Just Now Possible

How do you build an AI product for an audience that can't afford to be wrong—and won't trust you until you prove it?

In this episode of _Just Now Possible_, Teresa Torres talks with three leaders from Healio—Jennifer Deal (SVP of Product Development), Casey Utley (Senior UX Designer), and Matthew Skepner (VP of Technology)—about how their 125-year-old medical publishing company built Healio AI, an AI-powered assistant that helps physicians prepare for patient care.

They share how a survey of 300 healthcare professionals shaped their early assumptions, why physicians surprised them by asking for help with patient communication rather than diagnostics, and how they built a working prototype in a single weekend using Cursor. You'll hear how they combined RAG with hybrid search across trusted sources like PubMed, designed citation UX that physicians actually trust, and set up eight LLM judges alongside real physician feedback to evaluate response quality.

If you're building AI for a high-stakes domain where trust, accuracy, and transparency matter more than speed, this conversation is packed with practical lessons.

When a construction company receives a bid request, someone has to open that email, parse the attached PDF (sometimes 1,800 pages describing an entire building), figure out which products are relevant, look up pricing, and draft a quote—all before the deadline. It's tedious, error-prone, and surprisingly manual.

In this episode of _Just Now Possible_, Teresa Torres talks with Daniel Kappler (CTO, Product & Design) and Matthias Hilscher (CTO, Engineering) from Tendos AI about how they're automating this entire workflow for manufacturers in the construction industry. What started as a narrow prototype matching radiator requests to product catalogs has grown into a full agentic system that handles everything from email categorization to offer generation.

You'll hear how they validated the opportunity with a design partner, spent a week on-site watching users work, and built a multi-agent architecture where specialized agents collaborate—complete with a "review agent" that checks the work of other agents before anything reaches a human. They dig into why they evaluate each agent independently (not just the whole chain), why they built custom observability tools when off-the-shelf solutions fell short, and how human-in-the-loop feedback is pushing them toward a self-learning system.

How do you help disadvantaged students take action on opportunities they don't even know exist?

In this episode of _Just Now Possible_, Teresa Torres talks with Elliot Little (Product Manager) and Dan St. Paul (Software Engineer) from Zero Gravity, a UK-based platform that helps state school students access elite career opportunities through mentoring, community, and learning pathways. They've built an AI career co-pilot that acts as an orchestrator—not an automation tool—bridging the gap between knowing what to do and actually doing it.

You'll hear how they:
- Started with grand visions of AI mentors and synthetic avatars, then scaled back to something simpler and more effective
- Discovered that hiding the "LLM magic" backfired—students needed to feel the personalization
- Built context management strategies to handle multi-month student journeys without blowing up token counts
- Approached safeguarding as a first-class concern when building AI for 16-year-olds
- Used application logic rather than complex RAG architectures to manage tool availability and context freshness

It's a practical look at building AI products that augment human relationships rather than replace them—from a team navigating the unique challenges of educational technology.

What happens when a customer reports a stolen credit card? The frontline answer is simple—freeze it. But underneath lies a cascade of follow-ups: dispute filings, fraud investigations, merchant communications, and proactive outreach to gather more details. Most AI support tools handle only the tip of the iceberg.

In this episode, Teresa Torres talks with Jack Taylor (Product Engineer) and Ibrahim Faruqi (AI Engineer) from Gradient Labs, an AI-native startup building agents that automate the full scope of customer support in fintech. They share how they've architected a platform with three coordinating agents—inbound, back office, and outbound—all built on a shared foundation of natural language procedures, modular skills, and configurable guardrails.

You'll hear how they:

- Let non-technical subject matter experts define agent behavior through natural language procedures—no coding required
- Architected a state machine orchestrator that manages turns, triggers, and skill selection across long-running conversations
- Built guardrails as binary classifiers with eval pipelines, tuning for high recall on critical regulatory checks
- Designed an auto-eval system that samples conversations for human review to catch edge cases and build labeled datasets

It's a detailed look at how one startup is moving beyond simple Q&A bots to agents that can actually take action, coordinate across workflows, and handle the messy reality of customer support.

What if your small business could have a full marketing team—automated content calendars, customer segmentation, and channel-specific posts—without the headcount?

In this episode of Just Now Possible, Teresa Torres talks with Chris O'Connor (CEO) and Jessica Valenzuela (Co-Founder) of Mowie, an AI marketing platform built for small and medium-sized businesses in restaurants, retail, and e-commerce. Chris and Jessica share how their hands-on experience managing marketing for overwhelmed business owners at a previous company led them to build Mowie—first as a concierge service, then as a fully automated AI product.

They walk through their document hierarchy approach: how Mowie crawls the web to build a "dossier" about each business, infers customer segments and marketing pillars, and generates quarterly content calendars with channel-specific posts. You'll hear about the technical challenges of structuring unstructured data, the evolution from rigid schemas to loosely structured markdown, and how they use customer feedback—from calendar approvals to regeneration requests—as their primary evaluation signal.

Whether you're building AI products that synthesize messy real-world data or figuring out how to keep humans in the loop without overwhelming them, this conversation offers practical lessons from two founders who built their product by doing the work first.