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 30 episodes available.

Just Now Possible episodes:

July 23, 2026Building AI for Women's Health: How Hertility Combined Bayesian Diagnosis and Scan Automation
Guests
Tulsi Patel, Director of Product and Technology, Hertility
Lorna Brightmore, Head of Data and AI, Hertility
Jack Pickard, Head of Engineering, Hertility
In this episode
What makes Hertility's data set unique: seven years of linked symptoms, blood tests, and pelvic scans from over a million women
How Gyn.AI uses a Bayesian network to give clinicians probability-based diagnoses instead of binary yes/no calls
Why showing clinicians the reasoning behind a diagnosis—not just the label—builds trust and speeds up triage
Guarding against automation bias with holdout sets and independent, fresh-eyes review
Inside the scan automation pipeline: classifying ultrasound images, detecting follicles, and measuring ovarian volume more precisely than manual methods
Using an agentic loop to check AI-drafted clinical letters against patient data and catch hallucinations before a human sees them
The infrastructure challenge of securely piping DICOM ultrasound images from third-party scan providers into Hertility's systems
How Hertility handles PII and PHI: pseudonymization, data minimization, and running models in-house on AWS Bedrock
Why treating healthcare regulation as a product requirement from day one makes AI products more scalable, not slower
Key Takeaways
Probabilistic, transparent AI outputs build more clinician trust than binary classifications.
Guardrails against automation bias are as important as the model itself.
Data minimization and in-house infrastructure make it possible to build AI responsibly with sensitive health data.
Treating regulation as a design constraint from day one makes AI products more defensible and scalable, not slower.
Resources & Links
Hertility — At-home hormone testing and reproductive health diagnostics for women in the UK and Ireland
AWS Bedrock — The platform Hertility uses to run LLMs in-house under its own governance and regulatory controls
PyTorch — The foundation for Hertility's in-house image classification and contouring models
Chapters
00:00 Meet the Team
00:13 What Hertility Does
01:51 How Customers Access It
04:06 A Unique Women’s Health Dataset
07:03 Mission and Efficiency with AI
10:03 Why Long Assessments Convert
13:52 Before AI Workflows
16:52 Research Publications and Impact
18:48 GynAI Reducing Time to Diagnosis
21:21 Triage and Clinician Support
24:37 Keeping Patient UX the Same
26:12 Bayesian Network and Explainability
30:19 Multiple Diagnoses and Probabilities
32:37 Probabilistic Diagnosis Shift
33:50 Clinician Adoption and Workflow Fit
34:58 Communicating Medical Uncertainty
36:43 Scan Automation Overview
40:30 In House Image Analysis
44:25 DICOM Pipeline Engineering
47:30 Evals and Automation Bias
50:31 LLM Letter Guardrails
56:47 PHI Handling and Regulations
01:00:43 Infrastructure Choices and Wrap Up
...more
1h 5min
July 09, 2026From COVID Pivot to AI World Building: How Snapbar Reinvented the Photo Experience
Guests
Sam Eitzen, Co-founder & CEO, Snapbar
Joe Eitzen, Co-founder & CPO, Snapbar
Patrick Ellis, CTO, Snapbar
You'll hear how they:
Pivoted from physical photo booths to a cross-platform virtual product in spring 2020 using WebRTC—built from first principles out of necessity
Integrated Stable Diffusion 1.5 as their first generative AI model and ran custom LoRA fine-tunes on H100/H200 GPUs to produce brand-quality outputs nobody else in their space could match
Evolved from negative prompts to reasoning model long-form prompts, giving brands more precise creative and safety control
Built a meta-prompting pre-processing pipeline to ensure user likenesses—including non-obvious details like disabilities—are accurately represented in generated images
Designed an experiential marketing platform that lets brands "world build" at conferences, trade shows, and live events by bringing fans into branded creative worlds
Added participatory user inputs through Mad Lib-style prompts and prompt injection, turning photo experiences into co-creation moments between brands and their audiences
Used Claude Code and Codex to build and ship features rapidly as a small bootstrap team, and developed a four-pillar agent orchestration framework: context, tools, verification, and workflows
Are building customer-facing "vibe coding" using the Claude Agent SDK so brands can configure and create experiences themselves within Snapbar's platform
Resources & Links
Snapbar — AI photo booth and experiential marketing platform
Patrick Ellis on YouTube — Patrick's channel on AI engineering and agent orchestration
Sam Eitzen on LinkedIn — Follow Sam for experiential marketing insights
Claude Code — The AI coding tool Patrick and Teresa both use heavily
Stable Diffusion — One of the generative image models in the mix Snapbar evaluates for its AI photo experiences
Black Forest Labs / Flux — Another model family Snapbar tests and can route activations to, depending on the creative style needed
Chapters
00:00 Meet the SnapBar Team
00:59 From Photo Booth Side Hustle
02:31 Pandemic Pivot to Virtual
08:06 What AI Photo Booth Does
11:03 Why It Works at Events
13:15 When AI Entered the Product
14:26 Early GenAI Tech Stack
17:43 Patrick’s Learning Path
20:44 Why They Bet on AI
28:25 Building with Necessity
34:25 Applied AI Beats Models
36:01 Prompting Got Easier
37:53 Arcades to Home Shift
38:58 Commoditization vs Application Layer
40:58 Experiential Platform Overview
42:33 World Cup Activation Examples
45:34 QR Flow and WebRTC
47:51 Capture to Display Pipeline
49:24 Dark Factory and Vibe Coding
55:38 Brand Safety Prompting
01:00:23 Meta Prompting and Representation
01:03:22 Experiential Marketing Momentum
01:06:12 Agent Orchestration Framework
01:10:02 Claude Code and Fable Rant
01:12:48 Wrap Up and Where to Find Them
...more
1h 15min
June 25, 2026Is This Okay? How Override Labs Built a Safety-First AI Consent Coach for Teen Boys
Guests
Priya Nakra, Founder and Product Lead, Override Labs
Olivia Rowley, AI Advisor and Board Member, Override Labs
In this episode
Why Priya left a 10-year tech career to found a nonprofit focused on gender-based violence prevention
How scraping 2,000 Reddit posts per subreddit validated demand for a consent reflection tool
Why Override Labs defined a "South star" — the worst-case outcome — and designed the product to avoid it
How a licensed therapist and positive masculinity coaches shaped the product's tone and eval rubric
Why the product never gives a "green flag" response — and what it offers instead
How risk classification runs deterministically before Claude is ever called, then tailors the response by tier
The three-part response structure grounded in motivational interviewing: validate, reflect, invite reflection
Why "privacy by design" meant no accounts, no cookies, no cross-session tracking — and how that became a feature
The challenge of measuring prevention when success means something didn't happen
How Override Labs is building a product ecosystem: a women's tool, a web game as a top-of-funnel, and a future institutional API layer
Resources & Links
Override Labs — Building technology to prevent harm from gender-based violence
The Fund to Prevent Sexual Assault — The philanthropic incubator that funded Override Labs' first cohort
You can find Override Lab's products here:
Is this okay?
Game Boi
Chapters
00:00 Meet Priya and Olivia
00:50 Why Override Labs Exists
02:11 From Tech to Mission
04:51 Is Technology Good
07:49 Narrowing to Teen Consent
11:07 Researching Teen Scenarios
14:33 Clinical Guidance and Tone
16:48 Designing for Reflection
20:51 Prototype to Product Flow
24:46 Risk Tiers and Evals
28:10 Defining the Conversation Goal
29:35 Motivational Interviewing Framework
31:37 Measuring Real World Impact
35:39 Privacy First Data Practices
40:29 Reaching Teens With Ads
41:56 Web Game Funnel Strategy
44:30 Funding and Institutional Pathways
46:50 UX for Sensitive Moments
49:34 Matching the Older Brother Tone
52:09 Building Safe AI for Prevention
54:35 Closing Thoughts and Thanks
...more
56min
June 11, 2026Beyond Black Box Scores: How Musubi Trains Custom AI for Trust and Safety Teams
Guests
Nikki Marinsek, Data Scientist, Musubi
Brian McCaffrey, Software Engineer, Musubi
Dan Means, Machine Learning Engineer, Musubi
In this episode
Why off-the-shelf moderation scores fail and how custom-trained models fix that
How Musubi combines traditional ML with LLMs for different moderation tasks
The discovery that AI can outperform human moderators—and how to communicate that to clients
Using AI as a judge to referee disagreements between AI and human decisions
How Musubi onboards new customers with "reverse demos"
What custom model training actually means: fine-tuning, feature engineering, and reusable deployment pipelines
The policy optimizer: an agentic flow that helps customers iterate on their LLM moderation policies
Why pushing eval tools directly to customers is a core product strategy
How Musubi is building flexible orchestration workflows for non-technical trust and safety teams
Resources & Links
Musubi — AI-powered trust and safety toolkit for content platforms
Maven AI Evals Course — The course Teresa took to learn about evals (get 35% off with Teresa's affiliate link)
Chapters
00:00 Meet the Team
01:18 Why Everyone Wears Product
02:32 What Musubi Builds
04:51 AI for Human Moderation
09:59 Adversaries and Asymmetry
11:48 Early Days and Low Latency
13:35 First Prototype Slice
15:33 Traditional ML Meets LLMs
19:52 Benchmarking Against Humans
23:09 LLM as Judge and Policy Gaps
29:53 From Prototype to Platform
31:15 Customer Onboarding Reverse Demos
36:08 Custom Models Per Customer
38:05 Fine Tuning vs Training
39:14 Embedding Driven Classification
40:04 Cost and Latency Tradeoffs
43:21 Productizing Customization
49:16 Scaling Prototypes to Production
51:58 Golden Sets and Policy Loops
56:17 Coaching Customers Safely
01:02:06 Gamified Feedback Signals
01:06:19 Agentic Toolkit Roadmap
01:09:05 Workflow Orchestration Future
01:12:06 Wrap Up and Thanks
...more
1h 13min
May 28, 2026Building Lorikeet: How AI Humility and a Dual-Agent Architecture Are Redefining Customer Support
Guests:
Jamie Hall, Co-founder & CTO, Lorikeet
Xharmagne Carandang, Product Engineer, Lorikeet
Rona Wang, Product Engineer, Lorikeet
In this episode:
How Lorikeet evolved from failed ops tools to a full AI customer support concierge
The dual-agent architecture: Concierge for customer tickets, Coach for configuration and ongoing improvement
Why "AI humility" — defaulting to human handoff when uncertain — is a core design principle
How Lorikeet integrates with Zendesk and Intercom instead of replacing them
The UX evolution from workflow builder to conversational interface — and why the blank chat box is still hard
"Resolution in the loop": how human agents unblock the AI without taking over a ticket
Why guardrails need to be domain-specific — the cannabis company story
How customers define their own evals and guardrails through the Coach interface
Using AI to diagnose failure modes in traces and automatically suggest fixes
Lorikeet's product engineering culture: every engineer asks weekly what they learned from a customer
Resources & Links:
Lorikeet — AI customer support concierge for enterprises in regulated industries
Gradient Labs on Just Now Possible — another AI agent team in regulated financial services
Neople on Just Now Possible — AI digital coworkers with a similar training-by-conversation approach
Incident.io on Just Now Possible — AI SRE with multi-agent hypothesis investigation
Chapters
00:00 Meet the Team
01:05 What Lorikeet Builds
02:34 Origin Story and Early Missteps
06:42 Finding the Real Support Pain
07:37 Why AI Fits Support Work
11:16 First Prototype and Early Evals
14:42 Design Partners and Selling the CLI
16:30 Product Mindset and the Real Moat
19:47 Rona Joins and Scaling Up
21:02 Milestones Voice Actions Escalation
23:48 Integrations with Zendesk Intercom
25:59 How the Agent Works Today
28:30 Coach Agent and Configuration UX
32:58 SOPs to Test Cases
34:35 Refund Flow Setup
36:12 Coach Conversational UI
38:12 Hybrid UX Guidance
40:46 Resolution in Loop
43:17 Collaboration Middle Ground
49:40 Process Maturity Limits
53:30 Confidence and Guardrails
55:59 Customer Defined Guardrails
01:01:14 Trace Diagnosis Agent
01:03:14 Product Engineers Culture
01:07:46 Closing Thoughts
...more
1h 9min
May 14, 2026Building Rhea's Factory: How AI-Designed Enzymes Could Finally Solve Plastic Recycling
Guests
Arzu Sandıkçı, Co-founder & CEO, Rhea's Factory
Mert Topcu, Co-founder, Rhea's Factory
In this episode:
Why only 10% of plastic gets recycled—and why mechanical and chemical methods hit a ceiling
How enzymatic recycling breaks plastic all the way back to its original monomers, unlike traditional methods that just shorten polymer chains
Why enzymes are selective: they can target specific plastic types even in mixed waste streams
The discovery of a plastic-eating bacteria in Japan that opened the door to enzymatic recycling
How AlphaFold and the Nobel Prize in Chemistry transformed what's possible in enzyme engineering
How Rhea's Factory uses protein language models (PLMs) and multi-step AI pipelines to design novel enzymes computationally
The evolution from a human-orchestrated pipeline to an agentic AI scientist
How guardrails at each pipeline step keep the AI pointed in the right direction without limiting exploration
Why wet lab data—even just hundreds of proprietary data points—can be enough to train a powerful domain-specific prediction model
Why Mert sometimes wants the model to hallucinate (and how high temperature settings help explore the full enzyme design space)
The business constraint: enzymatic recycling must compete economically with cheap, oil-based plastic production
What's next: a process agent, a 5,000-ton demo plant in California, and enzymes for new plastic types
Resources & Links
Rhea's Factory — Enzymatic plastic recycling technology
AlphaFold — DeepMind's AI system for protein structure prediction (inspiration for the Nobel Prize in Chemistry)
Maven AI Evals Course — The course Teresa took to learn about evals (35% off with Teresa's affiliate link)
Chapters
00:00 Meet the Founders
01:50 Why Plastic Circularity
03:19 Mechanical vs True Recycling
04:52 Biology as the New Tool
07:20 Necklace and Pearls Analogy
13:22 Low Energy Reactor Process
17:33 Origin Story and PET Enzyme
22:52 Protein Folding and AlphaFold
28:32 AI Designed Enzymes
34:28 Protein Language Models Stack
37:14 Multi Step Protein Generation
39:00 Building on Foundation Models
40:50 Lab First Success Metrics
43:10 From Human to Agentic Orchestration
43:59 Problem Statements as Inputs
46:18 Guardrails at Every Stage
47:48 Prediction Models and Data Limits
50:03 Industrial Reality and Cost
52:30 Agentic Parallels and Orchestrators
57:45 Impact on Timelines and Diversity
01:03:23 When Hallucination Helps
01:04:09 Scaling Up and Process Agents
01:06:56 Enzyme Blends for Mixed Plastics
01:07:49 Why Clamshells Aren't Recyclable
01:09:34 Closing Thoughts and Thanks
...more
1h 11min
April 30, 2026Building AI Employees for Hospitality: How AITropos Takes Orders Where Customers Already Are
Guests
Santi Marchiori, CEO, AITropos
Juan Haedo, CTO, AITropos
You'll hear how they
Spent two years exploring hundreds of startup ideas before finding the specific niche of AI-powered order taking in hospitality
Went through three product iterations — hardware for waiters, a waiter app, and finally a customer-facing WhatsApp agent — before landing on the right form factor
Identified order item identification accuracy as their single most important KPI
Chose a tools-based agent architecture over MCP or pipelines to hit real-time response speed requirements
Built a parallelized pipeline that searches for multiple products simultaneously and pre-fetches product context before the agent even calls a tool
Use smaller, fast sub-agents to build an "immediate system prompt" that injects relevant data into each turn without extra tool calls
Test with thousands of agent-simulated customer conversations run overnight before deploying to new venues
Reduced new customer onboarding from three months to a few weeks — and continue to shrink it as they build domain templates
Resources & Links
AITropos
Chapters:
00:00 Meet the Founders
00:59 What Tropos Builds
01:51 AI vs Human Touch
06:17 Restaurant Use Cases
08:16 Why Hospitality
10:47 Finding the Wedge
16:00 Early Prototypes
16:46 Hard Parts of Ordering
18:03 Speed and Channels
21:15 Iteration and Model Jumps
30:50 Customer Order Flow
35:48 Menu Discovery Question
36:07 Menus Inside WhatsApp
36:50 Finding the Chat Entry
37:37 Why Text Ordering Wins
38:30 Under the Hood Pipeline
40:54 Tools Over Workflows
45:05 Tooling and Prompt Composer
49:29 Preloading Context Fast
54:02 Founder Learning Mindset
57:21 Evaluating Order Accuracy
01:00:03 Testing and Human Takeover
01:03:56 Onboarding and Scaling Up
01:06:10 Whats Next and Wrap
...more
1h 8min
April 16, 2026Building Todoist Ramble: How Doist Turned Voice Braindumps into Real-Time Task Capture
Guests
Ernesto Garcia, Front-end Product Engineer, Doist
Thomas Jost, Backend Software Engineer, Doist
Hugo Fauquenoi, Product Manager, Doist
In this episode
How Doist's 2-3 month AI exploration phase led to Ramble — and why voice-to-task emerged as the top contender
The user research insight behind Ramble: people using pen and paper or ChatGPT voice to brainstorm tasks before committing them to Todoist
Why Ramble skips transcription entirely and processes raw audio directly with a Gemini live audio model
How the model makes tool calls (add task, edit task, delete task) in real time while the user is still speaking — no text output at all
Designing for the driving use case: sound effects as audio confirmation cues alongside visual task cards
The challenge of teaching an LLM to capture tasks literally without over-interpreting or doing them — and how temperature tuning played a role
Date handling complexity: injecting the current date, normalizing to days vs. months, and always outputting dates in English for the natural language parser
Building an LLM-judge eval system with 20+ language recordings from 100+ employees across 35 countries to catch prompt regressions
Why Doist chose to inject the full project/label list into the system prompt instead of building a RAG pipeline — and why it worked
How easy correction beats perfect first-time accuracy in natural language interfaces
What's next: multimodal task capture from images and text blobs, Apple Watch support, and automation integrations
Resources & Links
Todoist
Doist
Google Vertex AI (Gemini)
Chapters:
00:00 Meet the Doist Team
01:40 What Doist Builds
02:27 Ramble Voice to Tasks
04:16 Why Voice Matters
07:42 Brain Dump Insight
09:46 Prototyping With LLMs
11:08 Live Audio Workflow
14:32 Driving Friendly UX
18:47 Tool Only Architecture
26:06 Evals and Multilingual Testing
28:41 Taming Dates and Time
33:28 Fixing Date Confusion
33:43 Defining Task Boundaries
34:34 Capture Versus Do
37:17 Tuning Creativity Levels
39:01 Evals Across Languages
41:23 Feedback and Regressions
44:09 Model Upgrades Over Time
46:33 Projects Labels Context
51:40 Handling Ambiguous Names
54:23 Whats Next Multimodal
58:48 From Capture to Execution
59:46 Closing Thoughts
...more
1h 1min
April 02, 2026Building Banani: How a Canvas-First AI Designer Is Raising the Floor on Product Design
Guests
Vlad Solomakha, CEO & Co-founder, Banani
Vova Parkhomchuk, CTO & Co-founder, Banani
Vlad Ostapovats, Founding Growth, Banani
In this episode
Why Banani started as a Figma plugin and what they learned from early organic distribution
The canvas-first approach: why Banani is built around a design canvas rather than a chat interface
How their agent architecture splits prompts into surgical edits instead of regenerating full screens
The "gulf of specification" problem and what Banani is building to help agents and designers speak the same visual language
Managing context across canvases with hundreds of screens — per-screen history with shared project context
Why Banani doesn't compile running applications — just HTML/CSS mockups — and how that shapes everything
How they evaluate design quality without traditional evals: spinning up 10 screens from one prompt to compare models
Their approach to building at the edge of what's possible: identifying which model limitations to work around vs. wait out
The role of context engineering and specialized agent tools in producing tasteful, high-quality design
Resources & Links
Banani
TL Draw
Chapters
00:00 Meet the Founders
01:12 What Bonani Builds
02:18 Why an AI Designer
03:40 Raising the Design Floor
06:23 Why AI Was Finally Ready
10:48 First Prototype Figma Plugin
14:10 Early Growth and Distribution
15:25 Standing Out in a Crowded Market
20:13 Product Tour Canvas First AI
23:40 Autopilot vs Manual Control
27:07 Tech Behind High Quality Design
32:08 Craft Beyond 80 Percent
33:40 Gulf of Specification
36:44 Proactive Agent Interviews
38:40 Canvas First UX Choices
42:54 Agent Architecture Under Hood
48:48 State History Context Tricks
52:32 Tooling Context Engineering
56:04 Navigating Busy Canvases
01:00:13 Betting on Model Progress
01:03:47 Shipping Around Imperfections
01:07:20 Try Banani and Next Steps
01:07:52 Building the Banani MCP
01:09:19 Final Thanks and Wrap
...more
1h 11min
March 19, 2026Building Agent Studio: How Medable Is Using Agentic AI to Accelerate Clinical Trials
Guests
Luke Bates, Product Leader (Agent Studio), Medable
Jen Brown, Product Manager, Medable
Matt Schoolfield, Product Designer, Medable
Fiachra Matthews, Principal Architect, Medable
What we cover in this episode:
What Medable does: enabling global clinical trials across 100+ languages and accelerating drug-to-market timelines
The two agents built on Agent Studio—ETMF (document classification) and CRA (clinical data monitoring)—and the problems they solve
Why Medable chose a platform approach to agents instead of one-off builds
How Agent Studio works: models, skills, knowledge bases, MCP connectors, versioning, and trigger types
Three deployment models: Medable-built products, services-led custom builds, and self-serve platform access
RAG approaches at scale: embeddings vs. markdown hierarchies vs. just-in-time MCP retrieval
How they built a unified ontology layer to map terminology across 13 different clinical data systems
Why they built custom MCPs with an authentication and credentialing wrapper
Context window management with sub-agents and automatic tool filtering
Evaluation design in a GXP-regulated environment: golden datasets, production monitoring, and the challenge of human feedback as ground truth
How they document agent intent → specification → test evidence to satisfy regulatory bodies
The "full self-driving" vision for clinical trials and what it would take to get there
Resources & Links
Medable - Clinical trial platform powering Agent Studio
Chapters
00:00 Meet The Medable Team
01:14 Medable Mission And Scope
03:27 Agent Studio Platform Overview
06:29 ETMF Document Automation
08:47 CRA Agent For Monitoring
10:40 Clinical Trial Workflow Primer
14:34 Why Build A Platform
17:51 Learning AI As A Team
21:47 Early Days Of Agent Studio
23:15 How Agents Are Built
25:15 Customer Adoption And UX
30:00 Skills And MCP Standards
31:15 Scaling Context Retrieval
33:07 RAG Patterns And Tradeoffs
34:48 Ontology Data Layer Explained
38:01 Customer Friendly Agent Setup
42:19 MCP Security And Connectors
44:36 Tool Bloat And Subagents
50:44 Evals For Reliable Agents
54:40 Human Feedback Isn’t Truth
57:43 GXP Compliance For Agents
01:03:34 Full Self Driving Trials
...more
1h 7min

FAQs about Just Now Possible:

How many episodes does Just Now Possible have?

The podcast currently has 30 episodes available.