Just Now Possible

What does it take to build an AI that can take a food order over WhatsApp — correctly, every time, fast enough that customers can't tell it's not a person? That's the core challenge Santi Marchiori and Juan Haedo set out to solve at AITropos, a company building AI employees for the hospitality industry.

In this episode of Just Now Possible, Teresa Torres talks with Santi Marchiori (CEO) and Juan Haedo (CTO) of AITropos about how they built an AI order-taking agent that handles the full flow — menu recommendations, modifiers, delivery zones, payment links, and status updates — entirely inside WhatsApp. They went through three product iterations to get there: first a hardware device for waiters, then a waiter-facing app, and finally a customer-facing conversational agent powered by a tools-based architecture designed for speed and reliability.

You'll hear how they solved the core technical challenge of translating non-deterministic human conversation into structured POS-compatible order data, why they chose tools over MCP for agent architecture, how they pre-inject product context to cut latency before the agent ever makes a tool call, and why they test with thousands of agent-simulated customer conversations overnight before deploying to any real venue.

How do you turn a rambling stream of consciousness into a clean task list — while the person is still talking? That's the core challenge Doist solved with Ramble, a voice-to-task feature inside Todoist that uses live audio AI to capture tasks in real time, no transcription step required.

In this episode of Just Now Possible, Teresa Torres talks with Ernesto Garcia (Front-end Product Engineer), Thomas Jost (Backend Software Engineer), and Hugo Fauquenoi (Product Manager) from Doist about how they built Ramble — Todoist's first pure AI feature. What started as a two-to-three month AI exploration phase became one of the most technically deliberate features they've shipped: a Gemini-powered pipeline that makes tool calls while the user is still speaking, surfacing tasks on screen in real time without any text output from the model.

You'll hear how they designed around the "brain dump" behavior they found in user research, why they chose direct context injection over RAG for project and label matching, the surprising complexity of date handling in a live audio pipeline, and how they built a multi-language eval system using real employee recordings across 35 countries. It's a detailed look at the discipline of keeping AI features simple, constrained, and genuinely useful.

What if the future of product design isn't about replacing designers — it's about giving every team access to one? For solo founders, stretched design teams, and early-stage startups, great UX has always been out of reach. Banani is trying to change that by building an AI product designer that doesn't just generate code — it generates design.

In this episode of _Just Now Possible_, Teresa Torres talks with Vlad Solomakha (CEO & Co-founder), Vova Parkhomchuk (CTO & Co-founder), and Vlad Ostapovats (Founding Growth) about how they built Banani from a Figma plugin proof-of-concept into a canvas-first AI design tool generating hundreds of thousands of designs per week. Vlad Solomakha brings a decade of design experience to the product — and a very specific vision of what it means for AI to produce beautiful, tasteful design rather than average, undifferentiated UI.

You'll hear how they engineered their agent to handle parallel screen edits, manage per-screen context across canvases with hundreds of frames, and make surgical edits without regenerating entire screens. They dig into the "gulf of specification" — the mismatch between how designers think visually and how agents understand text — and what they're building to close it.

It's a detailed look at what it takes to build an AI-native design tool that puts the designer in the driver's seat while letting the agent handle the production work.

What if AI could help reduce the 10-plus years it takes to get a new drug to market? That's the driving ambition behind Medable's agentic platform—and the bet that led them to build Agent Studio.

In this episode of Just Now Possible, Teresa Torres talks with four members of the Medable team: Luke Bates (Product Leader, Agent Studio), Jen Brown (Product Manager), Matt Schoolfield (Product Designer), and Fiachra Matthews (Principal Architect). Together they share how Medable—a clinical trial platform used by global pharmaceutical companies—built Agent Studio, a no-code/low-code platform for configuring and deploying agents across the clinical trial lifecycle.

You'll hear about the two agents they've built on top of it: an ETMF agent that automates document classification across 80,000-plus documents per year, and a CRA agent that monitors patient safety and data quality across 13 different clinical systems. The conversation goes deep on the architecture behind it all—how they handle RAG and context management at scale, why they built custom MCPs with an authentication layer, how they designed evals for a regulated GXP environment, and what human-in-the-loop really looks like when clinical decisions are on the line.

It's a rare look inside an enterprise AI platform built for one of the most regulated industries in the world—and a team that's still figuring it out in real time.

What if an AI could spot the moment two product teams start pulling in opposite directions -- before it derails a quarter?

In this episode of Just Now Possible, Teresa Torres talks with Matthias and Charlotte Kleverud, co-founders of Momental, about their vision for building what they call "GitHub for product management." Momental ingests documents, meeting transcripts, and voice recordings across an organization, then uses AI agents to map them into a structured context layer—a set of interconnected trees covering goals, decisions, learnings, and who's doing what. When it finds a conflict—say, one team betting on retention while another is prioritizing conversion—it surfaces the misalignment for humans to resolve, just like a merge conflict in code.

You'll hear how their journey started in 2022 with GPT-3, pivoted through a multi-agent team that exposed the real problem (agents need aligned context, too), and landed on an OODA-loop-driven document processing agent that builds and maintains a living knowledge graph. It's a detailed look at how domain-specific context modeling, human-in-the-loop conflict resolution, and self-improving agents come together to tackle one of the hardest problems in product organizations: keeping everyone aligned.