Twilio adds Deepgram Nova-3 support, HIPAA eligibility, and multi-language detection

I've spoken to hundreds of GCs and Subs about how they actually use AI. Here are the top 10 use cases in pre-construction — and the prompts that make the output 10x better. You only need a $20/month ChatGPT subscription. 1. Contract Review Upload your contract and ask ChatGPT to surface the key risks, clauses, and requirements that could impact cost or schedule. Power users give it a system prompt that outputs results in their company’s standard format (risk category, section reference, mitigation). Comment if you want that prompt (it's too long for this post). 2. Value Engineering Upload the spec or drawing excerpt, then ask: --> “List material or system substitutions that reduce cost by at least 10% without violating spec intent.” --> Add: “Summarize results by CSI division.” This turns generic suggestions into usable options for estimators and PMs. 3. RFI Generation Give ChatGPT the section or drawing set and say: “Identify unclear scope, missing dimensions, or conflicts and draft RFIs for each in standard format (Question / Reference / Reason).” It’s best at identifying coordination gaps between electrical, mechanical, and architectural sheets. 4. Addenda Review Upload both the base and addendum PDFs and ask: “List all changed sections and summarize the impact on scope, quantities, and risk.” Add “Use a table format with columns for Change / Impact / Discipline.” Cuts hours off manual diff reviews. 5. Report Generation After takeoff or review, drop in your notes and prompt: “Draft a 1-page summary for internal review covering risks, assumptions, exclusions, and next steps.” I'll post the remaining items tomorrow.

Open AIs Agent Builder vs N8N - a comparison for people who need to make a decision today. Agent Builder strengths: ✅ Conversational workflow design ✅ Natural language configuration (lowers barrier to entry) ✅ Strong if/then logic handling ✅ Hides technical complexity while preserving capability ✅ Excellent for guided user processes Agent Builder limitations: ⚠️ Cannot run scheduled/recurring tasks ⚠️ Must be human-initiated ⚠️ Limited tool integrations (expanding) ⚠️ Not suitable for unattended automation N8N strengths: 🔄 Scheduled execution (hourly, daily, custom) 🔄 Extensive integration library 🔄 True automation (no human trigger required) 🔄 More Mature ecosystem The decision matrix: Need conversational workflows with human interaction? Agent Builder Need unattended automation and scheduled processes? N8N The broader point: Companies with 50+ employees need to have a dedicated resource monitoring AI developments. The velocity in this space is unsustainable to track casually. You're either committed to staying current or you're accumulating technical debt. The gap between leaders and laggards is widening weekly. If your business tech isn't keeping pace, let's talk.

Yesterday, in a meeting, a client said, “we can just build some agents on GPT.” Well… no, no you can’t -because they’re not agents in the true sense, and therefore not relevant. Why? Because a true agent needs the equivalent of these five things: Role. Knowledge. Action. Guardrails. Channels. ChatGPT “agents” tick maybe three (if you squint hard enough) and only if you’re technically adept enough to wire in data, connect APIs, and structure logic properly. They can act, they can reason (to an extent), and they can access information BUT they DO NOT own their environment, DO NOT remember context, and DO NOT govern themselves. They react. They DO NOT operate. They follow. They DO NOT plan. They can be brilliant for individual run businesses where you just need a smart assistant to run repetitive tasks. But for a business that has teams, runs on processes, grounded in company data, need to be secure, they’re not scalable, auditable, or governed. They sit outside your systems, not within them. It’s not agentic. It’s mostly prompt templates Useful? Sure. Autonomous? Not even close Like pumpkins - Just because you can pick one doesn’t mean it’s the right one 😜

Lately, I’ve been rethinking how I work… I added one small rule to my workflow: 𝗱𝗼 𝗺𝗼𝗿𝗲 𝘄𝗶𝘁𝗵 𝗹𝗲𝘀𝘀. I did not just want to save time - I was trying to stay focused on what moves the needle. I actually took this principle from 𝘉𝘳𝘪𝘢𝘯 𝘛𝘳𝘢𝘤𝘺’𝘴 “𝘌𝘢𝘵 𝘛𝘩𝘢𝘵 𝘍𝘳𝘰𝘨.” 🐸 (Highly recommend, btw!) After applying it in my daily work, I realized something simple: You don’t always need new tools or complicated systems to be more 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝘃𝗲. Sometimes, you just need to start 𝘂𝘀𝗶𝗻𝗴 𝘁𝗵𝗲 𝗰𝗹𝗮𝘀𝘀𝗶𝗰 𝗼𝗻𝗲𝘀 𝗽𝗿𝗼𝗽𝗲𝗿𝗹𝘆. When I first started using ChatGPT, I treated it like a basic Q&A tool - asking quick, random questions. But after a year of experimenting and testing it in real business workflows, the results spoke for themselves. 📊 One of the examples — A process that used to take five hours now takes one. With more accurate and clearer result. That’s why I decided to record a short video about the 𝘁𝗵𝗿𝗲𝗲 𝗰𝗼𝗿𝗲 𝗖𝗵𝗮𝘁𝗚𝗣𝗧 𝗳𝗲𝗮𝘁𝘂𝗿𝗲𝘀 every leader should understand. ✅ Projects - store company context once and let ChatGPT think with it. ✅ Deep Research + Thinking Mode - move from scattered reports to structured executive insights. ✅ Canvas - turn those insights into visual dashboards your team can act on. ✅ Connectors - connect all your company’s tools- so ChatGPT can instantly find, summarize, and analyze the data your team needs. 🎥 I break down each feature step-by-step in this video — with real business examples.

I built an AI Receptionist that actually picks up the phone After spending the last month deep in n8n workflows and API integrations, I'm excited to share my latest project: an AI-powered receptionist that handles real customer calls through a live Twilio phone number. What it does: - Answers calls with sub-3 second response time using ElevenLabs voice synthesis - Handles FAQs, books appointments, and transfers to humans when needed - Uses custom context engineering so it actually knows your business - Saves every conversation to a database backend for analytics and training - Custom voice cloning - train it to sound like you or match your brand voice The tech stack: - n8n for intelligent workflow automation - OpenAI API for natural language understanding - ElevenLabs for human-like voice generation - Twilio for phone infrastructure - Custom database backend for conversation history Why this matters: Small businesses lose customers because they can't answer the phone 24/7. This solution handles the repetitive stuff (FAQs, scheduling, routing) while seamlessly transferring complex inquiries to humans. The real win? It learns from every interaction. The database tracks patterns, common questions, and customer intent - making it smarter over time. What I learned: Building production-ready voice AI is hard. Handling interruptions, context switching, and maintaining natural conversation flow required extensive prompt engineering and workflow optimization. But seeing it handle real calls successfully made it worth it. Currently testing with early users and iterating based on feedback. If you're curious about the technical details or want to see a demo, feel free to reach out. #AI #VoiceAI #Automation #TechProjects #SoftwareEngineering #AIAgents

Language mismatch is the most expensive 30 seconds on your phone line. Voice callers are high intent. If a prospect switches from Spanish to English mid-sentence and your team can’t keep up, you lose conversion and trust. What’s changed: AI voice agents that auto-detect and switch languages during the call—no IVR menus, no transfers. 24/7. Benchmarks you can bank on: targeted voice-AI flows routinely hit 40–70% containment (FAQs, bookings, order status) and shorten time-to-first-response—both reliable levers for inbound sales and CX. 30-day proof plan: pick 1–3 languages + one flow (after-hours/overflow), localize scripts (don’t just translate), set conservative confidence thresholds, route a slice of traffic, review transcripts daily. Track: containment by language, detection accuracy & switch latency, TTF response, conversions/bookings per 1,000 calls, CSAT, and warm-handoff success. Where Dialio fits: Sophie handles 58+ languages with automatic mid-conversation switching, real-time transcription, intent tagging, warm handoffs, CRM/webhooks, and analytics—so you capture global intent without spinning up regional teams. #VoiceAI,#MultilingualCX,#ContactCenter,#SalesOps,#CustomerExperience,#B2BSaaS,#Automation,#LeadGen,#AIinSales,#Dialio

Dear friends in the customer support industry. There's an important technical term to know. It's the "Model Context Protocol" (MCP). MCP is an open standard created by Anthropic. It has gained widespread adoption. It makes it easy to connect AI models with tools and data sources. In the short term, MCP gives your voice and chat agents great utility. It allows them to retrieve relevant information and take actions. All with low latency. In the long term, you will likely be providing full customer support through an MCP server. It increasingly appears that MCP will be the foundation to provide support inside platforms like ChatGPT. So, when you hear or see the term MCP, perk up. The info is likely relevant, useful, and exciting. --- Talk with industry peers about this topic at the Q4 Contact Center AI Association meeting. We're meeting in Austin on November 13.

Everyone thinks selling AI services means complex custom builds and 6 month projects. It doesn't. The smartest "foot-in-the-door" offer right now has nothing to do with building a new app. It is about enhancing the AI tools your clients' teams already use every day. Think about it. Most employees are using ChatGPT or Claude. The opportunity is not to replace that workflow, but to upgrade it. The pitch is simple: "In two weeks, I can make your team 5% more productive." You do this by connecting their internal systems directly into the chat interface they already know. - No new software to learn. - Instant value from familiar tools. - Uncovers bigger automation opportunities naturally. This approach turns a huge project into a small, low-risk first step. DM me "Entry" and I will send over the framework for this offer.

If you’re new to using Claude Pro for serious work, this will save you hours. Anthropic’s Claude 4 presents a paradox for power users. On one hand, it’s arguably the most effective “work” model. Programmers and productivity leaders value it for its solid, transparent outputs. It codes, strategises, and completes complex tasks with a high degree of reliability. On the other hand, this capability directly leads to its main frustration. Pushing the new Sonnet 4.5 on a large project (like building a website) means you will inevitably hit the Pro plan’s 5-hour message cap. For many, it’s a throwback to the hard rate limits of OpenAI’s ChatGPT in 2023. You’re forced into a tedious manual process of packaging your context to continue in a new chat, breaking your workflow right at the point of deep focus. Short of moving to the API, here are three practical workarounds for this: 1. Use “Projects” as a Context Cache Instead of re-uploading your codebase or key documents every session, use the “Projects” feature. By adding files to the “Knowledge Base” once, they remain available for all new chats within that project, significantly reducing the quota consumed by re-indexing. 2. Automate Chat Continuation When you hit the limit, use a browser extension (search “Claude continue” or similar). These tools automate the manual process of packaging your entire conversation history and loading it into a new chat, reducing the interruption from minutes to seconds. 3. Actively Manage Your Model (The Key) Your message limit is tied to compute cost. Don’t use the most powerful models for every task. • Sonnet 4.5 / Opus 4.1: Reserve for complex analysis, creation, or strategy. • Haiku 4.5: Switch to this model for boilerplate, scaffolding, or simple refactors. Using Haiku for lower-stakes tasks will make your 5-hour limit last dramatically longer. (Save this for later 💾) #OperationalStrategy #AIProductivity #WorkflowOptimization #StrategicPlanning #OperationalExcellence #AIForBusiness #ClaudeAI

The 6 AI tools I'd use if I was starting fresh: Personal: 1. ChatGPT - Handles 80% of daily tasks (research, drafting, brainstorming, custom GPTs) 2. Perplexity Pro - For when you need real sources and proper research, not generic answers (I've got a 12-month free Pro code worth £20/month - comment 'PRO' if you want it) 3. Gamma - Turns messy notes into polished presentations in minutes (no more fighting with PowerPoint) Business: 1. Apollo.io - Find leads, emails, company data, and run full outreach sequences 2. n8n - Connect your tools without code (CRM → email → follow-ups all automated) 3. Claude Code - Custom builds when no-code isn't enough (lead qualification, response systems, custom apps) Personal = works immediately, no setup. Business = scales with you as you grow. Starting cost: ~£50/month Time back: 15+ hours/week What's working for you?