Just Now Possible

By Teresa Torres

How AI products come to life—straight from the builders themselves. In each episode, we dive deep into how teams spotted a customer problem, experimented with AI, prototyped solutions, and shipped rea... more

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FAQs about Just Now Possible:

How many episodes does Just Now Possible have?

The podcast currently has 19 episodes available.

Just Now Possible episodes:

February 19, 2026Building AI Sales Reps: How ShowMe Orchestrates Voice, Video, and Multi-Agent Workflows to Close Deals
Guests
Yuri Vela Tulopov -- Co-Founder and CEO, ShowMe
Quique Gomez -- Co-Founder and Lead Product Engineering, ShowMe
What we cover in this episode:
How ShowMe builds AI digital workers that function as inbound sales reps
The origin story: spotting the conversion gap at a previous company and realizing AI could fill it
Why the first MVP was a voice agent with product videos and a simple RAG knowledge base
Adding a realistic avatar via HeyGen and how it changed user engagement through better affordances
Decomposing a single sales conversation into multiple specialized sub-agents (greetings, qualifying, pitching)
The three agent types: conversation agents, evaluator agents, and creator agents
How deterministic workflows manage the lead-to-close journey across days
Building toward a smart orchestrator agent that breaks out of rigid workflow paths
Ingesting sales transcripts and training materials to teach agents company-specific sales skills
Customer-driven evaluation loops that start at 100% review and taper to ~5% over time
Creating automated tests from customer feedback to prevent prompt regression
Confidence scoring and frustration detection for real-time human handoff decisions
Treating the agent as a coworker: onboarding via Slack, weekly reporting, CRM integration
Future plans: self-serve PLG motion, smart orchestration, and expanding to customer success
Resources & Links
ShowMe - AI digital sales reps for inbound teams
HeyGen - AI avatar platform used for ShowMe's video calls
Chapters
00:00 Meet the Founders: Juri & Kike Introduce Show Me
00:45 What Show Me Builds: AI Sales Reps as Digital Coworkers
02:17 Why Inbound-First Sales Agents (and Not Outbound Spam)
03:51 Origin Story: The Website Conversion Problem That Sparked the Idea
08:11 MVP Launch: Voice + Video Product Demos in Two Weeks
10:45 Bootstrapping the Knowledge Base: Videos, Docs, Scraping & RAG
11:54 Beyond Demos: Multi-Stage Buyer Journeys, Follow-Ups & Orchestration
14:34 Building Trust: Avatars, Video-Call UX, and AI “Affordances”
20:18 Whiteboard Architecture: Agents, Workflows, and the Orchestrator Layer
29:43 Where Agents Run: Creators, Evaluators, and Breaking Deterministic Flows
32:18 Sales Is High-Stakes: Personalization vs. Hallucinations & Revenue Risk
33:35 Conversation Agent Evolution: From Q&A Bot to Guided Sales Discovery
34:10 Why One Agent Becomes Many: Decomposing Stages for Latency & Memory
36:36 Orchestrator + Tooling: Routing Between Greeting, Qualify, Pitch, Next Steps
38:46 Teaching Sales Skills: Generic Prompting vs Company-Specific Playbooks
42:52 Ingesting Real Calls & Onboarding Like a Teammate (Transcripts, Training Docs)
45:05 Real-Time Voice + Avatar Demos: Latency Tricks and Video Clip Libraries
47:33 Creator vs Evaluator Agents: Data Cleaning, Custom Fields, Sentiment & Confidence
49:15 Human Handoff Guardrails: When Confidence Drops or Users Get Frustrated
50:15 Proving Quality in Production: POCs, A/B Rollouts, Dashboards, and CRM Logging
53:21 Evals at Scale: Customer Feedback Loops, Regression Tests, and the 5% Review Set
58:19 What’s Next: Smarter Orchestration, Self-Serve Setup, and More Digital Workers
01:02:07 Closing Thoughts: Customer Insight as the Moat in a Fast-Moving AI World
...more
1h 4min
February 05, 2026Building Earmark: How a Two-Person Team Turned Meetings into Finished Work
Guests**
Mark Barbir – CEO, Earmark
Sanden Gocka – Co-Founder, Earmark
What we cover in this episode:
How Earmark differs from generic AI notetakers by producing finished work, not just summaries
The pivot from Apple Vision Pro presentation coaching to a web-based meeting assistant
Running multiple agents in parallel during live meetings
Template-based agents: Engineering Translator, Make Me Look Smart, Acronym Explainer
Personas that simulate absent team members (security architect, legal, accessibility)
Why ephemeral mode (no data storage) became a selling point for enterprise
Reducing AI costs from $70/meeting to under $1 through prompt caching
Why GPT 4.1 still beats newer models for prose quality in their use case
The limits of vector search for analysis questions across meetings
Building agentic search with multiple retrieval tools (RAG, BM25, metadata queries, bespoke summaries)
Designing for product managers as the extreme user to solve for everyone
Their vision for an AI chief of staff that goes beyond automating deliverables
Resources & Links
Earmark — Productivity suite where the work completes itself
ProductPlan — Roadmapping tool where both founders previously worked
Granola — AI notetaker mentioned for comparison
Assembly AI — Speech-to-text service used by Earmark
OpenAI API — LLM provider with prompt caching support
Cursor — AI code editor with build integration in Earmark
V0 by Vercel — AI prototyping tool with build integration in Earmark
Chapters
00:00 Introduction to Earmark Founders
00:28 Background and Experience
01:05 What Does Earmark Do?
01:23 AI and Productivity
03:09 Comparing Earmark to Competitors
03:41 Earmark's Unique Features
05:53 Templates and Personas
10:06 Technical Details and Development
17:12 Early Product Versions and Challenges
28:44 Understanding Prompt Caching
29:49 Managing Multiple Tools and Costs
30:59 Optimizing Transcript Summarization
35:11 Challenges with Context and Reasoning Models
38:10 Innovative Search and Retrieval Techniques
44:06 Creating Actionable Artifacts from Meetings
48:30 Ensuring Quality and Managing Hallucinations
58:20 Future Vision for AI Chief of Staff
...more
1h 1min
January 22, 2026When Trust Is Everything: Building AI for Physicians at Healio
Guests
Jennifer Deal – SVP of Product Development, Healio
Casey Utley – Senior UX Designer, Healio
Matthew Skepner – VP of Technology, Healio
What we cover in this episode:
Why physicians need AI at the point of care—and how they actually use it (hint: it's preparation, not bedside)
The surprising discovery that physicians wanted help with patient communication and empathy, not just clinical answers
Building a working prototype in a weekend with Cursor after starting with Figma mockups
How Healio's RAG system combines lexical search, vector search, and semantic search across multiple trusted sources
Why "just use PubMed" isn't simple—five different ways to access the same data, each with trade-offs
Designing citations that physicians trust: subscripts, hover states, and progressive disclosure
Serving contextual ads while the LLM processes queries—a practical monetization approach
HIPAA compliance and input guardrails for masking personal health information
Eight LLM judges for evals: safety, medical accuracy, faithfulness, relevancy, completeness, reasoning, clarity, and overall quality
Why physician feedback trumps LLM-as-judge feedback in high-stakes medical contexts
The role of the Healio Innovation Partners in ongoing discovery and validation
Resources & Links
Healio — Medical news, education, and clinical guidance for healthcare professionals
PubMed — Database of biomedical literature
Cursor — AI-powered code editor used to build the prototype
Chapters
00:00 Introduction to Healio Team
01:00 Overview of Healio's Services
01:57 Introducing Healio AI
03:39 Addressing Physician Needs with AI
05:45 Building Trust in AI Solutions
13:56 Prototyping and Testing Healio AI
18:02 Refining the AI Product
21:48 Technical Architecture and Advertising Integration
25:16 Balancing Speed and Accuracy in AI Responses
26:30 Ensuring Credible and Trustworthy Content
27:41 Challenges in Data Integration and Web Crawling
29:00 Optimizing Search Strategies for Different Data Types
31:09 User Interface and Trust Building
34:31 Human Feedback and Continuous Improvement
35:41 Guardrails and Evaluations for Reliable AI
39:11 Experimenting with LLM as Judges
45:13 Future Directions and User-Centric Design
...more
50min
January 15, 2026Building Tendos AI: How an Agent Swarm Turns Construction Emails into Quotes
Guests
Daniel Kappler — CPO (Product & Design), Tendos AI
Matthias Hilscher — CTO (Engineering), Tendos AI
Key Takeaways
Start narrow to prove value: Tendos AI began with just radiators for one design partner before expanding to all building products
Own the interface: building a web application (vs. integrating into legacy systems) gave them control over UX and the ability to iterate toward full automation
Evaluate each agent, not just the chain: per-agent evals make debugging tractable and show exactly where performance changed
Use review agents: a separate agent that checks work (like code review) catches errors before they reach humans
Let customers pull you: customers asked Tendos to replace their CPQ software—strong signals of product-market fit
Topics Covered
The tendering chain in construction and why it's ripe for automation
How domain expertise (CEO's construction background) helped identify and validate the opportunity
Entity extraction from PDFs ranging from 1 page to 1,800+ pages
Planning patterns in agentic systems—creating and updating plans based on findings
How agents evaluate product fit against customer requirements
Building custom tracing and observability tools for complex agent chains
The path toward self-learning systems through human feedback loops
Links & Resources
Tendos AI
Chapters
00:00 Introduction to Tendo and Key Roles
01:01 Understanding the Tendering Chain
02:26 Real-World Construction Analogy
03:34 Challenges in the Construction Industry
04:48 AI's Role in Tendo's Product
12:59 Early Prototypes and AI Integration
18:31 Expanding Product Capabilities
28:56 Customer Collaboration and Workflow Automation
33:15 Strategic Partnerships and Technical Groundwork
34:20 Focusing on Specific Customer Segments
36:03 Product Evolution and Current Capabilities
38:17 Technical Workflow and Automation
40:12 Evaluating and Matching Product Requests
47:00 Dynamic Agent Architecture
55:29 Quality Measures and Evaluation
01:02:59 Future Directions and Customer-Centric Development
...more
1h 6min
January 08, 2026Building a Career Co-pilot for Disadvantaged Students: How Zero Gravity Bridges Knowing and Doing
Guests
Elliot Little, Product Manager, Zero Gravity
Dan St. Paul, Software Engineer, Zero Gravity
What we cover in this episode
Zero Gravity's mission: breaking down barriers to elite careers for disadvantaged UK students
The "knowing-doing gap"—why students struggle to act even when they know what to do
Why their first prototype (a job suitability summary) didn't create the "wow moment" they expected
The decision to use text chat over voice input and why guided prompts beat empty text boxes
Context management techniques: removing stale tool calls, summarizing history, exposing tools conditionally
Using different models for different tasks (GPT-5 Nano for structured outputs, lighter models for quick replies)
Safeguarding architecture: moderation endpoints plus external verification with Unitary
Building a failure taxonomy through internal red team/green team exercises
What's next: long-term memory management for multi-year student journeys
Links & References
Zero Gravity
Unitary – AI-powered content moderation
Blue Dot Impact AI Safety Course – free AI safety course Elliot recommended
Chapters
00:00 Introduction to Dan and Elliot
00:45 Zero Gravity's Mission and Impact
02:14 Introducing the AI Career Co-Pilot
04:01 Challenges Faced by Disadvantaged Students
06:49 Zero Gravity's Mentorship Program
09:14 Building the AI Career Co-Pilot
12:01 Early Prototypes and User Feedback
17:05 Refining the AI Career Co-Pilot
37:36 Introduction to Career Co-Pilot
38:02 Current Student Interactions
40:22 Technical Deep Dive
42:14 Context Management Challenges
44:43 Tool Call Optimization
51:48 Safeguarding and Moderation
57:52 Evaluating AI Performance
01:04:09 Future Directions for Career Co-Pilot
01:07:52 Concluding Thoughts
...more
1h 10min
December 18, 2025Automating the Full Customer Support Iceberg: How Gradient Labs Built a Multi-Agent Platform
Guests**
Jack Taylor, Product Engineer, Gradient Labs
Ibrahim Faruqi, AI Engineer, Gradient Labs
In this episode
The iceberg metaphor: why frontline support is only the tip of automation potential
How three agent types (inbound, back office, outbound) coordinate on complex tasks like fraud disputes
Natural language procedures that let subject matter experts train agents without engineering bottlenecks
The "turn" architecture: state machines that orchestrate agent logic across async, multi-day conversations
Skills as modular agent capabilities—and how they're scoped deterministically per turn
Defining "done" for outbound agents when the customer isn't the one ending the conversation
Guardrails as classification problems: balancing recall and precision for regulatory compliance
Ask a Human: a tool call that brings humans into the loop for approvals or missing APIs
Auto-eval pipelines that flag conversations for manual review and feed labeled datasets
Links & References
Gradient Labs
Incident.io episode – Referenced in the conversation
Chapters
00:00 Meet the Engineers: Jack and Ibrahim
00:39 The Role of Product Engineers in Tech
01:21 Introduction to Gradient Labs
02:11 The Three Pillars of Customer Support Automation
04:32 The Evolution and Growth of Gradient Labs
05:29 Building and Refining AI Agents
06:39 Outbound Agent: Addressing Customer Problems
09:12 Defining Success in Outbound Procedures
17:08 Ensuring Compliance and Guardrails
30:17 Understanding Agent Guardrails
31:54 Complexities of Natural Language Input
36:21 Skill Design and Management
39:53 Deterministic Skill Execution
41:54 Customer-Specific Guardrails
44:21 APIs and Customer Tools Integration
46:02 Ask A Human Tool
48:24 Guardrails as Classification Problems
57:12 Auto Eval System
59:12 Future of Multi-Agent Systems
...more
1h 2min
December 11, 2025Building Mowie: How a Concierge Service Became an AI Marketing Platform
Guests
Chris O'Connor – CEO, Mowie
Jessica Valenzuela – Co-Founder, Mowie
What we cover in this episode
How Mowie evolved from a concierge marketing service to an AI-powered platform
The "document hierarchy" architecture: how Mowie builds and maintains context about each business
Why they moved from structured schemas to loosely structured markdown for intermediate processing
Using Simon Sinek's Golden Circle framework to validate early product-market fit
How Mowie generates quarterly content calendars and weekly posts across email and social media
The three mini-calendars: public events, business-specific events, and recommended campaigns
Building traceability so customers can see which context documents influenced their content
Using customer approvals, edits, and regeneration requests as lightweight evals
Connecting marketing performance back to point-of-sale data for attribution
What's next: deeper attribution, omnichannel expansion, and digital out-of-home displays
Resources & Links
Mowie AI — AI marketing platform for SMBs
Simon Sinek's Golden Circle — The framework Mowie used for early validation
Chapters
00:00 Introduction to Mowie AI
00:06 Meet the Founders: Chris and Jessica
00:45 Understanding the Target Customers
01:35 Challenges Faced by SMBs in Marketing
04:20 The Evolution of Mowie AI
05:40 How Mowie AI Works for Businesses
08:16 Onboarding and Data Collection
14:56 Content Strategy and Calendar Creation
22:29 Technical Challenges and Solutions
29:41 Iterative Development and Feedback
37:23 Automated Content Calendar Approval
39:19 Content Calendar Creation Process
40:14 Marketing Pillars and Campaigns
42:11 Transparency and Traceability in Recommendations
45:23 Generating Weekly and Quarterly Calendars
48:24 Customizing Campaigns for Specific Events
54:15 Evaluating Campaign Performance
58:48 Customer Feedback and Document Hierarchy
...more
1h 8min
December 04, 2025From Prototype to Production: How Perk Built a Voice AI Agent That Makes 10,000 Calls a Week
Guests
Steven Payne, Product Manager, Perk
Gabriel Stock, Senior Engineering Manager, Perk
Philipe Steiff, Senior Software Engineer, Perk
What we cover in this episode
How Perk's team identified an AI use case by connecting prior experimentation with a real operational problem
Why they chose Make.com for prototyping—and shipped to production without touching backend code
The evolution from a single prompt to structured conversation stages (IVR handling, booking confirmation, payment request)
How breaking up the agent's task dramatically improved reliability
Building two eval systems: classification for success rates and LLM-as-judge for conversational behavior
Why the team still listens to calls manually even with automated metrics
The challenge of prompt engineering for voice: numbers, booking references, and text-to-speech markup
Lessons learned from expanding to German (prompts in native language improve results)
How this project uncovered other operational problems they didn't know existed
Resources & Links
Perk
Make.com – No-code automation platform used for the prototype
Twilio – Voice/telephony provider
11 Labs – Text-to-speech provider (used in early experiments)
Chapters
00:00 Introduction to the Team
01:54 Understanding PERK's Mission
02:59 Challenges in Travel Booking
07:27 AI Solutions for Customer Care
09:52 Prototyping with AI and Voice
17:00 Implementing AI in Production
25:51 Learning Through Trial and Error
26:40 Prompting Challenges and Solutions
27:58 Iterating on Prompts and Evaluations
30:08 Scaling and Production Challenges
32:43 Advanced Evaluation Techniques
35:32 Real-World Applications and Success
49:07 Future Directions and Expansion
53:53 Conclusion and Team Reflections
...more
55min
November 20, 2025Building an AI Sleep Coach: How Rest is Making CBTI Principles Accessible to DIY Sleep Hackers
Guests
Martin Siniawski, CEO and co-founder, Rest
Ignacio, CTO, Rest
You'll hear how they:
Discovered the sleep use case from podcast app user behavior (10% of users, but high willingness to pay)
Used jobs-to-be-done research to identify "DIY sleep hackers" as an underserved segment
Chose CBTI (Cognitive Behavioral Therapy for Insomnia) as their foundation—a clinically proven approach with 80% efficacy
Evolved from text chatbot to voice-first AI using Vapi for voice and OpenAI for reasoning
Built a memory system that remembers user context (like traveling, having a dog) with time-based relevance
Created dynamic agendas that drive daily conversations based on sleep data, program stage, and user compliance
Managed parallel development paths (text via OpenAI Assistants and voice via Vapi)
Moved from massive system prompts to RAG for general sleep knowledge, keeping user data in prompts
Navigated wellness vs. medical product positioning with clear guardrails against diagnosis and medication advice
Used weekly error analysis with domain experts (sleep therapists) to drive product iterations
Built LLM-powered evals for safety boundaries and experimented with Hamming for voice testing
Resources & Links
Rest – AI sleep coach app
Vapi – Voice agent platform Rest uses
Langfuse – Observability and evals platform
Hamming – Voice testing platform
AI Evals Maven Course by Hamel Husain and Shreya Shankar (Get 35% off with Teresa's affiliate link)
Chapters
00:00 Introduction to Rest and Its Founders
00:33 The Origin Story of the AI Sleep Coach
02:07 Exploring the Podcast App and Sleep Use Case
03:35 Transitioning to a Dedicated Sleep Audio App
05:47 Understanding User Segments and Sleep Challenges
07:45 Introduction to the AI Sleep Coach
13:14 The Role of Voice in the AI Sleep Coach
18:46 Daily User Interaction and Features
21:30 Prototyping and Early Learnings
28:09 Navigating Ethical and Regulatory Concerns
30:39 Navigating the Line Between Health and Wellness Apps
31:00 Incorporating Adjacent Disciplines into the App
32:15 The Power of 24/7 Availability
32:53 Evolution of the Chatbot and Error Analysis
34:49 User Experience Improvements and Voice Integration
46:49 Implementing Memory and Personalization
50:18 Dynamic Agenda and User-Centric Conversations
57:37 Evaluation and Guardrails
01:00:05 Future Roadmap and Enhancements
01:03:38 Combining Data Layers for Enhanced AI
01:06:00 Conclusion and Final Thoughts
...more
1h 7min
November 13, 2025Turning Vendor Chaos into Answers: How Xelix Built an AI Helpdesk
Guests
Claire Smid — AI Engineer, Xelix
Emilija Gransaull — Back-End Tech Lead, Xelix
Talal A. — Product Manager, Xelix
Key Takeaways
Start narrow to win: pick high-volume, high-cost requests (invoice status & reminders).
Enrichment > magic: accurate replies come from great retrieval/matching, not just a bigger LLM.
Design for adoption: familiar inbox view helps onboarding, but a ticket-first UI unlocks AI features.
Measure outcomes, not vibes: track “messages sent from Helpdesk”, % auto-resolved.
Confidence builds trust: show match quality and response confidence so humans know when to edit.
0:00 Meet the Team: Claire, Emilija, and Talal
00:36 Introduction to Xelix and Its Products
01:08 Understanding Accounts Payable Teams
01:37 Help Desk Product Overview
03:11 Challenges Faced by Accounts Payable Teams
04:03 AI Integration in Help Desk
05:47 Automating Reconciliation Requests
07:45 Development Methodology: Carpaccio
09:11 Prototyping and Beta Testing
12:00 Manual Tagging and Data Collection
16:39 Focusing on High-Impact Use Cases
18:55 User Experience and Interface Design
24:56 Pipeline Architecture and Email Processing
28:21 Data Enrichment Challenges
29:04 Handling Vendor Identification
33:33 Email Thread Management
36:15 Generating Accurate Responses
40:48 Evaluating System Performance
49:20 Future Developments and Goals
...more
54min

FAQs about Just Now Possible:

How many episodes does Just Now Possible have?

The podcast currently has 19 episodes available.