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 9 episodes available.

Just Now Possible episodes:

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
October 09, 2025Building Alyx: How Arize AI Dogfooded Its Way to an Agentic Future
Guests:
SallyAnn DeLucia, Director of Product, Arize
Jack Zhou, Staff Engineer, Arize
In this episode, we cover:
What tracing, observability, and evals really mean in GenAI applications
How Arize used its own platform to build Alyx, its AI agent
The role of customer success engineers in surfacing repeatable workflows
Why early prototyping looked like messy notebooks and hacked-together local apps
How dogfooding shaped Alyx’s evolution and built confidence for launch
Why evals start messy, and how Arize layered evals across tool calls, sessions, and system-level decisions
The importance of cross-functional, boundary-spanning teams in building AI products
What’s next for Alyx: moving from “on rails” workflows to more autonomous, agentic planning loops
Resources & Links
Arize AI — Sign up for a free account and try Alex
Arize Blog — Lessons learned from building AI products
Maven AI Evals Course — The course Teresa took to learn about evals (Get 35% off with Teresa’s affiliate link)
Cursor — The AI-powered code editor used by the Arize engineering team
DataDog — For understanding application traces
OpenAI GPT Models — GPT-3.5, GPT-4, and newer models used in early and current versions of Alex
Jupyter Notebooks — A tool for combining code, data, and notes, used in Arise’s prototyping
Axial Coding Method by Hamel Husain — A framework for analyzing data and designing evals
Chapters:
00:00 Introduction to Sally Ann and Jack
01:08 Overview of Arize.ai and Its Core Components
01:44 Deep Dive into Tracing, Observability, and Evals
03:56 Introduction to Alyx: Arize's AI Agent
04:15 The Genesis and Evolution of Alyx
08:51 Challenges and Solutions in Building Alyx
24:33 Prototyping and Early Development of Alyx
26:22 Exploring the Power of Coding Notebooks
26:51 Early Experiments with Alyx
27:59 Challenges with Real Data
29:20 Internal Testing and Dogfooding
31:55 The Importance of Evals
35:16 Developing Custom Evals
43:09 Future Plans for Alyx
47:59 How to Get Started with Alyx
...more
50min
October 02, 2025Debugging AI Products: From Data Leakage to Evals with Hamel Husain
Guest: Hamel Husain
AI products and problems discussed:
GitHub Copilot
Forecasting AirBnB Guest Growth
- NurtureBoss
Resources & Links
Hamel’s blog on AI evals
AI Evals for Engineers and PMs course on Maven (Get 35% off with this affiliate link)
Chapters:
00:00 Introduction to Hamel Hussein
00:34 Challenges in AI Consulting
02:00 Machine Learning Fundamentals
04:47 Debugging Machine Learning Models
05:00 Case Study: Airbnb's Guest Growth
08:51 Understanding Machine Learning Models
18:35 Introduction to Nurture Boss
25:40 Building AI Products with Synthetic Data
41:20 Connecting Machine Learning to Error Analysis
42:28 Real-World Example: Text Message Errors
44:15 Prioritizing and Documenting Errors
45:59 Continuous Improvement and Iteration
58:08 Using Synthetic Data for Evaluation
01:08:42 Avoiding Overfitting in Evaluations
01:19:28 Practical Tips for Error Analysis
01:25:10 Final Thoughts and Resources
...more
1h 27min
September 25, 2025Inside eSpark’s AI Teacher Assistant: RAG, Evals, and Real Classroom Needs
Guests:
Thom van der Doef, Principal Product Designer at eSpark
Mary [last name], Director of Learning Design & Product Manager at eSpark
Ray Lyons, VP of Product & Engineering at eSpark
Topics covered:
The origin story of Teacher Assistant: connecting administrator mandates with teacher needs
Why the team abandoned a chatbot interface in favor of a more structured workflow
How retrieval augmented generation (RAG) and embeddings shaped the product architecture
Lessons learned from debugging semantic search vs. keyword search
Building evals with rubrics, Braintrust, and a human-in-the-loop approach
What’s next for Teacher Assistant: more contextual recommendations using student data
Links & References:
eSpark Learning
Braintrust – evals and observability for LLM applications
AI Evals Course by Hamel Husain and Shreya Shanker (Get 35% off with my affiliate link)
Chapters:
02:05 Overview of Epar's Adaptive Learning Program
07:19 Challenges and Insights from COVID-19
17:06 Developing the Teacher Assistant Feature
24:55 User Experience and Interface Evolution
34:29 Chat GPT-5's New Features
35:16 Balancing Engagement and Efficiency
35:40 Seasonal Business and Real Traffic
36:29 Technical Decisions and RAG Implementation
38:28 Challenges with Embeddings and Metadata
41:24 Improving Recommendations and Data Enrichment
55:18 Evaluating the Teaching Assistant
01:05:51 Future Plans and User Feedback
01:07:57 Conclusion and Final Thoughts
...more
1h 9min
September 18, 2025Turning Disruption into Opportunity: The Stack Overflow AI Story with Ellen Brandenberger
Guest:
Ellen Brandenburger – Product leader and coach; former head of product at Chegg Skills and Stack Overflow’s data licensing team.
What we cover in this episode:
How Ellen joined Stack Overflow just two weeks before ChatGPT launched, reshaping the company’s future overnight
The creation of Overflow AI: a team tasked with exploring “what’s just now possible” for developers
Four iterations of conversational search:
V1: a chat UI on top of keyword search
V2: semantic search to handle natural questions
V3: fallback to GPT-4 for gaps in Stack Overflow’s corpus
V4: adding RAG for attribution and transparency
Why attribution and transparency were critical for developer trust
How the team used simple spreadsheets and subject-matter experts to evaluate answer accuracy, relevance, and completeness
Why Stack decided to sunset conversational search despite heavy investment—what they learned and why it wasn’t wasted
The pivot to data licensing: how Stack Overflow leveraged its 14M+ Q&A corpus to power LLM training and benchmarks
Building industry benchmarks with subject-matter experts to prove Stack data improved LLM accuracy and relevance
Key lessons:
Take one bite of the apple at a time—prototype, learn, iterate
Product in the AI era means managing probabilities, not certainties
Links & References:
Ellen Brandenburger on LinkedIn
The Changing State of the Internet and Related Business Models
ProLLM: LLM benchmarks for real-world use-cases
...more
1h 8min
September 10, 2025 Podcast Preview
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 real features. We dig into everything from workflows and agents to RAG and evaluation strategies, and explore how their products keep evolving. If you’re building with AI, these are the stories for you.
The first full episode drops on Thursday, September 18th. Don't miss it!
...more
2min

FAQs about Just Now Possible:

How many episodes does Just Now Possible have?

The podcast currently has 9 episodes available.