Future Proof: Building AI Products that Last

AI in Finance: Solving the "Last Mile" Problem with Morten Bruun | Product at Hebbia

How do you build AI for industries where "average" is a failure? In this episode of Future Proof, Morten Bruun, Member of Technical Staff and Product Manager at Hebbia, breaks down why off-the-shelf models like Claude and ChatGPT aren't enough for the high-stakes worlds of finance and legal.Morten shares his journey from co-founding Flash Docs (acquired by Happier) to developing a platform that manages terabytes of unstructured data for leading investment banks and law firms. We dive deep into: * The "Last Mile" Challenge: Why 90% accuracy is effectively 0% for professional standards.Process Engineering: Moving beyond chatbots to create a "canvas" for team-specific workflows. * The SaaS-pocalypse: How software companies can differentiate as the cost of code drops to zero. * The Technical PM: How AI is compressing feedback loops and turning product managers into builders. Whether you’re a founder navigating the "SaaS-pocalypse" or a product leader integrating AI, Morten’s insights on winning trust in regulated industries are essential.Ready to scale your product’s integrations without the engineering overhead? Visit UseParagon to learn how to ship production-ready integrations in days, not months.Chapters00:00 - Meet Morten Bruun and Hebbia00:42 - From Flash Docs to Acquisition: Morten’s Journey03:15 - Why Finance Needs an Edge (The Problem with "Average" AI)05:18 - Process Engineering: Moving Beyond the 30,000ft View08:02 - The Retrieval Gap: Analyzing Terabytes vs. 20 Documents11:19 - Solving the "Last Mile" for Professional Standards14:19 - High-Stakes Outcomes: Why Precision Matters17:16 - The "Canvas" Model and AI Strategists21:54 - Surviving the SaaS-pocalypse: Differentiation in the AI Age25:49 - The Changing Role of Product Management and Technical Staff36:01 - Winning Trust in Highly Regulated Industries40:43 - Customer Empathy: Focusing on the "Job to be Done"

Braze’s approach to the future of AI-powered personalization

In the latest episode of Future Proof, we sat down with Kevin Wang, Chief Product Officer at Braze, to unpack how AI is reshaping the relationship between brands and consumers and what marketers need to do to stay ahead of the shift.We cover: * Why the next big ad surface isn't search or social and how LLMs will know what you want before you ask * How personalization is evolving from coarse-grained categories to something closer to music taste than ice cream flavor * Why the real battle in AI-driven commerce isn't capability, it's who owns the consumer context * How Braze is using reinforcement learning and in-product agents to give marketers exponential strategic leverage * Why human-in-the-loop isn't going away and how the role of the marketer is shifting up the stack * What every major platform shift tells us about where brand-to-consumer relationships are headed next Highlights: 00:00 — AI as the Next Big Ad Surface: Why LLMs Are Opening Up a Platform Shift Bigger Than Social 04:30 — Spelling, Diction & Depth: How Agents Will Personalize Beyond What Facebook and Google Can See 09:15 — Who Owns Your Preferences? The Battle Between Foundational Models, Retailers, and Consumers 14:00 — Agentic Shopping and the Limits of Automation: Why People Still Want to Buy for Themselves 19:30 — Braze's AI Decisioning Studio: Reinforcement Learning, Guardrails, and Offer Fit 25:00 — Human-in-the-Loop: Why AI Is Also Really Good at Checking on Humans 30:00 — Enterprise AI Adoption: AI Academies, On-Ramps, and Getting Comfortable with Experimentation 36:00 — LLMs Are Not Deterministic: What Happens When Your Model Picks Its Own Magic Numbers 41:00 — The Future of Brand-to-Consumer Relationships: More Personal, Not More Distant Ship integrations 7x fater with Paragon.useparagon.com/?utm_source=spotify&utm_medium=organic_social

Databricks’ Approach to MCP, Identity, and Control | Future Proof Ep. 12

In the latest episode of Future Proof, we sat down with Elise Gonzalez, Staff PM at Databricks, to unpack how Agent Bricks and the MCP marketplace are shaping what enterprise-ready agents actually look like. We cover: * Why governance, audit-ability, and user-level identity are the real blockers to enterprise agent adoption * How MCP unlocked momentum — and why marketplaces, validation, and control are required to make it safe * When multi-agent architectures make sense (and when they just add complexity) * Why most MCP servers today won’t survive long-term — and what agent-native tools need to look like * How AI is changing who uses data platforms, not just what they build Highlights (00:00) Multi-Agent Architecture From Microservices to Agent Supervisors (02:12) Agent Bricks Overview Core Use Cases Extraction, Knowledge Assistants, Composition (04:01) MCP Marketplace Discovery, Governance and Enterprise Control (07:39) Validating MCP Servers Auth, Permissions and Auditability (11:37) Preventing Data Leakage Tool-Level Controls and Safety Patterns (14:58) MCP vs REST Rebuilding APIs for Agent-Native Workflows (18:48) Multi-Agent Risks Privilege Escalation, Context Sharing and Debugging (22:16) When to Use Multi-Agent vs Single-Agent Architectures (30:13) Why Databricks Invested in AI Agents and Data Governance (33:22) The Shift in Users Engineers, Business Users and Databricks One Ship integrations 10x faster with Paragon

How Intercom built Fin:The Flywheel Behind a Production-Ready AI Agent | Future Proof Ep. 11

In the latest episode of Future Proof, we sat down with Christine Zdelar, Director of Product at Intercom, to unpack how Fin has evolved from one of the first AI CX agents in production into a best-in-class support agent. We cover: - The Fin Flywheel and why training, testing, and analysis are core to agent success - Why Intercom went all-in on outcome-based pricing- How MCP, permissions, and oversight show up in real-world production systems - What it takes to push AI resolution rates from ~20% to 60% - And why Intercom is investing deeply down the stack, including building its own models Timestamps: (0:00) Clips (0:55) Introduction (1:26) Getting Fin production ready (4:46) Adopting MCP, is it ready? (7:26) Integrating with helpdesks (11:26) Connecting with knowledge bases (13:26) Resolution-based pricing strategy (19:26) Is the market ready for end-to-end AI resolutions? (23:26) Intercom adopting AI processes (25:26) What needs to happen for customers to adopt Fin (28:26) Intercom builds its own models? (34:26) Closing thoughts Ship integrations 7x faster useparagon.com Host: Ethan Lee Guest: Christine Zdelar Produced by: Forrest Herlick

Inside Gong’s AI Strategy: Context, Memory and Human Trust with Gong's VP of Product, Noaa Ilani

In Episode 10 of Future Proof, we sit down with Noaa Ilani, VP of Product at Gong, to unpack how AI is transforming sales, Gong’s vision for agentic workflows, and what the future of human–AI collaboration really looks like. (0:00) Clips + Introduction (1:50) Fully automated sales rep? (5:00) Sales is built on human trust (5:30) Solving the memory problem (10:00) Understanding conversations (13:00) Accounting for tone (16:20) Embedding agents into other systems? Gong’s vision (22:00) Integrating with other AI tools (23:00) Mitigating missing data (28:35) Human collaboration with AI (34:00) Closing thoughts