The Tech Trek

Most healthcare AI stories start with diagnosis. Edmund Jackson thinks that misses the real bottleneck.

In this episode of The Tech Trek, Edmund Jackson, CEO and founder of Unity AI, joins Amir to talk about AI for healthcare operations. The conversation gets into why scheduling, staffing, follow up, payer coordination, and interoperability are often where healthcare breaks down, and why solving those operational problems may matter more than chasing the flashiest use cases.

Edmund brings a healthcare first view to AI. His argument is simple: healthcare is not slow because people are ignoring technology. It is slow because the real workflows are complex, regulated, high context, and hard to capture cleanly in software.

What You’ll Take Away

• Why healthcare experience matters when choosing which AI problems are actually worth solving
• Why diagnosis is not always the best starting point for healthcare AI
• How scheduling becomes much more complex when patients, payers, clinics, staff, protocols, and follow up all have to line up
• Why AI can help clinics save time while moving human staff toward higher value patient interactions
• Why interoperability is still hard, even with standards like FHIR gaining momentum

Timestamped Highlights

00:29, What Unity AI does and why healthcare operations is the focus
01:11, Why healthcare AI needs people who understand the domain, not just the technology
02:26, The danger of solving the hardest or flashiest problem instead of the most pragmatic one
05:15, Why AI may finally help healthcare handle personalization and operational complexity at scale
07:47, Why scheduling a healthcare visit is nothing like scheduling a delivery or restaurant order
10:42, How operational AI can save time and reduce downstream chaos in clinics
24:33, Why healthcare data is much harder to structure than financial data

One Line That Stuck

“Software is like children. Making it is all fun and games. Maintaining it is a whole other question.”

Practical Takeaways

• Start with the workflow that actually blocks progress, not the one that sounds most impressive
• In healthcare, operational context is often the product
• AI should create more room for humans to handle the interactions that require judgment, care, and clinical responsibility
• More software is not always the answer, especially in regulated environments where maintenance, compliance, and security matter

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Deepak Bapat, CTO and co founder at Tabs, joins The Tech Trek to talk about how his team is using tools like Claude Code and Cursor, where AI is helping, and why systems thinking may matter more than raw coding ability as engineering work shifts.

Practical Takeaways

• AI coding agents are already producing useful production work, but judgment still matters.
• Tool choice may be less important than standardizing the expected output.
• Messy repos can make AI generated work harder to trust, so cleanup and patterns matter.
• The future engineer may look more like a product engineer with strong systems thinking.
• Teams may move from debating features to rapidly building multiple versions and testing what works.

Timestamped Highlights

00:37
What Tabs does and why contracts create hard revenue workflow problems for B2B finance teams.
02:16
Deepak compares pre AI engineering work with the current shift toward AI assisted development.
05:09
How the Tabs engineering team uses Claude Code, Cursor, and other coding tools in real work.
08:13
Why inconsistent codebases create more risk when teams add coding agents
14:00
The idea that teams can build the same feature multiple ways in one afternoon.
20:53
Deepak’s view on whether the future team needs separate PMs and engineers, or more product engineers.
23:38
A future where software can become more bespoke to each customer because AI changes the cost model.

One Line That Stuck

“You can build on three different work trees the same feature in three different ways and see which one you like, and you can do it all in an afternoon.”

Practical Moves From The Conversation

• Keep humans close to the review process, especially when the last five percent still requires taste and judgment.
• Clean up inconsistent code patterns before letting agents operate broadly across the repo.
• Hire for adaptability, systems thinking, and problem solving, not just past tool familiarity.
• Use AI to explore more product options faster, but do not remove the need to ask whether the feature should exist.

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Agentic AI is not just a model problem. It is exposing gaps in how teams store, share, retrieve, and coordinate context across applications, agents, and people.

In this episode, Amir talks with Karthik Ranganathan, cofounder and co CEO at Yugabyte, about why databases are under new pressure as AI moves from model serving into agentic workflows. They discuss Yugabyte’s evolution, the limits of today’s data infrastructure, and why memory, knowledge, and shared context may become central to how agentic systems actually work.

Practical takeaways

• Agentic workloads push databases beyond simple relational access because agents may need relational, vector, graph, NoSQL, scale, and multi tenant support in the same workflow.
• A query can be optimized inside each data store and still be slow, expensive, or wasteful when the work spans multiple systems.
• Context sounds simple to humans, but it becomes messy when it includes private memory, shared project knowledge, conversation history, team collaboration, and agent actions.
• Human handoffs can erase much of the speed promised by agents when teams have to copy outputs, re explain reasoning, and manually reconcile conflicts.
• Yugabyte is working on Meko as a data infrastructure layer for agents, with a focus on memory, knowledge, context quality, and shared learnings.

Timestamped highlights

00:43
What Yugabyte does and why critical data needs to survive infrastructure change
04:08
How databases evolved from mainframes to internet apps, mobile, cloud native systems, and now AI
09:42
Why agentic workloads create new demands across relational, vector, graph, NoSQL, and multi tenant data
12:29
Why the current agentic data stack is still in the messy middle
15:24
Why context becomes hard when agents, people, teams, and permissions collide
21:50
How agent collaboration can fall back to human speed
24:34
How eeko aims to capture memory, knowledge, learnings, and reasoning across agent workflows

One Line That Stuck

“We have killed the velocity of agentic development and brought it back to human speed.”

Practical signals for teams building with agents

• Do not treat context as one generic blob.
• Decide what should stay private, what should be shared, and what should become reusable project knowledge.
• Watch for hidden cost when agents query across separate systems.
• Pay attention to agent collaboration, not just single agent output.
• Build for memory and knowledge flow before team size makes the gaps harder to fix.

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Agentic coding is not just making engineers faster. It is changing how teams triage bugs, prototype features, involve product, and think about hiring.

Scott Weller, CTO and founder at EnFi, joins The Tech Trek to talk about how his team is building around agentic software development while operating in financial services, where trust, accuracy, and human judgment still matter. EnFi uses AI agents to work through complex financial data rooms, extract knowledge, and support faster analysis in commercial lending.

In this episode, Scott breaks down how EnFi moved from simple coding assistance to a broader development harness, why Slack became a central interface for agents, how product and business leaders can now participate earlier in feature creation, and why engineering interviews need to change when AI is part of the actual job.

Practical Takeaways

• Start with specific productivity goals before trying to rebuild the whole development process.
• Agentic tools work better when they connect to the team’s real workflow, shared context, and software lifecycle data.
• Faster code generation changes the cost model, but it also creates new problems around review, testing, prioritization, and decision fatigue.
• Product, sales, and executive teams may be able to prototype ideas faster, but engineering still has to make the work production ready.
• Hiring needs to test how people solve problems with AI, not whether they can perform the old interview format without help.

Timestamped Highlights

00:38, What EnFi is building around financial data, AI agents, and commercial lending
02:13, Why software teams may need to forget part of their old development process
04:45, How EnFi started with productivity gains before building a broader development harness
09:53, Why merge requests went up, and why that alone is not the same as better outcomes
10:30, How Slack became the entry point for an agentic development harness
14:10, What happens to agile ceremonies when teams can create discovery builds much faster
25:08, Scott’s view on whether AI reduces engineering headcount or changes the work engineers do
31:00, How EnFi is changing technical interviews for an AI assisted engineering environment

One Line That Stuck

“We do not care if you use AI to solve the problems, we just want to know you can solve the problem.”

Practical Takeaways For Technical Teams

Put agents close to where work already happens.

Keep humans in the loop for review, testing, and production judgment.

Treat AI generated code as cheaper to create, not free to maintain.

Build stronger test harnesses instead of slowing everything down with excessive process.

Update interviews to reflect how engineering work is actually getting done.

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AI adoption looks very different when mistakes can create legal, financial, and reputational risk.

Vijay Gandra, Global CDO at Acrisure, joins The Tech Trek to talk about AI transformation inside a regulated industry, where explainability, data quality, governance, cost, and team readiness matter just as much as model capability.

The conversation covers the trust gap in AI, how data teams are shifting from dashboard production to conversational data access, when to buy versus build, and why AI proof of concepts need to be judged by business value, operational efficiency, and customer impact.

Practical Takeaways

• Regulated industries cannot treat AI as a black box. Decisions need traceability, consistency, and often a human review layer.
• Data quality has to be addressed from the start. AI can amplify bad data as easily as it can create value.
• Data teams are moving beyond dashboard factories toward conversational data access and generative interfaces.
• Most companies can likely use existing AI tools for many needs, but sensitive IP and core business logic may require internal capabilities.
• AI cost will become a bigger production question as companies move from experimentation to scaled deployment.

Timestamped Highlights

00:47, Acrisure’s shift from insurance brokerage toward fintech and financial tools.
01:44, Why regulated industries face a trust gap with AI and need explainable decisions.
04:41, How data teams are evolving from dashboards to conversational data enablement.
08:28, The build versus buy question and where internal AI tools may still make sense.
10:52, Why AI experimentation can get expensive before companies know what works.
16:15, How to evaluate AI proof of concepts based on customer value, efficiency, and business impact.
18:14, Why data governance and data quality need to be treated as day one requirements.

One Line That Stuck

“In an industry like this, a 5 percent deviation is not just a simple glitch. It is actually a legal liability.”

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