The Tech Trek

Elevano

The Tech Trek is a podcast about building and leading technology companies. Each episode features founders, CTOs, engineering leaders, and operators sharing how they make decisions around product, engineering, AI, data, teams, hiring, and growth.

  1. 1d ago

    How AI Is Reshaping Healthcare Revenue and Access

    Healthcare providers can wait 60 to 75 days to get paid, while many hospitals spend 5% to 7% of revenue on the collection process. That makes revenue cycle management more than a back office issue. It affects margins, staffing, patient experience, and access to care. Akash Magoon, cofounder and CEO of Adonis, joins The Tech Trek to explain how agentic AI can help medical groups and hospitals automate denials, accounts receivable work, and other manual billing processes. He also shares how Adonis applies AI internally across engineering, sales, and customer success. The conversation goes beyond automation. Akash explains why healthcare companies often win through distribution, not product quality alone, why focused solutions can create more progress than broad attempts to fix healthcare at once, and why leaders need to frame AI as a tool that helps people work at the top of their license. Practical Takeaways • Start with a narrow, material problem rather than trying to rebuild healthcare all at once. • Measure AI through business outcomes, including net collection rate and cost to collect. • Invest in marketing and distribution early, even when the product is strong. • Build employee trust by showing how AI improves effectiveness, not only efficiency. Approximate Highlights 00:45 How Adonis applies agentic AI to revenue cycle management 02:05 Lessons from building a second healthcare technology company 04:45 Using AI for customers and inside the company 08:55 Why healthcare progress often starts with focused swim lanes 14:25 The distribution lesson Akash carried into Adonis 20:40 How operational efficiency may improve patient access and rural healthcare One Line That Stuck “Healthcare ends up becoming a very humbling place to build.” Follow The Tech Trek for more conversations on AI, data, product, engineering, and technical leadership.

    How AI Is Reshaping Healthcare Revenue and Access

  2. 3d ago

    How AI Agents Are Rewriting Enterprise Analytics

    Most companies are not short on data. They are short on the time, cost, and coordination required to turn it into action. Ethan Ding, co founder and CEO of TextQL, joins The Tech Trek to explain how AI agents are changing enterprise analytics. The conversation moves beyond faster dashboards into a larger shift, analysts managing fleets of agents, business teams asking far more questions, and companies finding revenue and cost opportunities that were previously too expensive to pursue. What Technical Teams Can Take From This • Making answers cheaper does not reduce analytics work. It increases the number of questions people ask. • Analysts may spend less time assembling dashboards and more time managing agents, data sources, permissions, quality, and costs. • The clearest ROI comes from decisions with direct financial outcomes, including fraud prevention, upsell opportunities, churn risk, and unused vendor spend. • Faster analysis matters most when teams can act on valuable opportunities they previously could not afford to investigate. • Token costs will force AI companies and buyers to reconsider where software budgets go, especially across BI tools and data platforms. Moments Worth Hearing 00:00 Ethan explains how TextQL agents work across messy enterprise systems including Cognos, Teradata, Snowflake, Databricks, Tableau, and Power BI. 04:52 Why giving people faster answers does not create free time. It creates even more demand for analytics 07:10 How self service analytics quickly moves from asking what a number is to asking whether it matters and what to do next. 10:08 The analyst role shifts toward managing fleets of agents and tuning an insight factory for the business. 14:38 Why faster access to data can reveal valuable opportunities that were previously too expensive to investigate. 19:55 A practical way to measure analytics ROI through fraud prevention, upsell opportunities, and other direct financial outcomes. 24:18 How token costs, AI margins, and easier migrations could reshape spending on traditional BI tools. One Line That Stuck “It becomes much more of an operations manager job. It is a factory. It takes in tokens and churns out dashboards, reports, and recommendations.” Follow The Tech Trek on your podcast platform, subscribe for future episodes, and share this conversation with someone rethinking how their team works with data.

    How AI Agents Are Rewriting Enterprise Analytics

  3. Jul 16

    Scaling Enterprise AI Beyond the POC

    Enterprise AI is easy to demonstrate. The real test begins when a promising POC meets production costs, security requirements, data movement, latency, and internal adoption. Shimon Ben-David, CTO at WEKA, joins Amir to discuss the gap between experimenting with generative AI and operating it at scale. They explore how classical AI differs from generative AI, why production exposes problems that demos hide, and how companies with limited AI maturity can start building useful internal capability. Practical Takeaways • A successful POC proves that an outcome is possible. It does not prove that the system will be affordable, secure, reliable, or fast at scale. • Enterprise AI adoption reaches across infrastructure, engineering, data, security, and business teams. It cannot be owned by one group in isolation. • Adding more GPUs will not fix slow data access, poor utilization, weak pipelines, or an experience users do not want to use. • External support can help, but the person or firm involved needs to stay through implementation and production, not stop at recommendations. • Companies that are behind should begin with proven use cases, build internal experience, and quickly stop experiments that fail to show value. Key Moments 00:00 Why moving enterprise AI into production remains difficult 01:55 The difference between classical AI and generative AI adoption 07:05 How companies can use AI without having a formal AI strategy 11:35 Why successful POCs often struggle when they reach production 17:35 Competitive pressure, AI FOMO, and the need to calculate real ROI 22:00 Why AI adoption requires cross organizational change 33:10 Where a company with limited AI maturity should begin One Line That Stuck “The promise is there. It is possible. You just need to do it properly.” Subscribe to The Tech Trek for more conversations about how technical teams are building, operating, and adapting around AI, data, product, platform, and engineering execution.

    Scaling Enterprise AI Beyond the POC

  4. Jul 14

    How Todoist Is Rethinking Engineering With AI

    AI can generate code faster, but that does not make software delivery simple. It shifts the pressure to requirements, architecture, review, and technical judgment. Goncalo Silva, CTO at Doist, explains how AI is changing the way teams behind Todoist and Twist build software. He shares why greater individual autonomy has led to more collaboration, why deep expertise still matters, and how faster execution is reshaping product delivery, project planning, and engineering hiring. What Leaders Can Take From This • Faster code generation makes strong planning and clear requirements more important, not less important. • Designers, product leaders, and engineers can work from richer prototypes, but production systems still need experienced technical judgment. • Engineering capacity does not have to move into other functions. Teams can use it to improve reliability, performance, quality, and the amount of valuable work they ship. • Token counts are a weak measure of progress. Doist looks at team feedback and whether projects are staying on track. • Engineering interviews need to test architecture, decision making, curiosity, and depth, not simply whether a candidate can produce working code. Approximate Highlights 00:00 Meet GonCalo Silva and the products behind Doist 02:00 How broadly AI is being used across Doist 04:15 Why greater autonomy has brought teams closer together 09:45 Where nontechnical coding works, and where it creates risk 17:50 How AI compressed a major refactoring effort by 20 to 30 times 25:05 Measuring AI value without counting tokens 30:20 Why faster execution requires more up front planning 34:50 How Doist changed its engineering interview process One Line That Stuck “We are the bottleneck. Our attention span, our ability to memorize, our ability to understand, and deep expertise.” Follow The Tech Trek for more conversations on how technical teams are changing the way they build, hire, and operate.

    How Todoist Is Rethinking Engineering With AI

  5. Jul 9

    The Future of Engineering May Have Fewer Handoffs

    AI is not just changing how engineers write code. It is changing who gets close enough to shape the work. In this episode of The Tech Trek, Robert Stewart, CTO at Arbital Health, joins Amir to talk about how AI is bringing actuarial subject matter experts closer to product and engineering teams, especially in healthcare and risk based contracts. Robert shares how his team is pairing technically minded SMEs with software engineers, using AI tools in development, and rethinking technical hiring now that AI assisted coding is part of the job. Practical Takeaways • AI can reduce the distance between domain experts and engineering when the SMEs can clearly describe requirements, acceptance criteria, and edge cases. • Pairing a subject matter expert with an experienced engineer can be more powerful than traditional pair programming because each person brings a different kind of judgment. • Better written requirements matter more in an AI assisted workflow because tools can work directly from detailed tickets and context. • Technical interviews may need to test how candidates use AI, not whether they can avoid it. • Hiring teams need stronger signals around identity, environment fit, prompting skill, and how candidates respond to AI output. Timestamped Highlights 00:00 Robert Stewart on Arbital Health, value based care, and the role of actuarial expertise in healthcare infrastructure. 03:06 Why actuarial knowledge is hard to transfer into engineering teams through normal handoffs. 04:40 How AI helps subject matter experts move closer to product and engineering work. 06:08 Why engineering fundamentals still matter, even when AI makes code easier to create. 09:55 How Arbital Health is using Cursor, Claude Code, and human review in a regulated environment. 14:52 Why more detailed Jira tickets are becoming more valuable in AI assisted development. 17:10 How AI is changing technical interviews from “you may use AI” to “you must use AI.” 22:16 What suspicious candidates, remote interviews, and fake profiles are forcing hiring teams to rethink. One Line That Stuck “You can judge an expert by the type of questions they ask.” Pro Tips • Ask candidates to share their screen during AI assisted technical interviews. • Watch how they prompt, not just what they produce. • Look for whether they catch strange or weak AI output. • Use a rubric, but also evaluate whether the candidate fits the way your team actually works. • For AI generated code, add stronger human review, especially in regulated environments. Subscribe to The Tech Trek for more conversations on how technical teams are adapting around AI, data, product, platform, hiring, and engineering execution.

    The Future of Engineering May Have Fewer Handoffs

  6. Jul 7

    The Hard Part of Voice AI Is Not the Voice

    Voice AI is moving from simple call routing into work that used to require trained human agents. The harder question is what happens when those conversations involve lending, collections, servicing, compliance, and real customer risk. In this episode of The Tech Trek, Amir Bormand speaks with Joshua March, founder and CEO of Veritus, about building AI voice agents for regulated financial services. Joshua shares why consumer lending is a demanding test case for voice AI, what makes regulated conversations different, and why the next version of the contact center may be built with much smaller teams overseeing AI systems. Practical takeaways AI voice agents only matter if they can actually resolve the issue. Joshua argues that users have been trained to distrust automated phone systems because most IVRs block progress instead of helping. Regulated communication is not just about what the agent says. It also includes who can be contacted, when they can be contacted, call frequency, TCPA rules, QA, and post call compliance. Complex voice agents require more than a prompt. Joshua talks about context engineering, state management, specialized background agents, compliance monitoring, KYC workflows, latency, and turn detection. AI changes startup execution. Small teams with experienced people can build and ship much more than before, but that also raises the pressure to move faster. Venture backed AI companies face a bigger bar. Joshua makes the case that higher seed valuations and larger funds increase the need for very large outcomes. Timestamped highlights 00:00, Why Veritus is focused on AI communications for financial services and voice agents in consumer lending 02:10, Joshua’s path from Facebook apps to social customer service, messaging, bots, and now voice AI 05:05, Why AI voice agents may replace a large share of traditional call center work 07:00, Why customers have learned to fight IVRs and what changes when AI can actually solve the problem 10:00, The compliance layers around regulated voice conversations in lending, servicing, origination, and collections 14:00, Why production voice agents need context engineering, state machines, background agents, observability, and monitoring 20:30, How AI has changed startup hiring, management, productivity, and the role of experienced individual contributors One Line That Stuck “This isn’t a crappy IVR that’s just trying to get in my way, this is an intelligent system that can actually take actions and actually resolve my issue.” Practical lens for technical teams If you are building AI into customer operations, the hard part is not only getting the model to speak well. The harder work is making sure it knows what it can do, when it can act, what rules apply, how it is monitored, and when humans need to step in. That matters even more in regulated industries, where the conversation itself is only one part of the system. Follow The Tech Trek for more conversations on how technical teams are building, operating, and adapting around AI, data, product, platform, and engineering execution.

    The Hard Part of Voice AI Is Not the Voice

  7. Jul 2

    Why Healthcare AI Is So Hard to Ship

    Joanne Chen, VP of Data and AI at SimplePractice, joins The Tech Trek to talk about what it takes to build AI data products in a regulated, sensitive domain where privacy, consistency, monitoring, and customer trust have to be designed from the start. This conversation gets into why AI product development feels different from traditional software, how teams should think about quality control, and why not every valuable AI solution needs to be GenAI. Practical Takeaways • AI products need defense in depth, especially in healthcare, where privacy, confidentiality, and security cannot depend on one layer of protection. • The core product questions still matter. What customer pain does this solve, who benefits, and what does it take to ship responsibly? • AI changes the development life cycle because outputs are not always deterministic and quality can degrade after launch. • Teams need monitoring, validation, and a plan for edge cases before putting AI features in front of customers. • AI literacy is becoming part of every role involved in building, marketing, supporting, and operating software products. Timestamped Highlights 00:00 Joanne Chen on AI data products, deterministic outputs, and safely shipping AI features 01:20 What SimplePractice does for mental health practitioners and group practices 02:30 Why healthcare AI needs multiple layers of risk protection 05:00 What makes an AI data product different from a traditional data product 08:15 Why stakeholder expectations around AI have widened so much 10:40 How AI changes the work across engineering, CS, marketing, and support 13:10 Where AI can help reduce tedious administrative work in healthcare 16:45 Why leaders need to keep their hands dirty with new AI tools One Line That Stuck “Keeping hands dirty is important.” Subscribe to The Tech Trek for more conversations on how technical teams are building, operating, and adapting around AI, data, product, and engineering execution.

    Why Healthcare AI Is So Hard to Ship

  8. Jun 30

    Engineers Are Becoming AI Orchestrators

    Kevin Haggard, Vice President of Engineering at Barracuda, joins The Tech Trek to talk about how AI is showing up across cybersecurity products, engineering workflows, team adoption, and software delivery culture. He shares how Barracuda is approaching AI with guardrails, why adoption varies across teams, and what happened when the company ran protected AI dev days for the engineering organization. What to take from this episode * AI adoption inside engineering teams will not be even. Some teams are already orchestrating agents from requirements to deployment, while others are still figuring out where AI fits into their day to day work. * Guardrails matter more in security sensitive environments. Barracuda uses an AI gateway and control plane so teams can experiment without leaking data or letting agents take uncontrolled actions. * Protected time changes behavior. Barracuda’s AI dev days gave teams three days with no meetings so they could work with the tools inside real projects instead of treating AI as a side experiment. * The coding bottleneck may move. AI can create more code faster, but QA, testing, release safety, problem definition, and rollback mechanisms become even more visible. * The engineer’s role is shifting from operator to orchestrator. Kevin argues that system design, review, context, and crisp instruction will become more valuable as agents take on more execution. Key moments 00:28, What Barracuda does and how AI pairs with people in cybersecurity 03:35, Why AI adoption varies across engineering teams inside a larger organization 05:06, The need for AI guardrails, gateways, and control planes 06:20, How Barracuda ran AI dev days across the organization 08:42, The light bulb moments from product, design, and engineering teams 13:07, Why AI velocity makes existing delivery bottlenecks harder to ignore 17:00, How engineers may move from writing code to orchestrating agents and reviewing systems One line that stuck: “Their role is going to elevate more.” Practical moves from Kevin’s experience * Bring trusted partners in for training, but follow that with hands on sessions. * Give teams protected time to use AI inside actual work, not just demos. * Share what the advanced teams are learning so adoption does not stay isolated. * Keep people in the loop, especially when agents are generating code, tests, or workflow changes. * Work backward from release bottlenecks, not just coding speed. Subscribe to The Tech Trek for more conversations on how technical teams are adapting around AI, data, platform, product, and engineering execution. #agenticai #ai #techleadership #engineeringleadership #engineering

    Engineers Are Becoming AI Orchestrators
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About

The Tech Trek is a podcast about building and leading technology companies. Each episode features founders, CTOs, engineering leaders, and operators sharing how they make decisions around product, engineering, AI, data, teams, hiring, and growth.

Information

  • Creator
    Elevano
  • Years Active
    2020 - 2026
  • Episodes
    687
  • Rating
    Clean
  • Copyright
    © Elevano
  • Show Website
    The Tech Trek

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