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 25 episodes available.

Just Now Possible episodes:

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
November 06, 2025When AI Becomes Your SRE: How Incident.io Is Automating Incident Response
Guests
Lawrence Jones, Founding Engineer at Incident.io
Ed Dean Product Lead for AI at Incident.io
Key Takeaways
AI’s biggest impact comes from compressing time—identifying causes minutes instead of hours.
Retrieval-augmented reasoning still benefits from simplicity: deterministic tagging and re-ranking often beat complex vector setups.
Post-incident “time travel” evals let teams score AI accuracy after they know what really happened.
Building trust in AI isn’t just about precision—it’s about showing reasoning and uncertainty in ways humans understand.
Mentioned Tools & Concepts
Slack as the interface for human-AI collaboration
PGVector and Postgres for retrieval experiments
RAG (Retrieval-Augmented Generation)
Multi-agent orchestration
“AI as your company’s immune system”
Chapters
00:00 Meet the Founders: Lawrence and Ed
00:41 Introduction to Incident.io
01:25 Evolution of Incident.io Products
02:14 Understanding SRE and Its Importance
04:01 Real-World Incident Management
05:51 The Role of AI in Incident Management
10:12 Challenges and Innovations in AI SRE
12:14 Prototyping and Iterating AI Solutions
16:25 Refining Retrieval Strategies
21:52 Balancing AI and Human Interaction
32:06 User Experience and Trust in AI Systems
36:08 Interactive Slack Integration
37:08 Understanding the AI Investigation Process
37:50 Parallel Checks and Data Sources
38:35 Building Hypotheses and Refining Findings
40:09 Human-Agent Collaboration
49:23 Evaluating AI Effectiveness
a01:04:13 Future Developments and Integrations
...more
1h 9min
October 30, 2025Building Trainline’s AI Travel Assistant: How a 25-Year-Old Company Went Agentic
Guests
David Eason, Principal Product Manager at Trainline
Billie Bradley, Product Manager, Travel Assistant at Trainline
Matt Farrelly, Head of AI and Machine Learning at Trainline
Key Takeaways
AI assistants need both scalable reasoning and deep domain context to be useful.
Tool design and guardrails are as critical as prompt design in agent systems.
LLM-as-judge evals make it possible to measure open-ended systems without massive labeling costs.
Even legacy companies can move fast when they embrace experimentation and tight PM–engineering collaboration.
Chapters:
00:00 Introduction and Team Introductions
00:51 Overview of Trainline's Mission and History
02:30 AI Integration in Trainline's Services
05:08 Challenges and Solutions in AI Implementation
06:52 Building and Iterating the AI Travel Assistant
14:58 User Experience and Guardrails
22:26 Technical Challenges and Solutions
34:29 The Challenge for Product Managers in AI
34:55 Billy's Background in AI
35:42 The Rapid Evolution of AI Technology
37:14 Managing Information Overload
37:58 Collaboration Between Product Managers and Engineers
38:42 Trainline's Approach to Machine Learning
39:36 Scaling Up: From 450 to 700,000 Pages
40:21 Challenges in Data Retrieval and Processing
45:55 Evaluating AI Assistants
48:22 The Role of LLM as Judges
50:19 User Context Simulation for Real-Time Evaluation
01:06:56 Future Directions for Trainline's AI Assistant
...more
1h 9min
October 23, 2025Powering Government with Community Voices: How ZenCity Built an AI That Listens
Guests
Noa Reikhav, Head of Product, Zencity
Andrew Therriault, VP of Data Science, Zencity
Shota Papiashvili, SVP of R&D, Zencity
In this episode
How Zencity helps local governments reach, understand, and act on community voices
Turning thousands of survey responses, social posts, 311 calls, and news items into usable insight
Building a data model with multiple layers—raw data → elements → highlights → insights → briefs
Why context is everything when building AI for civic use
How the team designed their AI assistant using MCP servers to safely negotiate data access
Balancing agentic flexibility with deterministic trust
Evaluating accuracy when latency matters: how they think about evals, citations, and model-as-judge systems
Using workflows like annual budgeting or crisis communication to deliver AI-generated briefs to the right people at the right time
Why government workflows are the ultimate “jobs to be done” framework
Takeaways
Data architecture defines what AI can do.
Guardrails and transparency matter more than flashy outputs.
Agentic systems become powerful when grounded in real, multi-tenant data.
AI in the public sector can make democracy more responsive—if built responsibly.
Chapters:
00:00 Introduction to the Team
00:16 What is ZenCity?
01:26 AI in ZenCity's Platform
06:00 Survey Methodologies and Use Cases
09:01 Community Voices and Social Listening
14:36 Workflows and AI Integration
22:15 Annual Budget Planning Workflow
32:44 Data Layers and Sentiment Analysis
33:53 Post Interaction Surveys and Resident Engagement
34:20 Data Enrichment and Sentiment Analysis
35:14 Topic Modeling and Semantic Search
36:50 AI Content Summarization and User-Driven AI Assistant
38:53 Highlights, Insights, and the Gold Layer
41:19 Challenges and Solutions in AI Data Processing
46:47 AI Assistant and Guardrails
01:05:27 Future Developments and Orchestration Layer
01:06:44 Conclusion and Final Thoughts
...more
1h 8min
October 16, 2025Building AI Coworkers: How Neople Is Making Agents Work Where You Work
Guests:
Seyna Diop, Chief Product Officer, Neople
Job Nijenhuis, CTO & Co-founder, Neople
Christos Constantinou, Lead Design Engineer, Neople
Chapters:
00:00 Meet the Team: Introducing Neople's Key Players
01:16 Understanding Neople's Product: Digital Coworkers
03:25 The Origin Story: How Neople Came to Be
06:24 Customer Success: Real-World Applications of Neople
12:36 Evolution of Neople: From Suggestions to Automations
15:18 Integrations and Scaling: Making Neople Work Everywhere
20:33 Ensuring Quality: Feedback and Evaluation Mechanisms
37:13 Ensuring Quality in Customer Knowledge Bases
37:45 Visibility and Transparency in Neople Responses
39:42 Customer Feedback and Error Analysis
42:57 Technical Details of Knowledge Retrieval
49:15 Embedding Strategies and Document Types
57:06 Automation and Actions in Customer Support
01:06:37 Expanding Beyond Customer Support
01:11:02 Conclusion and Future Directions
Links & References:
Neople.io – Learn more about Neople’s AI coworkers
The Joy Lab – Neople’s community and podcast about AI and work
...more
1h 13min

FAQs about Just Now Possible:

How many episodes does Just Now Possible have?

The podcast currently has 25 episodes available.