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Can vertical SaaS transform niche industries with artificial intelligence

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Some of the most interesting startup pitches I see these days do not promise to change the world. They promise to change a workflow, with artificial intelligence solving a critical problem of a particular industry. A messy, outdated, painful process that is too niche for large software giants to notice. That is the magic of vertical SaaS powered by artificial intelligence. Also, these businesses need very little venture capital to reach escape velocity, also known as breakeven at scale. Hence, the guerrilla way of venture building. As a seed investor, I believe this category is quietly reshaping how industries function, one focused solution at a time.For those unfamiliar, vertical SaaS refers to software products or platforms built specifically for a single industry or use case. Examples include a debt collection solution used by banks and NBFCs to improve recovery and compliance, a sustainability reporting tool built for textile manufacturers to track emissions and meet ESG norms, or a customer conversation platform that uses artificial intelligence to assist call centre agents in real time.
Their value lies in deep domain knowledge, built to navigate the unique rules, workflows, and challenges of each industry.
As venture capitalists, we are not just chasing trends, we are also looking for signals. And in the case of vertical SaaS, the signals are strong. These startups tend to have longer-lasting customer relationships, stronger margins, and much clearer paths to profitability. Their clients rarely churn because once an industry-specific tool is integrated into daily operations, replacing it is painful. That creates stickiness, and stickiness builds enduring companies.

But what is really interesting is the timing. Several things are coming together to make this moment ripe for vertical SaaS. Cloud infrastructure is now robust and affordable, allowing startups to build faster and scale without massive upfront investment. Even traditional businesses, from clinics to small factories, are now open to digitisation, especially when the solutions speak directly to their problems.


At the same time, artificial intelligence, often viewed as a general-purpose technology, is supercharging these vertical solutions. A chatbot trained on dermatology data with over one thousand patient conversations can outperform any generic artificial intelligence model in a clinical setting. That is the power of specificity, and it is what makes artificial intelligence in vertical SaaS truly transformative.I often get asked why not just invest in broader platforms that can scale across industries. The answer lies in focus. Startups that go deep into one sector understand their customers better. Their messaging is clearer. Their sales cycles are shorter. They do not have to educate the market on the value of software. They show how their product solves a known, painful problem. That is a huge advantage, especially in emerging markets where trust and usability are everything.We are seeing this play out across India and other developing economies. From supply chain platforms for agri-exporters to legal tech tools for small law firms, entrepreneurs are spotting inefficiencies that have long been ignored. These are not glamorous problems, but they are real, and solving them creates real value. In many cases, these startups are not even competing with other software. They are replacing pen and paper, Excel sheets, and WhatsApp groups. That is an enormous opportunity.Of course, capital plays a crucial role, but it is not just about writing cheques. At the early stage, vertical SaaS startups often need guidance on how to sell, how to price, and how to build for scale without losing their niche. As venture capitalists, we bring not only capital but also access. Access to pilot customers, to advisors from within the industry, and to talent that understands both technology and the specific sector. I have seen companies unlock growth just by getting introduced to the right distribution channel or hiring someone who has worked on the client side of the industry.

What is also heartening is how founders in this space tend to operate. Many come from the industries they are trying to fix. They have seen the gaps firsthand, often as bankers, engineers, teachers, or logistics operators. They decided to build solutions because nobody else was solving the problems they encountered daily. That lived experience brings empathy, and it reflects in the product. The interface is more intuitive. The features are more relevant. The onboarding feels less like software and more like a helping hand.

There is also something to be said about the resilience of vertical SaaS models. Most operate on a subscription basis, creating predictable revenue. Contracts are often annual, if not multi-year. This gives startups the stability to reinvest in product development and customer success, both of which matter a lot more when you are solving for depth rather than breadth. It also gives investors like us the confidence that the business can withstand market swings better than many flashier counterparts.

After investing in several such companies at the pre-seed stage and witnessing their journeys first-hand, we have seen a clear pattern emerge. In categories like debt recovery, construction management and artificial intelligence-enabled sales platforms, it is possible to reach ₹300 crore in topline within the first five years when founders are solving a deep, industry-specific pain point and distribution is cracked early. With strong product-market fit, the path to ₹500–1000 crore revenue becomes a question of execution, not potential.

None of this is to say that vertical SaaS is easy. Going deep into one sector means founders must be patient. The total addressable market might look small at first glance. But once the product proves itself, expansion into adjacent segments or into new geographies with similar market structures becomes a natural next step. And that is when growth really takes off.

Looking ahead, I believe the next decade will see vertical SaaS companies emerge not just as profitable businesses but as quiet disruptors within industries that desperately need modernisation. Whether it is farming, education, construction, or logistics, sectors that are often overlooked in technology conversations will be transformed by software built not for the general market but for the people who wake up every day to real, operational problems.

As investors, we are excited to partner with these founders. The ones who know that transformation does not always begin with scale. Sometimes, it starts with a small fix to a big problem. And from there, everything changes.

Author is Founder and Managing Partner of Zeropearl VC.



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University Spinout TransHumanity secures £400k | News and events

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TransHumanity Ltd., a spinout from Loughborough University, has secured approximately £400,000 in pre-seed investment. The round was led by SFC Capital, the UK’s most active seed-stage investor, with additional investment from Silicon Valley-based Plug and Play.

TransHumanity’s vision is to empower faster, smarter human decisions by transforming data into accessible intelligence using large language model based agentic AI. 

Agentic AI refers to artificial intelligence systems that collaborate with people to reach specific goals, understanding and responding in plain English. These systems use AI “agents” — models that can gather information, make suggestions, and carry out tasks in real time — helping people solve problems more quickly and effectively.

TransHumanity’s first product, AptIq, is designed to help transport authorities quickly analyse transport data and models, turning days of analysis into seconds. 

By simply asking questions in plain English, users can gain instant insights to support key initiatives like congestion reduction, road safety, creation of business cases and net-zero targets.

Dr Haitao He, Co-founder and Director of TransHumanity, said: “I am proud to see my rigorous research translated into trusted real-world AI innovation for the transport sector. With this investment, we can now realise my Future Leaders Fellowship vision, scaling a technology that empowers authorities across the UK to deliver integrated, net-zero transport.”

Developed from rigorous research by Dr Haitao He, a UKRI Future Leaders Fellow in Transport AI at Loughborough University, AptIq, previously known as TraffEase, has already garnered significant recognition. 

The technology was named a Top 10 finalist for the 2024 Manchester Prize for AI innovation and was recently highlighted as one of the Top 40 UK tech start-ups at London Tech Week by the UK Department for Business and Trade.

Adam Beveridge, Investment Principal at SFC Capital, said: “We are excited to back TransHumanity. The combination of cutting-edge research, a proven founding team, clear market demand, and positive societal impact makes this exactly the kind of high-growth venture we are committed to supporting.”

AptIq is currently in a test deployment with Nottingham City Council and Transport for Greater Manchester, with plans to expand to other city, regional, and national authorities across the UK within the next 12 months.

With a product roadmap that includes diverse data sources, advanced analytics and giving the user full control over the AI tool when required, interest from the transport sector is already high. Professor Nick Jennings, Vice-Chancellor and President of Loughborough University, noted: “I am delighted to see TransHumanity fast-tracked from lab to investment-ready spinout.

This journey was accelerated by TransHumanity’s selection as a finalist in the prestigious Manchester Prize and shows what’s possible when the University’s ambition aligns with national innovation policy.”



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Legal-Ready AI: 7 Tips for Engineers Who Don’t Want to Be Caught Flat-Footed

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An oversimplified approach I have taken in the past to explain wisdom is to share that “We don’t know what we don’t know until we know it.” This absolutely applies to the fast-moving AI space, where unknowingly introducing legal and compliance risk through an organization’s use of AI is a top concern among IT leaders. 

We’re now building systems that learn and evolve on their own, and that raises new questions along with new kinds of risk affecting contracts, compliance, and brand trust.

At Broadcom, we’ve adopted what I’d call a thoughtful ‘move smart and then fast’’ approach. Every AI use case requires sign-off from both our legal and information security teams. Some folks may complain, saying it slows them down. But if you’re moving fast with AI and putting sensitive data at risk, you’re also inviting trouble if you don’t also move smart.

Here are seven things I’ve learned about collaborating with legal teams on AI projects.

1. Partner with Legal Early On

Don’t wait until the AI service is built to bring legal in. There’s always the risk that choices you make about data, architecture, and system behavior can create regulatory headaches or break contracts later on.

Besides, legal doesn’t need every answer on day one. What they do need is visibility into the gray areas. What data are you using and producing? How does the model make decisions? Could those decisions shift over time? Walk them through what you’re building and flag the parts that still need figuring out.

2. Document Your Decisions as You Go

AI projects move fast with teams needing to make dozens of early decisions on everything from data sources to training logic. So, it’s only natural that a few months later, chances are no one remembers why those choices were made. Then someone from compliance shows up with questions about those choices, and you’ve got nothing to point to.

To avoid that situation, keep a simple log as you work. Then, should a subsequent audit or inquiry occur, you’ll have something solid to help answer any questions.

3. Build Systems You Can Explain

Legal teams need to understand your system so they can explain it to regulators, procurement officers, or internal risk reviewers. If they can’t, there’s the risk that your project could stall or even fail after it ships.

I’ve seen teams consume SaaS-based AI services  without realizing the provider could swap out a backend AI model without their knowledge. If that leads to changes in the system’s behavior behind the scenes, it could redirect your data in ways you didn’t intend. That’s one reason why you’ve got to know your AI supply chain, top to bottom. Ensure that services you build or consume have end-to-end auditability of the AI software supply chain. Legal can’t defend a system if they don’t understand how it works.

4. Watch Out for Shadow AI

Any engineer can subscribe to an AI service and accept the provider’s terms without knowing they don’t have the authority to do that on behalf of the company.

That exposes the organization to major risk. An engineer might accidentally agree to data-sharing terms that violate regulatory restrictions or expose sensitive customer data to a third party.

And it’s not just deliberate use anymore. Run a search in Google and you’re already getting AI output. It’s everywhere. The best way to avoid this is by building a culture where employees are aware of the legal boundaries. You can give teams a safe place to experiment, but at the same time, make sure you know what tools they’re using and what data they’re touching.

5. Help Legal Navigate Contract Language

AI systems get tangled in contract language; there are ownership rights, retraining rules, model drift, and more. Most engineers aren’t trained to spot those issues, but we’re the ones who understand how the systems behave.

That’s another reason why you’ve got to know your AI supply chain, top to bottom. In this case, when legal needs our help in reviewing vendor or customer agreements to put the contractual language into the appropriate technical context. What happens when the model changes? How are sensitive data sets safeguarded from being indexed or accessed via AI agents such as those that use Model Context Protocol (MCP)? We can translate the technical behavior into simple English—and that goes a long way toward helping the lawyers write better contracts.

6. Design with Auditability in Mind

AI is developing rapidly, with legal frameworks, regulatory requirements, and customer expectations evolving to keep pace. You need to be prepared for what might come next. 

Can you explain where your training data came from? Can you show how the model was tested for bias? Can you justify how it works? If someone from a regulatory body walked in tomorrow, would you be ready?

Design with auditability in mind. Especially when AI agents are chained together, you need to be able to prove that identity and access controls are enforced end-to-end. 

7. Handle Customer Data with Care

We don’t get to make decisions on behalf of our customers about how their data gets used. It’s their data. And when it’s private, it shouldn’t be fed to a model. Period. 

You’ve got to be disciplined about what data gets ingested. If your AI tool indexes everything by default, that can get messy fast. Are you touching private logs or passing anything to a hosted model without realizing it? Support teams might need access to diagnostic logs but that doesn’t mean third-party models should touch them. Tools are rapidly evolving that can generate comparable synthetic data devoid of any customer private data that could help with support use cases for example, but these tools and techniques should be fully vetted with your legal and CISO organizations prior to using them. 

The Reality

The engineering ethos is to move fast. But since safety and trust are on the line, you need to move smart, which means it’s okay if things take a little longer. The extra steps are worth it when they help protect your customers and your company.

Nobody has this all figured out. So ask questions by talking to people who’ve handled this kind of work before. The goal isn’t perfection—it’s to make smart, careful progress. For enterprises, the AI race isn’t a question of “Who’s best?” but rather “Who’s leveraging AI safely to drive the best business outcomes.” 



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Progress Unveils Subsidiary for AI-Driven Digital Upgrade

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Progress Software, a company offering artificial intelligence-powered digital experience and infrastructure software, has launched Progress Federal Solutions, a wholly owned subsidiary that aims to deliver AI-powered technologies to the federal, defense and public sectors.

Progress Federal Solutions to Boost Digital Transformation

The company said Monday the new subsidiary, announced during the Progress Data Platform Summit at the International Spy Museum in Washington, D.C., is intended to fast-track federal agencies’ digital modernization efforts, meet compliance requirements, and advance AI and data initiatives. The subsidiary leverages MarkLogic’s data management and integration expertise, a platform that Progress Software acquired in 2023.

Progress Federal Solutions functions independently but will offer the company’s full technology portfolio, including Progress Data Platform, Progress Sitefinity, Progress Chef, Progress LoadMaster and Progress MOVEit. These will be available to the public sector through Carahsoft Technology‘s reseller partners and contract vehicles.

Remarks From Progress Federal Solutions, Carahsoft Executives 

“Federal and defense agencies are embracing data-centric strategies and modernizing legacy systems at a faster pace than ever. That’s why we focus on enabling data-driven decision-making, faster time to value and measurable ROI,” said Cori Moore, president of Progress Federal Solutions.

“Progress is a trusted provider of AI-enabled solutions that address complex data, infrastructure and digital experience needs. Their technologies empower government agencies to build high-impact applications, automate operations and scale securely to meet program goals,” said Michael Shrader, vice president of intelligence and innovative solutions at Carahsoft.





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