Episode 111: How AI Agents Powering the Next Era of Heavy Industry with James Zhan | EP111

Being an engineer myself and talking with the engineers, first of all, there is a job shortage when it comes to engineering.

If you think about this, since when do you hear people still now when I grow up, I want to be an engineer? Even doctor and lawyer, not as much.

They all want to be like streamers or TikTok stars or whatever that entertainment side of things.

If you think about it, industrial engineer is what runs our world, whether it's from a waste, water processing plant to making beer, vinegar, soy sauce to energy extraction.

Hi, my name is Demetri Panici and I'm a content creator, agency owner, and AI enthusiast. You're listening to the AI Agents podcast brought to you by Jotform and featuring our very own CEO and founder, Aytekin Tank.

This is the show where artificial intelligence meets innovation, productivity, and the tools shaping the future of work. Enjoy the show.

Hello and welcome back to another episode of the AI Agents podcast. In this episode, we have James Jen, the founder and CEO of Ranger AI.

How you doing today, James?

I'm doing great. Thank you for having me, Demetri.

Thanks for coming on the show. Just to kind of kick things off, tell us a little bit about your personal background. How did you find yourself operating kind of at this intersection of AI and heavy industry and obviously a little bit before that? How did you even get into AI?

Yeah. I'm a repeat founder. This is actually not my first venture startup but before I went down the tech journey, my background is actually industrial engineering, specifically in chemical engineering.

My very first job was doing industrial RFPs or tendering for a large-scale food and beverage company and had an idea what the job is but the reality was extremely daunting.

Spending all night sifting through thousands of technical drawings, engineering requirements, and these types of documents.

Funny story, at one point in my career during that job I was so distraught by the entire process. I was 24, 25 at the time, didn't have time to go on dates because every night I was just reading, highlighting, selecting all the specifications.

I called my dad and told him I think I'm going to go back to school and get a degree in computer engineering. He's like, why? You're in a great field.

I told him if this is going to be my life, you're not seeing grandkids. I can't even go on this.

All things aside, that was actually one of the reasons why I went down the computer engineering path and hence I'm solving that problem I encountered here at Ranger.

Interesting. Okay. Well, very cool. I'm glad that we found you going from one to the other and this intersection makes much more sense for me now.

Take us to kind of the early days of your first ventures that you had prior to what you're doing here. If you can keep it brief, but just like one of what did you do from a startup standpoint prior to this? And then tell us a little bit about how Ranger kind of came to be.

Yeah, the two previous ventures I've gotten, my very first one was actually very similar to Ranger thesis. It was using recommendation engines as a way to build out proposals and ERP system for industrial companies.

We were very fortunate to get a set of angels to invest in us, work through the problem and was eventually acquired by a large publicly traded company.

I was pretty young at the time and wasn't able to really go through inventory scale but I learned a ton in the process of how to build a company, sourcing the right type of founders, co-founders, and advisers.

That helped me to go through the next couple ventures focusing on Agentic AI for SDRs and prospecting.

Ultimately last year around September, I had the opportunity to partner up with 25 Madison out of New York to revisit my very first industrial thesis and build out Ranger.

Very cool. Okay. So, Ranger in a sentence, what does it do?

We help industrial companies with their tendering management process. Help them win more business, win more bids, and win more RFPs.

Tell me a little bit more about what that looks like without the augmentation of AI.

I'll just explain my first job. My very first project I worked on, I was in charge of working with my engineering team, procurement team, finance team, and legal team to bid for a massive project for the company called AB InBev.

They're the owner of Heineken, Budweiser, all these great beer brands.

In my mind, I thought I was going to be playing beer ponds all day on site with my fellow engineers. But the reality was I was handed a thick, probably about one and a half foot long of technical documentation and told to evaluate basically all the engineering drawings, count all the instrumentations, figure out some of the requirements, and work with different counterpart departments to gather all the requirements.

My goal was to answer a simple question: can we do this? Which means have we done a project, do we have the engineering specifications or specialties to solve the requirement asked by the vendor?

That process took about two months to get done. Thankfully, we won the bid and went on site to see the construction but that initial tendering process was excruciating.

Not only did I have to review and work with the legal team on the financial terms, which I had no idea what I was doing, but I had to go through tons of past projects to figure out if we procured this type of instrumentation before, if we did this process before, how it did before, and how to make sure the margin, the quote, the subcontractor, everything maps out.

Next morning, your professional engineer would ask you, 'Hey James, what's the chassis thickness that we need for this part?' and I had to sift through these thousand-page documents to try to figure out where that requirement is.

It's not as simple as referencing one document to another, drawing to a text spec sheet or somewhere along that entire tender profile. The answer lies.

That's a pretty clear and obvious timesaver. I know that this is kind of where AI is managing to do a lot of work. It's the nonsensical but you do have to think through it type of work or stuff you don't have to think through as well.

Tell us a little bit more about how what you guys are doing at Ranger would be described as agentic.

As I mentioned earlier, this process is a multi-departmental collaboration. It's not just one person working on their own trying to come up with a proposal or a bid or a tender.

You have multi-layer going from the issuer of the proposal to the EPC company, which is essentially the project owner that bids and fleshes out the engineering details.

Then you have the equipment provider or the OEM in that process. The OEM is responding to the proposal by the EPCs and the EPC is responding to it by the issuer.

These issuers are issuing projects in the hundreds of millions to a billion dollars in value.

When you go through these types of cross-departmental and cross-company collaboration, the agentic side of things really helps automate and reduce not just the timesaving part but improve your accuracy.

Getting that communication layer done and having a multitude of individual digital twins that can help.

For example, one of my jobs before was to go to every single set of engineering drawings and manually count on the design sheet the valves that are on the drawing, then compare that specification to the bill of materials that's provided and make sure there's no mismatch or issues that arise from manual mistakes.

Now you have an AI agent that goes using computer vision to detect all these types of engineering specifications, create that BOM, cross-reference it, and provide you with that level of accuracy and risk assessment with a click of a button.

Another example is in the tendering process, I have to evaluate the engineering or legal requirements, for example, payment terms, what do I need to do in case I screw up, how much money I have to pay back in that process with the insurance policy approved vendor and tons of requirements.

Now, someone manually reads through the entire sets of requirements highlighting and copying it down. With AI, especially with Ranger's agent, the AI goes and creates that entire set of requirement documentation that's customer-facing and generates it within a few minutes.

When you have these multi-agent collaborations, you can create that proposal in a much shorter time with the help of the agent and the human in the loop just needs to review, adjust, and make any necessary judgment in terms of subject matter expertise or engineering discipline to make sure it's right.

Tell me a little bit more about how much time that's saving or how much time you feel like you save in a process like that over the course of a year for a company if they're doing a decent amount of work.

For one of our customers, we're seeing time saving somewhere between 15 to 30% of turnaround time, which is massive.

A lot of their revenue comes from this sales enablement process. How do they make money through winning these proposals, these tenders?

We're helping them especially doing something like 30 plus additional quotes every single month. They might not win all of them, but the key is they're competing, they're winning, and it really generates that topline revenue for them.

Another way to think about this is the ability to bid through these massive projects. Winning is one thing but the relationship building with these issuers is another thing.

If you do a home renovation and invite three different contractors, the person that never gets back to you with a bid, you're never going to contact them again. The person that responds first, even if it's a little pricier but done well, has a higher chance to win.

If you respond faster with better quality, even if it's pricier, you have a better chance to win. If you don't respond, you lose that future proposal opportunity completely.

Ranger is providing not just an acceleration of time to value or bidding on the contract but an overall increase in their topline revenue and a help reduction in their bottom line as well.

There's a lot of problems with communication that end up solving a lot of these things in many different aspects of work.

I had a conversation with a friend who worked at a very small company that did home work like drywall work for houses and businesses.

She was getting asked by her boss how to automate opening a new client folder automatically in Google Drive with some templated stuff. I told her this has been a thing for like seven years, not revolutionary.

I asked how she bids on stuff. She said it takes about four hours to do one proposal and put it together.

I told her you could train an agent to make that happen in like 20 minutes at most with you tweaking it and sending it. She was surprised.

She does about 300 of these a year, so that's a quarter of a job saved.

Is that kind of what you're noticing? Maybe the industries are a little behind in regards to where technology can help them outside of the physical operation of the thing they're doing?

Yeah. I've seen a lot of these proposals coming to contractors like drywall or HVAC or these types of coding tools. But at industrial scale, due to the number of players involved, it gets really complex.

When I first started a similar thesis, one of the biggest problems was data readiness. Some legacy companies don't have all the data digitized.

I was with a customer who had a room literally filled with file cabinets with old projects done prior to 2008, not digitized.

AI adoption in industrial space is still being adopted by the end user themselves. Many professional engineers are well in their 40s or 50s and have been doing the same way their mentors did.

Things that used to take a week can be done by AI in seven minutes or five minutes. It takes a while to build trust with AI and their own skill set.

Why do you think that is?

If you are used to doing something for so long and it becomes part of your identity and job, it's hard to trust AI doing that job.

You can put that in parallel with engineers. That's why I haven't bought a Roomba yet.

There are many companies in Agent AI for industrials covering a wide variety. We're at the top of the funnel helping companies win more deals and earn more revenue.

There are startups doing process controls monitoring production lines, flow rates, machine speed, quality checkpoints, and using AI to optimize.

Quality control uses high-speed cameras, sensory arrays, and computer vision to detect cracks, dimensional variances, and issues during production.

Supply chain inventory management and demand forecasting companies help contract raw material providers to avoid supply chain issues.

A friend had a company that automatically goes through vendor bills about to expire and starts negotiation cycles using agents to lower prices.

You can claw some money back or do savings. People change mobile or internet providers every few years to save money. Imagine agents doing that with hundreds of vendors every quarter at scale.

AI has the ability to impact you at scale in a way that maybe some don't realize because of the complexity and transaction volume.

Going back to legacy space, data readiness, risk and compliance anxiety, can I trust AI to do the right job? For industrial quality, you can't have 75% accuracy; you need 99.9%.

Change management fatigue is often overlooked. Industrial companies adopted cloud and online everything quite recently compared to software companies.

They went through ERP systems, ESG rule upgrades, tariff shocks, and now AI. The agility needed is not as trained as software companies.

This is a very challenging problem for industrial AI startups. The ability to deploy AI agents and get fast return is super important.

Adoption fatigue is a real thing. Many industries are behind in adopting technology.

Even banking is behind, still using manual server-based ticker systems.

I talk to many people who don't utilize AI. I see amazing opportunity but also realize many don't know what they're missing.

What is the most exciting thing you're bringing to your product recently or in the next few months?

It's the computer vision stuff. It's one thing to work with unstructured text or tables, but another to deal with engineering drawings.

Imagine going back to that file cabinet of thousands of products from the past that are handwritten. If you can take a photo and put it into Ranger, the AI can decipher and index it properly to digitize instantly.

If I sold an engine 10 years ago and haven't modified it, now with a massive batch of these engines and the person who worked on the project has quit, nobody else knows much about it.

They open the file cabinet, look through the product catalog manually to figure out how to quote or bid.

If you can take a photo, put it into Ranger, and instantly create your quote or query if a specification meets certain criteria, that's super exciting.

The ability to detect all instrumentations in engineering drawings and understand the flow and process is very cool.

This is also seen in construction and civil engineering, using computer vision to create bill of materials by tagging steel components and calculating quantities.

Currently, it's all manual and verified by humans. Junior technicians spend time on grunt work that AI can do, allowing them to move up the chain and get involved with engineering work and processes.

The need to become a certified professional engineer will be even more important in the near future.

I hadn't considered that. It's interesting you brought up different aspects of AI related to industries that do things people wouldn't expect.

For me, images and video and content have done wonders recently, like a leap in screen UI/UX comprehension from Gemini 2.5 to Gemini 3.

What recent AI improvements have opened up opportunities for you to help more clients in your industry?

I wouldn't say technological advancement itself is the catalyst but the wider adoption and improvement in AI readiness makes a huge difference.

More users have encountered ChatGPT or Gemini and dealing with AI in personal use cases, making adoption into work much easier.

A couple years ago, prompting was unknown in industrial. Now many engineers and technicians know how to do basics, building trust in AI.

This helps deploy Ranger into legacy customers. If they've never done something before, it's challenging, but with exposure, ramp is lower and willingness to adapt is higher.

Would you say with AI it's about knowing how to get something done and articulating that so the agent can replicate the human process more than AI completing processes by helping sort out the process?

For us and many industrial startups, we limit openness for customers. If you can keep agentic interactions to simple commands and outputs, that's ideal.

Everything else is a black box. For example, in Ranger, you upload 10,000-page RFP documents, AI extracts cross-functional requirements, embeds data, and runs multi-channel agents in the background.

The user gets outputs relevant to their role, like engineering or finance, rather than deciding what AI workflow to use.

It doesn't matter to typical users how AI delivers results, what matters is delivering value fast and making jobs easier to improve margins and ROI.

For example, put an industrial contract into Ranger's legal agent, click one button, and get red line clause comparisons, risk analysis, and action items automatically.

I have a follow-up question. Do you think sometimes work is just a set of if-then propositions and 90% of what we do would be acceptable if done faster? Has AI helped make that more common in business?

For sure. The 80/20 rule applies. AI can get you 80% there, the rest is up to humans.

Earlier in my career, many stayed busy with busy work, which is not a good use of time.

With AI, busy work shouldn't be needed by humans. Critical thinking and decision-making are more important.

School doesn't prepare people for that as much as needed.

I saw research that 23% of Harvard MBAs are jobless within 90 days of graduation, up from 10% in 2022.

AI is automating junior jobs in hedge funds, compliance reviews, financial reviews, and analyst research.

Entry-level jobs focusing on grunt busy work are slowly being replaced.

I realized many associate-level knowledge work jobs are basic if-then logic, which AI can automate.

There's a job shortage in engineering. Since when do you hear people say they want to be engineers? Many want to be streamers or TikTok stars.

Industrial engineering runs our world, from water processing to beer, vinegar, soy sauce, and energy extraction.

Entry-level engineers want to do more engineering work and less paperwork, which kills them.

Junior technicians and engineers have more opportunities to level up and be out of busy work with AI.

Gen Z and Gen Alpha graduates want to coexist and learn to use AI, but there's almost no AI adoption in industrial space.

Cloud and new-age tech tools have drawn smart talent away from the industry.

Top students from Harvard are not going into oil and gas engineering.

Trades like electricians and contractors are making record income, and parents' attitudes have changed.

Engineering with AI will make things better, faster, and more efficient, boosting the economy and reshoring opportunities.

Talent can now use technology in legacy industries to innovate, creating a huge canvas for creativity.

Coming from an engineering background to tech, did you notice these trends early and decide to get into this?

I think we're the first generation that would love to get back into brick-and-mortar physical stuff because with admin work down, it would be chill.

My wife owns an Olympic-level hip-hop dance studio for kids, a brick-and-mortar business with hundreds of families.

She uses tons of AI to automate billing, customer success, event management, and content creation.

The community and face-to-face interaction in brick-and-mortar is hard to replace and creates a tribal feeling.

People feel more detached working from home versus in the office.

Brick-and-mortar businesses are making a comeback with AI adoption improving margins and viability.

Buying out boomer businesses and applying automation instantly improves returns, a strategy private equity firms use.

Successful 30-somethings buy businesses from retiring boomers, improve operations, and sell to private equity.

A friend bought a fireplace manufacturing company with 10 products, $1.2 million revenue, and 15% margin.

They dug into numbers and found inefficiencies in paperwork, misbilling, follow-ups, and material costs due to poor forecasting.

They used Gemini and ChatGPT to build a website with auto-configure tools like Stripe and Shopify.

They boosted margin from 15% to 32% in six months and increased revenue by 50%.

Where can everyone find you and check out what you're doing at Ranger?

You can go to ranger rfx.com or search Ranger AI or Ranger RFX on Google or LinkedIn. Connect with me on LinkedIn.

Thank you for having me. This has been a lot of fun.

Thanks for coming on. If you enjoyed this, go to Ranger RFX.com, hit the like button on YouTube, and leave a review on Apple Podcast and Spotify to get this out to as many people as possible. Thank you for listening or watching and we'll see you in the next one. Peace.