Discover how an enterprise sales founder uses Claude to turn raw call recordings into tailored, country-specific proposals in a fraction of the usual time. This deep dive walks through the connectors he set up, the prompt he never actually wrote by hand, the human review gates that keep client data safe, and the lessons from iterating the same workflow more than ten times.

In this episode:

* Why generic templates fail for enterprise proposals, and where customization actually moves the deal (currency, country-specific regulations)

* Building a Claude automation that turns a sales call into a draft proposal

* How the setup connects Fathom recordings, the Attio CRM, and his existing proposals

* The role of outline approval, draft-only outputs, and a human review checklist

* Getting client consent before recording, and protecting sensitive information

* How iteration took proposal time from over two hours down to roughly thirty minutes

* Why the bar for enterprise sales keeps rising, and where human relationships still decide deals

Timestamps:

* (00:00) Intro: Vinayak’s sales call volume and proposal workload

* (01:01) Cutting proposal time from 2+ hours to 30 minutes with Claude

* (01:46) Why Claude became the go-to AI tool

* (02:15) Why templates don’t work: the case for customization

* (03:08) A real-world example: currency and regulatory differences by country

* (04:48) Using AI to create meaningfully different customer experiences

* (05:32) Who this workflow is for: the ideal personas

* (07:08) Live demo of the automation setup

* (08:15) The automation prompt and how it was built

* (09:57) Walkthrough of a real output: the US client deck

* (11:33) How to control what gets emphasized in proposals

* (12:14) Lessons from iterating the automation

* (13:21) The current setup, with manual review checkpoints

* (14:19) Human in the loop: keeping an approval gate

* (15:07) How much the output improved through iteration

* (16:24) What happens when recordings are incomplete

* (17:48) The review checklist, and how the time spent has dropped

* (18:15) Handling sensitive client information

* (20:35) What the next version of this looks like

* (22:14) Step by step: how to set this up yourself

* (22:47) Top tips for getting started

* (24:05) Why the bar for sales has gone up

* (24:34) The irreplaceable role of human relationships in enterprise sales

* (26:30) Competing in an AI-enabled sales landscape

* (26:56) Rahul’s story: why presentation quality wins deals

* (27:43) Wrap-up and closing remarks

* (27:48) About Superjoin

* (29:34) Sign-off

Resources & Links:

* Claude

* Attio (CRM)

* Fathom (meeting recorder)

* Notion

Connect with Vinayak Jhunjhunwala:

* LinkedIn

* X

* Superjoin

Discover how a property owner leverages AI automation to streamline booking reconciliation, enhance data accuracy, and gain peace of mind. This deep dive explores the technical workflow and benefits of building a custom AI-powered system for managing multi-channel bookings.

In this episode:

* The challenges of manual booking management across multiple channels

* Building a custom AI skill with Claude to automate data reconciliation

* How the system pulls data from Gmail, Airbnb calendar, WhatsApp, and offline bookings

* The role of conflict detection, issue flagging, and iterative refinement

* Practical insights on data validation, error handling, and decision-making

* Future possibilities: a web interface for broader accessibility

* The peace of mind and data-driven decision advantages for hosts

Timestamps:

* (00:43) How long he hosted before needing a system

* (01:02) Why daily manual logging was impossible

* (01:59) What was wrong with the property manager’s reporting: late and unverifiable

* (03:04) Why existing property apps don’t solve multi-channel, no export

* (04:56) The raw data he actually receives: WhatsApp, Gmail, iCal

* (06:30) The schematic: iCal as source of truth, then Gmail, then WhatsApp gap-fill

* (09:40) Running the demo skill and how it anonymises real data

* (11:20) Why it needs Gmail access and why scope can’t be narrowed

* (12:23) What a Claude skill is and how he built it iteratively

* (13:26) Resolving conflicts: Airbnb email beats WhatsApp as source of truth

* (13:58) Run time and why he runs it end-of-month

* (15:03) The offline-booking blind spot and how iCal catches it

* (16:15) The case for an Airbnb MCP server

* (17:13) Human in the loop, not full automation

* (17:57) How many real issues it caught, plus the tax-deduction edge case

* (19:39) The real payback: peace of mind and clean data

* (20:35) Hard ROI numbers versus other hosts’ gut feel

* (22:07) Flagged conflicts and the monthly summary

* (23:32) Excel and dashboard baked into the skill

* (25:12) Claude Code version versus a hypothetical GUI

* (26:01) The binary insight: you do it or you don’t

* (26:38) Building trust with the manager and the idea of layering in neighbourhood rates

* (27:42) Wrap-up

Resources & Links:

* Claude Code

Connect with Sharath C George:

* LinkedIn

* Twitter

* Website

In this episode, Sushant shares a detailed walkthrough of how they’ve built and optimized Hermes AI agent like called Yumiko to automate and improve lead generation and business workflows. Learn how cutting-edge tools are making it easier for startups to scale prospecting and outreach in minutes, not hours.

Main topics:

* Manual vs. automated lead sourcing: the evolution

* Setting up Hermes AI agent: step-by-step process

* Integrating tools like Google Cloud, Composio, Apify, Crust Data, and more

* How Hermes learns and improves over time through self-updating skills

* Cost considerations and infrastructure setup for startups

* Ensuring lead quality and avoiding hallucinations

* Practical steps for deploying a lead-generation AI in 20 minutes

Timestamps:

00:00 - How Sushant automated his lead sourcing process using Hermes02:08 - The pain points with manual LinkedIn prospecting and outreach03:04 - How Hermes searches, gathers, and organizes leads automatically04:11 - The importance of giving context to AI agents for precision05:15 - Limitations of tools like Apollo and manual lead vetting06:38 - Setting up virtual machines on Google Cloud easily08:15 - How Hermes learns and refines its lead-finding skill over time09:01 - Adding proactive lead alerts and social post monitoring10:21 - The orchestration engine powering multiple AI agents working together11:44 - Demonstration of Yumiko, the AI executive assistant, in action12:48 - Managing meetings, contacts, and data without manual input14:10 - Tools like Composio, Crustdata, and EXA for enriched lead data15:58 - Self-updating skills make Hermes more accurate over time16:36 - Common mistakes and safety guardrails during automation18:22 - How AI agents can send emails, but with safeguards20:27 - Infrastructure setup: virtual machine configurations and APIs23:00 - Connecting Hermes to messaging gateways like Telegram and Slack24:34 - Cost analysis: infrastructure + API credits, budget-friendly options34:44 - Ensuring lead authenticity and avoiding hallucinations36:24 - How simple and accessible deploying AI-based lead gen has become38:13 - Final tips for non-technical founders to set up in 20 minutes39:02 - The future of AI agents acting as virtual team members

Resources & Links:

* Hermes

* Composio

* Crust Data

* Exa Search API

* Apollo

* SimpleClaw (easy setup virtual machine service)

Connect with Sushant:

* LinkedIn

* Twitter

Note:

This methodology demonstrates that with minimal setup—roughly $50/month in cloud and API costs—any startup can automate lead sourcing within minutes, making outreach smarter and scalable. Whether you’re technical or not, the ecosystem options now support effortless deployment.

Discover how Dipesh automated his email inbox with AI agents, reducing distractions and increasing efficiency. Learn the step-by-step setup, costs involved, and potential security risks of integrating AI with your Gmail and calendar system.

Main Topics Covered:

* How Dipesh’s AI agent reads and briefs his emails three times daily

* Full live walkthrough of setting up Claus with Gmail and Google Calendar

* Prompt engineering to prevent AI from sending emails without approval

* The time investment to automate email management (~15-20 minutes setup)

* Cost considerations for using AI agents ($19–$39/month)

* Security risks including data hallucination and unauthorised access

* Strategies to safeguard sensitive information (e.g., using separate email addresses)

In this episode:

* Dipesh shares his initial email checking habits (15-18 times daily) and how automation replaced this

* Step-by-step setup process using CLAWS.app and OpenClaw framework

* How the AI filters important emails, highlights urgent actions, and syncs with Google Calendar

* Custom training of the AI using emails downloaded via Google Takeout

* The importance of setting clear instructions, e.g., “Draft only, do not send”

* How to detect and handle setup hiccups, and the tools involved

* Cost justification based on hours saved and focus gained

* Security measures: connection restrictions, minimising hallucinations, and auto-forward rules

* Practical advice for replicating the setup in under an hour

Timestamps:

00:00 — Introduction: how Dipesh stopped checking email entirely using an AI agent

00:34 — The problem: checking Gmail 15–18 times a day as muscle memory

01:50 — What he tried first: filters, rules, and unsubscribing (and why they failed)

03:27 — Live demo: the agent setup and what the daily briefings look like

07:08 — Setup time and the service he used

08:10 — Connecting Gmail and Google Calendar

08:40 — How the agent decides what’s important (without explicit instructions)

09:49 — Training the agent with historical email data via Google Takeout

10:53 — Replicating the setup live from scratch

13:47 — Scheduling briefings with plain-language instructions (9 AM, noon, 4 PM)

16:43 — The real risks of giving AI access to your Gmail

18:21 — How to prevent the agent from sending email without your approval

19:25 — Additional safety setups (separate forwarding address, local AI filtering)

21:24 — Using “skills” to lock in standing instructions

23:49 — Efficiency gains: three weeks without opening Gmail

24:19 — The cost ($19–$39/month) and whether the ROI makes sense

25:50 — How to get started in under an hour

Resources & Links:

* Klaws.app — Easiest AI automation for Gmail and Google Calendar

* OpenClaw — Framework for managing AI agents

* Google Takeout — Download all emails for training your AI

* Composio — Service used for linking Gmail with AI

Connect with Dipesh:

* LinkedIn

* Twitter

Note:

Security practices and prompt engineering are crucial to prevent unintended AI actions, hallucinations, or data breaches. Always assess the risks before giving AI access to sensitive data.

Discover how Librarian, an AI-powered executive assistant, is revolutionising productivity across various industries, from real estate to insurance. This episode explores practical use cases, integration strategies, and how to start leveraging AI tools for efficiency and peace of mind.

Main Topics:

* What is Librarian and how does it function as an AI executive assistant?

* The evolution of AI from static software to dynamic, voice-controlled workflows

* Use cases in real estate: lead management, property searches, and task automation

* How Librarian integrates with popular platforms like WhatsApp, Gmail, Google Drive, and more

* Building vertical-specific solutions: focusing on real estate and insurance

* Challenges in setup, integration, and maintaining data privacy and security

* Practical steps to get started with Librarian and maximising its features

* The broader applicability of AI assistants for lawyers, coaches, and sales teams

* Impact on peace of mind and productivity through reminders, briefs, and automation

Timestamps:

00:00 - Introduction to Librarian, an AI executive assistant for task automation00:44 - How Librarian works and interfaces with users via WhatsApp01:20 - The leap from traditional software to AI-enabled voice interactions02:13 - The origin story: from blockchain disappointment to AI revolution03:05 - The search for pain points: inefficiencies in real estate workflows03:55 - Demonstrating Librarian’s ability to search and organize information across platforms04:53 - The focus shift to verticals, especially real estate, for deeper impact05:46 - Illustrating real estate use case: driving, lead capturing, and task automation08:03 - Practical demo: managing a property lead call via WhatsApp09:29 - Invoking Librarian to share property options and reminders for clients11:10 - Capturing lead data, tasks, and scheduling follow-ups automatically12:13 - Crafting messages and updating tasks hands-free while on the move14:17 - Beyond property searches: calendar management and email automation16:42 - Workflow automation: from lead intake to automation on Slack and tickets18:29 - The value of integrations like GitHub for troubleshooting and task management20:29 - Addressing setup complexities and the value Librarian provides in stitching workflows23:19 - Ease of setup: from messaging setup to full automation with minimal effort25:22 - How Librarian enhances peace of mind and daily planning with reminders and briefs27:53 - Expanding use cases: lawyers, consultants, and sales professionals29:27 - Practical applications for insurance agents and client management30:30 - Security and privacy measures: encryption, SOC 2 certification, and data handling32:25 - How to get started: creating a free account, top features to explore33:22 - Final thoughts and call to action for productivity enthusiasts

Resources & Links:

* Librarian AI Executive Assistant

Connect with Tiago:

* LinkedIn

* Twitter

Experience the future of work now — start your free 14-day trial with Librarian and transform your productivity!

Discover how Kairos, an AI intern, is transforming small business automation by handling outreach, lead research, and customer interactions without technical support. Learn about its core features, real-world applications, and future direction towards autopilots.

Main Topics:

What is Kairos and how it functions as an AI intern for SMBs

Use cases: lead generation, outreach automation, and workflow simplification

The technical architecture: AI models, agent harness, and cloud infrastructure

Challenges: cost, verification, safety, and model limitations

Future outlook: autopilots, autonomous agents, and scalable AI workflows

Timestamps:00:00 Introduction to Manas Bam and Kairos AI intern demo02:00 What is Kairos? Explaining its role for small business owners04:00 Why build an AI intern instead of hiring? Cost and efficiency benefits06:00 Evolution of AI agents: from helpers to autonomous systems08:00 The technical setup: cloud computers, tool integration, and sandboxing10:00 How Kairos manages outreach tasks: LinkedIn, Reddit, and email campaigns12:00 Automation in action: finding startup founders and reaching out14:00 Personalizing outreach through AI and data-driven prompts16:00 How vague prompts can still yield precise results with screen sharing18:00 The future of autonomous agents: continuous background work and self-starting workflows20:00 Handling platform risks: avoiding bans and platform policies22:00 Infrastructure behind Kairos: virtual machines and cloud services (AWS)24:00 Verifying lead quality: manual review, use cases, and ongoing improvements26:00 Cost considerations: AI model expenses versus business automation value28:00 Business profiles best suited: solopreneurs handling multi-faceted workflows

Resources & Links:

Kairos

Connect with Manas Bam:

LinkedIn

Twitter