 2023-2024: The LLM Revolution Enter LLMs. After months of trials and countless failures, we cracked the code. Now we process everything from simple ID cards to 20-page offering memorandums with 90-95% accuracy using a single sample. No annotations needed. The Real Breakthrough? It wasn't just about better technology. It was about rethinking the entire document processing paradigm. We moved from "How do we train better models?" to "How do we eliminate training altogether?" 2025 Preview: We're taking it further. Imagine dropping a folder of documents and getting a clean, structured table with two clicks. Complete workflow automation, no technical expertise required. This journey taught us: Sometimes the biggest innovations come not from building better tools, but from eliminating the need for tools altogether. What's your document processing evolution story? How are you handling complex documents in your workflow? [Quick demo: Watch our latest model process a 20-page offering memorandum in 60 seconds]

I turned Claude into my personal workflow automation engine using nothing but slash commands and markdown. The gist: you design complex workflows as custom Claude Code commands that guide you through multi-step processes, pulling data from systems, updating others, and handling tasks that need human judgment - all without tab-switching into oblivion. Here’s how I’m building these: 1 - Sketch the workflow first I use Mermaid diagrams. Not just because I love diagrams, but because I can feed them directly to the agent to help it orchestrate better. Visual structure = better execution. 2 - Break big workflows into Lego blocks Learned this the hard way. Started with one massive workflow file. Total mess, impossible to test. Now I break things down. My ideation workflow? Actually three smaller workflows that call each other: Gather insights and analytics, then prompt for ideas based on real problems Deep dive on the promising ones Design quick tests to de-risk before building Way more flexible. Way less brittle. 3 - Keep steps dead simple Each step does ONE thing. When a step starts doing two things, split it. Makes debugging 10x easier when something inevitably breaks. 4 - Structure everything with markdown & XML Sounds nerdy, but it works. I use XML properties to annotate steps and shift the LLM's behavior for each step. For example, sometimes I want the LLM to act more like a facilitator when executing a step, prompting me for input and guiding me towards a better result. Other times, I just want it to do something like grab data from other systems. 5 - Let the LLM update its own workflows Meta, but practical. Since everything's in Mermaid and structured text, I can ask it to refine its own workflow based on what's working. Saves me tons of time. 6 - Version control everything Git isn't just for code. When you inevitably break a working workflow prompt at 4 pm on a Friday, you'll thank yourself for that commit history. The result? Over the past few weeks, we’ve run several ideation sessions and saved hours pulling data and creating tickets in Vistaly and GitHub. I also started sharing these commands with customers and started to see them run with them and make updates. So cool. Who else is building custom workflows like this? What's the most complex thing you've automated with your LLM/MCP? Drop a comment or DM me if you want to swap workflow files. Building a small library of these things.

“What AI skill should my team and I actually learn right now?” I will scream this from the rooftops of NYC. ➡️ Learn agent delegation Target a dedicated workflow or task. Assign an AI agent said role, define the outcome, set constraints, and schedule review gates. Treat it like a junior teammate and give it work, while monitoring so you can review for accuracy. Here’s my do-this-now stack, and how I’d run it with a team ⏬ If you’re a beginner: Start with ChatGPT Agent Mode. Open a new ChatGPT chat and change the dropdown to ‘Agent Mode’. It can plan tasks, execute steps, and return cited outputs for market scans, vendor comparisons, executive briefs, and decision memos. Kick off the job, let it run, WATCH IT RUN, and then review the completion. If you’re more technical or ops-heavy: Use Claude Code when the work requires operating UIs or your computer - clicking through portals, filling forms, wrangling spreadsheets, saving down documents. Expect more upfront setup and ownership, so keep a step-by-step prompt checklist, add automatic reruns for failing steps, and update the checklist only when the site’s labels or paths change. If you’re living in Google Workspace: Turn on Google connectors (Drive, Gmail, Calendar) inside ChatGPT or Claude. Ask the model to find your team’s file, summarize threads, compare document versions, prepare for and schedule meetings, or draft from past emails. This lets your agent pull context and act on it without manual hunting. How to turn this into outcomes in 30 days ⏬ → Twice a week, use Agent Mode to produce a one-page brief with citations and a recommendation on a real business question. Track cycle time and data/citation quality, and, where relevant, use Claude Code to automate in parallel. At the end of the month, you should know where a few agents can tackle real work and have the data to support what to scale. #AIinWork

LlamaIndex just unveiled a new approach involving AI agents for reliable document processing, from processing invoices to insurance claims and contract reviews. LlamaIndex’s new architecture, Agentic Document Workflows (ADW), goes beyond basic retrieval and extraction to orchestrate end-to-end document processing and decision-making. Imagine a contract review workflow: you don't just parse terms, you identify potential risks, cross-reference regulations, and recommend compliance actions. This level of coordination requires an agentic framework that maintains context, applies business rules, and interacts with multiple system components. Here’s how ADW works at a high level: (1) Document parsing and structuring – using robust tools like LlamaParse to extract relevant fields from contracts, invoices, or medical records. (2) Stateful agents – coordinating each step of the process, maintaining context across multiple documents, and applying logic to generate actionable outputs. (3) Retrieval and reference – tapping into knowledge bases via LlamaCloud to cross-check policies, regulations, or best practices in real-time. (4) Actionable recommendations – delivering insights that help professionals make informed decisions rather than just handing over raw text. ADW provides a path to building truly “intelligent” document systems that augment rather than replace human expertise. From legal contract reviews to patient case summaries, invoice processing, and insurance claims management—ADW supports human decision-making with context-rich workflows rather than one-off extractions. Ready to use notebooks https://lnkd.in/gQbHTTWC More open-source tools for AI agent developers in my recent blog post https://lnkd.in/gCySSuS3

Your CRM should be saving your team hours of manual work. Is it? (Automation tip Tuesday 👇) A marketing agency client had an excellent sales process — and a less-than-excellent manual onboarding process. Internally: → attach a credit card authorization pdf form to an email → send it to the client → wait for the client to send it back → send a followup email to the client after a set number of days On the client’s end: → download the pdf → print the pdf → sign the pdf → scan the completed form → attach to email → send email What a waste of energy, right? Now, their process is automated. When the deal stage is changed in the CRM, the signature request form is automatically sent through PandaDoc. The client submits details and signs online, with zero back-and-forth. A task is created for the account rep following receipt of client signature. And of course, if the client delays, a followup email is automatically triggered following a specified number of days. Your CRM should be saving you hours of work every day. Are you getting the most out of your CRM? -- Hi, I’m Nathan Weill, a business process automation expert. ⚡️ These tips I share every Tuesday are drawn from real-world projects we've worked on with our clients at Flow Digital. We help businesses unlock the power of automation with customized solutions so they can run better, faster and smarter — and we can help you too! #automationtiptuesday #processautomation #workflow #crm

𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗻𝗴 𝗱𝗼𝗰𝘂𝗺𝗲𝗻𝘁 𝗮𝗽𝗽𝗿𝗼𝘃𝗮𝗹𝘀 𝗶𝘀𝗻'𝘁 𝗷𝘂𝘀𝘁 𝗮 𝗻𝗶𝗰𝗲-𝘁𝗼-𝗵𝗮𝘃𝗲. 𝗜𝘁'𝘀 𝗮 𝗴𝗮𝗺𝗲-𝗰𝗵𝗮𝗻𝗴𝗶𝗻𝗴 𝗲𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆 𝗯𝗼𝗼𝘀𝘁. Here's why: 1. 𝗧𝗶𝗺𝗲 𝗦𝗮𝘃𝗶𝗻𝗴𝘀 Manual approvals eat up hours. Automation gives that time back. 2. 𝗘𝗿𝗿𝗼𝗿 𝗥𝗲𝗱𝘂𝗰𝘁𝗶𝗼𝗻 Human errors vanish. No more missed signatures or approvals. 3. 𝗙𝗮𝘀𝘁𝗲𝗿 𝗧𝘂𝗿𝗻𝗮𝗿𝗼𝘂𝗻𝗱 Approvals that took days now take minutes. 4. 𝗜𝗺𝗽𝗿𝗼𝘃𝗲𝗱 𝗖𝗼𝗺𝗽𝗹𝗶𝗮𝗻𝗰𝗲 Automated trails make audits a breeze. 5. 𝗖𝗼𝘀𝘁 𝗥𝗲𝗱𝘂𝗰𝘁𝗶𝗼𝗻 Less paper, printing, and storage costs. But here's the kicker: Most companies are doing it wrong. They slap on a basic e-signature tool and call it a day. That's not true automation. It's just digital paper pushing. Real automation means: • Smart routing based on document type • Automatic reminders for pending approvals • Integration with existing systems • Mobile-friendly interfaces for on-the-go approvals • Analytics to spot bottlenecks The result? A streamlined process that doesn't just save time—it transforms how work gets done. One company I worked with saw: • 70% reduction in approval times • 90% decrease in errors • Annual savings in admin costs But here's the truth: Implementing this isn't easy. It requires: 1. Process mapping 2. Stakeholder buy-in 3. Tech integration 4. Change management Yet, the payoff is massive. So, ask yourself: Are you truly automating? Or just digitizing old problems? The answer could be the difference between staying ahead or falling behind. Connect with me Halid Ayob, I'm passionate about helping professionals optimize their work with digital tools! #WorkflowAutomation #WorkProductivity #DigitalTransformation