Turning a startup idea into a successful IPO is a journey spanning years – often a decade or more. It’s a path filled with iterative product development, rapid growth spurts, and the demanding transition into a mature, global enterprise. Throughout this journey, custom software development plays a pivotal role at every phase, from crafting the initial Minimum Viable Product (MVP) to scaling up for millions of users and finally to achieving the robustness required of public companies.
This case-study-style exploration breaks the startup journey into three key phases – MVP, Scaling, and enterprise-readiness, and examines how startups in fintech, healthtech, and SaaS leverage custom software to navigate each stage. Along the way, we’ll highlight real-world startup examples and insights, illustrating how the right technology decisions (often made in partnership with experienced developers like Empyreal Infotech) can make the difference in scaling from idea to IPO.
(On average, it takes around 10 years to go from founding to IPO, underscoring that successful “overnight” successes are usually backed by long-term, scalable technology strategies.)
Phase 1 – From Idea to MVP: Laying the Foundation with Custom Software
Every startup begins with an idea – a solution to a problem or a new service to offer – but turning that idea into a tangible product requires focus and speed. This is the MVP stage, where the goal is to develop a minimum viable product: the simplest version of the product that delivers core value to early customers. Custom software development is often the engine that brings an MVP to life, enabling founders to implement unique features or innovative processes that off-the-shelf tools can’t provide.
Why MVPs Matter: Building an MVP allows a startup to test its concept in the real world without pouring resources into a fully mature product. As famed entrepreneur Guy Kawasaki noted, “Ideas are easy.
Implementation is hard – the MVP is the first real implementation of that idea. The emphasis is on rapid development and iteration: release quickly, get user feedback, and refine. This often means making trade-offs in engineering; the product must work well enough to attract early users, even if it’s not yet perfectly architected for scale. In fact, seasoned technologists acknowledge that in early stages, speed trumps perfection. Startups intentionally take on “prudent technical debt”– quick-and-dirty coding or manual processes – to get the product out faster. This is a healthy approach initially: “Startups should trade technical aspects like quality or robustness for delivery speed” to achieve a viable business model and a product that users love. The polish and scaling can come later once the concept is proven.
Custom Software in an MVP: At the MVP phase, custom software gives startups the flexibility to innovate. Founders can build exactly what they envision as the core feature set, rather than being limited by existing platforms. For example, in the fintech arena, two friends in London frustrated by high bank fees decided to solve their own problem. They built a simple peer-to-peer money exchange system – this was the MVP of Wise (formerly TransferWise) in 2011.
The premise was straightforward but powerful: match people who need to swap currencies, bypassing banks’ hidden fees. That early custom-built platform allowed Wise to charge a mere £4.50 on a £1,000 transfer versus £50–100 via traditional methods – a dramatic value proposition that attracted users. By focusing custom development on the core problem (cheap, transparent exchange rates) and nothing extraneous, Wise’s MVP resonated with customers and validated the idea quickly. It wasn’t fancy – the backend matched currency orders and routed payments cleverly- but it worked.
Another classic MVP story comes from the SaaS world. Slack, the workplace messaging app now used by tens of millions, started as an internal tool at a small gaming company. Stewart Butterfield and his team were building a game called Glitch, which ultimately flopped, but they had created a real-time chat system.
for their own communication. Recognizing its potential, they pivoted: Slack’s MVP was essentially this repurposed internal chat software. Early on, Slack’s architecture was monolithic and relatively simple.
PHP (Hack) backend for core logic and a separate Java-based message server for real-time chat. This setup was enough to support small teams and allowed rapid iteration. The Slack founders invited a few companies to beta test the MVP in 2013, gathered feedback, and quickly refined the user experience. Within months of its public launch in 2014, Slack became one of the fastest-growing B2B apps ever, precisely because its custom-built features (like intuitive channels and integrations) addressed a gap that generic messaging tools didn’t. The key lesson is that custom software enabled Slack’s unique approach to team communication an off-the-shelf solution wouldn’t have provided the real-time, persistent messaging with search that Slack offered out of the gate.
Healthtech MVPs also illustrate the importance of starting with a focused, custom solution. Consider Livongo Health, founded in 2014 to help patients manage chronic diseases like diabetes. Livongo’s MVP combined a smart glucose meter, a simple mobile app, and a basic data analytics system to provide personalized coaching for diabetics. This was a novel, integrated approach at the time – most diabetes tools were standalone devices or generic health apps. By custom-building an “applied health signals” platform (device + app + data pipeline) from scratch, Livongo delivered an empathetic user experience tailored for patients.
The early product wasn’t loaded with features; it focused on one condition (diabetes) and one core objective (helping patients reduce their blood glucose levels). That focus paid off: even the MVP showed improved outcomes, like significantly lowering patients’ HbA1c levels of blood sugar control and cutting medical costs by 21.9% in trials. These real-world results validated Livongo’s approach and attracted its first enterprise customers (self-insured employers and health plans) even while the product was still minimalistic.
MVP Best Practices (Lessons Learned):
Solve a Specific Problem Exceptionally Well: The most successful MVPs zero in on a clear pain point. Wise tackled one problem (expensive international transfers) with a clever fix, and Livongo focused on one condition (diabetes) with a tailored solution. A sharply defined scope for your MVP ensures your custom software development effort is concentrated on features that truly matter to users, rather than nice-to-haves.
Leverage Existing Tech Strategically: “Custom” software doesn’t mean reinventing every wheel. Slack’s early backend was built in PHP/Hack – a pragmatic choice, using a familiar language to move fast. The team borrowed concepts from their game architecture (real-time event handling) to jump-start Slack’s development. Similarly, a fintech MVP might use established security libraries but weave them together in a new way. Smart startups mix custom code with open-source tools or cloud services to accelerate development while maintaining uniqueness where it counts.
Iterate with User Feedback: An MVP is not a one-shot product but the beginning of a conversation with users. Early Slack beta users, for instance, requested features like easier file sharing – the Slack team quickly added these in subsequent updates, shaping the roadmap based on real needs. Custom software is particularly amenable to fast iteration; since you built it, you can change it. Adopting agile development practices (short sprints, frequent releases) and deploying updates continuously will refine the MVP into a product-market fit. As one guide notes, agile, iterative development is “crucial in refining the product based on user feedback and market demands.”
Plan for Scalability (but Don’t Over-engineer): There’s a balancing act in MVP development. You shouldn’t invest months in a perfect, scalable architecture before you have users – that risks building something nobody wants (the proverbial “second system syndrome”). However, keeping an eye on future scale is wise. For example, Slack’s founders likely didn’t anticipate how quickly usage would explode, but their separation of the real-time messaging component into a dedicated server was a forward-thinking choice that made it easier to scale later. In short, build your MVP to validate the idea first; if it succeeds, be ready to re-architect or optimize later (a theme we’ll revisit in the scaling phase). Remember the advice from experienced scale-up engineers: the hacks and shortcuts are fine early, but “once the company looks to scale up, we have to address the shortcuts taken” lest they “very 3 quickly affect the business.”
Custom Software Partner Tip: Not every founding team has a CTO or a full development team to code the MVP. In such cases, partnering with an experienced development firm can be invaluable. For instance, Empyreal Infotech – a custom software development company that serves clients “ranging from startups to enterprises”– often helps startups rapidly prototype and build their MVPs. With a skilled partner handling the heavy technical lifting, founders can focus on vision, domain expertise, and user testing.
The right partner will employ frameworks and agile methods to deliver a functional MVP quickly while laying a foundation that won’t crumble when your user base doubles. Many successful founders credit early engineering partnerships for getting them to market faster and avoiding rookie mistakes in architecture. The goal is to combine the startup’s innovative idea with the development partner’s expertise in execution.
Real-World Snapshot – MVP in Action: When Dropbox was just an idea for seamless file syncing, its founder, Drew Houston, famously created a 3-minute demo video as a “Visual MVP” to gauge interest before writing all the code. The overwhelmingly positive response gave him the confidence to invest in fully developing the product. While not software or a prototype per se, this approach highlights the MVP ethos: find the fastest way to validate your idea.
Once validated, Dropbox’s team wrote custom synchronization software that now serves hundreds of millions of users. The initial scrappiness (using a video to simulate the app) saved time and guided development a mentality every startup can embrace.
By the end of the MVP phase, a startup should have a working product that a core group of users can’t live without. Key metrics might include a few hundred or thousand users, positive user feedback, and maybe some revenue or usage data that attract seed investors. The custom software built for the MVP likely has limitations (single-server deployment, rudimentary code structure, minimal features), but it proves the concept. This sets the stage for Phase 2, where the challenge shifts from building the right product to building the product right at scale.
Phase 2 – Scaling Up: From Product-Market Fit to Global Expansion
Once a startup has an MVP with early traction, it enters the scaling phase. Here, the company’s focus pivots to growth acquiring more users or customers, expanding the feature set, entering new markets, and often, dramatically increasing the team and technical capacity. This is the stage of “hypergrowth” when a startup becomes a scaleup. The custom software that was hastily thrown together may now start creaking under the strain of many more users and use cases.
Scaling up is arguably the most perilous phase for a startup: systems must be re-architected on the fly, technical debt from the MVP stage needs paying down, and any inefficiencies can become bottlenecks that slow growth. As one analysis bluntly puts it, “The most common scaling bottleneck we encounter is technical debt startups regularly state that tech debt is their main impediment to growth.” In this phase, the startup’s engineering approach matures significantly often with help from seasoned experts or partners to turn a scrappy product into a robust platform.
Key Scaling Challenges: When a product catches on, usage can grow exponentially. For example, Slack’s user base went from that handful of beta testers to over 1 million daily active users by late 2015 and 12 million by 2019. With such growth, a monolithic MVP codebase can start to buckle. Slack engineers recall that as some customers grew to 10,000+ users sending millions of messages, the original single database design began “knocking over databases” due to load. Performance issues, crashes, and limitations on data storage per workspace became serious concerns. Similarly, Wise (TransferWise), after proving its concept, faced the need to expand internationally and handle massive transaction volumes.
By 2014 (just three years after launch), Wise had raised a $58 million Series C to fuel global expansion and launched in the US and Australia. Its platform crossed £1 billion transferred cumulatively by 2014, and remarkably, by 2017 Wise was moving over £1 billion every month through its system while remaining profitable.
To support that growth, Wise’s tech had to evolve from a simple peer-to-peer matcher to a sophisticated global payments network. They began integrating directly with banking systems; in 2016, Wise became the first tech company with direct access to the UK Faster Payments network, reducing reliance on third-party banks and improving speed. This kind of deep integration is custom development work that requires top-notch engineering and regulatory collaboration, but it paid off in scalability.
By 2025 Wise was handling £145 billion in international transfers annually for 15+ million customers—an unimaginable scale compared to its MVP days. The ability to scale custom software (routing payments, managing liquidity across 170+ countries, and preventing fraud in real-time) was absolutely critical to Wise’s success. They essentially had to build a global bank’s infrastructure through software, step by step.
Another vivid example of scaling challenges comes from Zoom, the video conferencing startup. Zoom had a solid product and fit in the enterprise market (it IPO’d in 2019 as a profitable company), but the true trial by fire was the 2020 pandemic. Overnight, Zoom’s usage exploded—it rocketed from about 10 million daily meeting participants in December 2019 to 300 million in April 2020. That’s a 30x increase in a matter of months.
Very few software architectures are designed to handle such an astronomical surge. Zoom managed this hypergrowth by quickly scaling up its cloud infrastructure (leveraging data centers and public cloud services to add capacity) and by prioritizing performance over new features (the CEO instituted a 90-day feature freeze to focus on stability and security). It wasn’t flawless – the influx of users also exposed security gaps (like “Zoom-bombing” incidents) that Zoom’s team had to race to fix. They rolled out Zoom 5.0 with default passwords and better encryption within weeks and later introduced end-to-end encryption for all users.
The Zoom case highlights two important scaling truths: infrastructure scalability (can your servers/architecture handle 10x or 100x load?) and process scalability (can your team respond to crises and patch issues quickly at scale?). Zoom’s custom software was designed from the outset for efficiency – its video codec and networking were highly optimized – which gave it an edge when scaling.
But even so, without rapid scaling of backend resources and swift software updates, Zoom might have buckled under demand. Instead, it emerged from 2020 as a household name and a critical global service, in large part due to the scalability of its platform.
Scaling Strategies for Custom Software:
Re-architect for Distribution: Many startups start with a monolithic architecture (everything in one codebase or one database). As users grow, splitting the system into components or microservices becomes necessary to distribute the load. Slack’s journey illustrates this well. Initially a monolith served Slack’s needs. But as Slack signed bigger customers (e.g., large enterprises with tens of thousands of employees), on Slack), the company introduced an Enterprise Grid architecture (2017) that allowed multiple interconnected workspaces and sharded data across databases. Essentially, Slack moved from “one big workspace” per company to a hierarchical model: many smaller workspaces under an organization umbrella, with a special global shard for shared data.
This was a significant architectural evolution requiring custom engineering – it solved immediate scaling issues (no single database had to handle a Fortune 100 company’s entire message history anymore). If your startup is hitting limits – e.g., certain queries are too slow or data doesn’t fit on one server – it’s time to consider re-architecting. Techniques include database sharding, moving to cloud-native services, introducing caching layers, or breaking out services for heavy workloads (as Slack separated real-time messaging into its own service early on).
Custom software gives you the freedom to redesign your system in the optimal way for your use case, rather than being stuck with a one-size-fits-all SaaS backend. Many famous scaleups (Twitter, Netflix, etc.) underwent major refactors during their growth: Twitter famously went from a Ruby on Rails monolith that caused the “Fail Whale” outages to a resilient microservices architecture (with components in Scala, Java, etc.), and Netflix migrated from a data center monolith to a distributed, cloud-based microservices system to handle streaming growth.
The common theme is don’t fear rebuilding parts of your product. As Thoughtworks tech director Tim Cochran notes, the practices that help you in early startup days (rapid hacks) “need to change once growth kicks in.” Allocate time and resources to refactor and improve the foundation. It’s an investment that pays off in performance and agility as you scale.
Address Technical Debt and Quality: During scaling, neglecting the “dirty” parts of your code can come back to bite hard. A startup that continually slapped Band-Aids on the MVP code will find it increasingly difficult to add new features or even maintain stability. In scaling mode, wise startups schedule periods for “paying down tech debt.” This can mean rewriting a fragile module, improving test coverage, automating parts of deployment, or upgrading to more robust frameworks.
It might slow feature development temporarily, but it dramatically improves the pace long-term. As Martin Fowler’s team observed, if early shortcuts aren’t addressed, “the low-quality MVP becomes core components… with no clear path to improve,” making it “increasingly difficult to expand the team or feature set.” Successful scale-ups balance new feature work with infrastructure work – some even do a “V2” of their platform. For instance, when Facebook grew, it rewrote its PHP core with a custom compiler (HipHop) to handle scale; Instagram famously refactored significant parts of its backend as it went from 1 million to 100 million users. Prioritizing code quality, modularity, and testing during the scale phase creates a virtuous cycle: better code -> fewer bugs and faster onboarding of new engineers -> faster development of new features. It’s no surprise that many startups bring in experienced engineering leaders or architects at this stage to guide these improvements.
Optimize, Automate, and Monitor: At scale, efficiency matters. Small inefficiencies that were fine when you had 100 users can become crippling with 100,000 users. Startups often invest in performance optimization of their custom software- e.g., database query tuning, using content delivery networks (CDNs) to speed up assets globally, or optimizing algorithms. A real example: Wise had to optimize how it routes and nets transactions; moving money for millions of users across currencies requires complex logistics to minimize fees and wait times. By investing in optimization (both in code and in financial operations), Wise achieved a level of efficiency that let it remain profitable even as it scaled – a rarity in fintech. Automation is another pillar: as user volume grows, tasks like deployment, scaling servers, backups, and monitoring should be automated.
Embracing DevOps practices – infrastructure as code, CI/CD pipelines, and automated testing helps a startup deploy updates daily (or hourly) without downtime, which is essential when you have a global user base expecting 24/7 service. Monitoring and observability tools also become crucial: you need real-time insights into system health. Companies at scale instrument their software with dashboards and alerts (APM – Application Performance Monitoring) to catch issues before they impact all users. Think of it as adding a nervous system to your software as you grow, you need to feel where the pain points are instantly. When Zoom’s usage exploded, you can bet their engineers lived on dashboards showing meeting connection success rates, CPU loads, etc., to ensure the service stayed up. Scaling isn’t just about writing code faster; it’s about running a larger, more complex system effectively.
Scale the Team and Processes: With growth, it’s not just the code that scales—the engineering team itself often grows from a handful to dozens or hundreds of developers. This necessitates process changes. Startups introduce more structured workflows – code reviews, design documents, sprint planning, and so on to coordinate the efforts of a bigger team. There is truth in the saying “what got you here won’t get you there”- the informal communication of a 5-person team doesn’t work when 50 people are working on the code.
For example, Slack, by its high-growth period, had dozens of developers and had to maintain multiple platform clients (web, iOS, Android, and desktop) in parallel, which required stronger project management. Empyreal Infotech and similar partners can assist here by augmenting teams with additional skilled developers or advising on process improvements. Empyreal’s global experience means it knows how to integrate with a client’s development processes seamlessly. If a startup needs to quickly hire 5 more engineers to build new features while the core team focuses on scaling the backend, partnering with a firm like Empyreal can be a smart move.
They provide “dedicated resources” with quick ramp-up, allowing the startup to increase output without compromising quality or velocity. As an offshore-friendly company, Empyreal can also enable near 24-hour development cycles by working across time zones, a tactic many scaling startups use to speed up progress.
Global Expansion and Localization: A critical aspect of scaling is expanding to new markets, which often requires additional custom software considerations. Wise’s growth again is instructive from a UK-based service, it expanded to support sending money to 170+ countries. This meant building a global payments infrastructure: handling dozens of currencies, complying with various countries’ regulations, and localizing apps in multiple languages. Custom software was needed to integrate with each country’s banking systems, implement KYC/AML compliance workflows for each jurisdiction, and allow for things like local payment methods.
It’s a huge undertaking, but it resulted in Wise “moving money without borders” as per their mission. Similarly, Airbnb (though not our main case here) had to scale from a site for renting airbeds in San Francisco to a global travel platform; it invested heavily in custom tools for things like internationalization, localized maps and search, and even a system to automatically detect and block scam listings – all bespoke software to handle scaling globally.
The takeaway is scaling globally often demands customizing your software for different regions (language, currency, cultural expectations) and ensuring robustness (because global users will be accessing your service at all hours, on different devices, with varying internet quality). This is where having a flexible custom codebase shines you can adapt and extend it as needed for each market. Startups that rely purely on third-party platforms might hit a wall when trying to expand globally if the platform doesn’t support certain locales or regulations.
Let’s not forget the healthtech scaling story: Livongo, after nailing its diabetes program MVP, scaled by broadening its platform to other chronic conditions and ramping up its user base through enterprise contracts. By 2019, just before its IPO, Livongo had over 600 self-insured employers and 48 healthcare clients using its programs. To support this, Livongo’s custom software had to evolve from a single-condition app to a multi-condition platform – integrating data from blood pressure cuffs, connected scales, etc., and delivering insights for weight management, hypertension, and behavioral health alongside diabetes.
The software’s analytics capabilities were scaled up (including AI-driven insights), and the system had to handle more data per user. Moreover, as a healthcare company, scaling meant scaling compliance and security – ensuring HIPAA compliance as users and data grew and integrating with electronic health records and insurance systems. Livongo even partnered with a device maker (Abbott) to offer advanced continuous glucose monitors, feeding that data into the Livongo app. This required custom integration software and APIs. The result was a richer health platform that could deliver personalized coaching across multiple conditions, setting Livongo apart from point-solution competitors.
By the time of its IPO in July 2019, Livongo’s annual revenue was on track to double to about $61 million, demonstrating successful scaling in a tough industry. They showed that a healthcare startup can scale both technology and clinical impact – and in doing so, they attracted the attention of Teladoc, which acquired Livongo in 2020 for $18.5 billion (one of the largest digital health mergers). For a startup, such an exit is the culmination of scaling well: proving not only that you can acquire users, but that your custom software can drive outcomes at scale (better health for tens of thousands of patients) and is valuable to larger players.
Scaling Stage Takeaways: If you’re experiencing fast growth, embrace it as a validation – but also recognize that what worked for 100 users may not for 100,000. The scaling phase is an opportunity to harden and broaden your software. As one expert noted, “You hit breaking points where you need to actively evolve to succeed at your next stage of scale.” Don’t shy away from big engineering changes in the name of short-term gains; investing in scalability is investing in the business’s ability to capture that growth.
Prioritize critical scaling projects: performance hot spots, critical refactors, and security. Improvements should be addressed sooner rather than later. Meanwhile, continue delighting users by rolling out new features – it’s a dual track. Many companies do this by splitting teams: one focuses on “keeping the lights on” and scaling, and another on “new features/experiments.” This is where hiring and partnering become vital because a small founding team can rarely do it all. Bringing in a firm like Empyreal Infotech to handle, say, a front-end rebuild or an Android app port while the core team scales the backend can accelerate progress.
Watch your metrics and listen to users. As you scale, data should drive decisions. Track response times, error rates, user growth cohorts, churn rates, etc. For example, if a certain feature is slowing down under load (perhaps search queries on your platform are taking 5 seconds with 10x users), that metric tells you where to optimize. Similarly, if users in a new country are signing up rapidly, invest in that region (add language support or servers closer to them). Scaling is as much a strategic endeavor as a technical one allocate resources to areas of greatest impact.
Case in point: By the end of this phase, you often hear the term “unicorn” for startups companies valued at over $1 billion—which typically correlates with significant scale in users or revenue. Wise, Slack, Zoom, and Livongo all achieved unicorn status during their scaling years. Slack, for instance, hit a $7 billion valuation by 2018 with over 8 million daily users (a mix of free and paying), and wise was valued around $5 billion by 2021 while still private. These valuations reflect investor belief that the companies’ custom software and platforms can keep scaling to eventually dominate global markets. To justify that, the startups must show they’ve built scalable tech and a plan for the next level—which leads us into the final phase of the journey.
Phase 3 – Enterprise-Readiness and IPO Preparation: Building for Longevity
Reaching the enterprise-ready stage means a startup is no longer just a scrappy innovator; it’s now a market leader or incumbent in the making, often gearing up for an IPO (Initial Public Offering) or a major acquisition. At this phase, the company might have millions of users, significant revenue, and operations in multiple countries. The role of custom software evolves again – now it’s about robustness, security, compliance, and scalability at an enterprise level. Public markets and large enterprise customers have high expectations: they demand near-zero downtime, strong data protection, detailed reporting, and compliance with a myriad of regulations. For the technical teams, this phase is about transforming your product and your organization’s processes to meet those rigorous standards. Essentially, the startup must “grow up” without losing what made it innovative.
Stepping up Security & Compliance: One of the first hallmarks of enterprise readiness is an obsessive focus on security and regulatory compliance. When startups prepare to go public, they often undergo formal security audits and compliance checks. A 2024 report notes that startups must ensure cybersecurity operations meet regulatory requirements and suggests conducting thorough security assessments as part of IPO prep.
This is because any security lapse can be disastrous once you’re in the public eye (not to mention for user trust). For SaaS or tech companies, obtaining certifications like SOC 2 Type II, ISO 27001, or compliance with NIST security guidelines can provide assurance to enterprise clients and investors. Startups likely need to implement features like advanced encryption, audit logs, role-based access controls, and single sign-on (SSO) integrations if they haven’t already, because corporate customers and regulators expect them.
Our case companies illustrate this well. Slack, as it moved toward an IPO in 2019, invested heavily in enterprise security features. It launched Enterprise Key Management (EKM) in early 2019 an add-on for its top-tier Enterprise Grid product – allowing customers in regulated industries (finance, healthcare, government) to bring their own encryption keys and have full control over message data encryption. Slack’s Chief Security Officer noted this was in direct response to requests from big banks and healthcare clients who needed tighter security to adopt Slack. With EKM, a financial firm using Slack can revoke access to its encrypted data at any time or meet strict compliance by managing keys via their own HSM (Hardware Security Module).
Slack also provided detailed audit logs for enterprise customers. These capabilities were custom-developed by Slack’s engineering team to meet enterprise needs and remove barriers for adoption in sensitive sectors. The payoff: by the time Slack went public (via direct listing) in mid-2019, it boasted 65 of the Fortune 100 as customers and strong penetration into large enterprises.
Enterprise features like EKM played a huge role in that trust-building. Slack’s IPO paperwork and Marketing could confidently address security-conscious prospects, saying essentially, “We’ve done the work; Slack is ready for your enterprise.” This is the kind of transformation a startup must make – from “move fast and break things” to “move fast and don’t break anything important.”
In fintech, Wise had to operate like a bank by the time it reached IPO. It secured licenses and regulatory approvals in multiple jurisdictions, built in compliance checks (KYC – Know Your Customer, AML- Anti-Money Laundering filters) into its software, and created a risk engine to monitor transactions. Going public in 2021 (London’s largest tech listing at the time at an ~$11 billion valuation), Wise opened itself to even more scrutiny. But having custom-built its core platform, Wise could adapt to regulatory demands. For instance, they developed features for fraud detection and account freezing internally and tools to generate reports for regulators, ensuring transparency. By 2025, Wise’s systems were handling money for 16 million people and businesses with trust at scale. It’s telling that Wise remained both fast-growing and profitable – profitability in fintech often indicates disciplined risk and compliance management through robust internal software systems (e.g., automated fraud prevention saves costs compared to manual reviews). When Wise listed publicly, it could highlight how its technology saves consumers £1 billion 764.
A year in fees and operates with bank-grade reliability, which helped convince investors of its longevity.
For healthtech startups like Livongo, enterprise readiness meant proving clinical efficacy and building credibility with large healthcare clients (and ultimately public investors). Before its IPO, Livongo made sure to publish peer-reviewed studies showing the effectiveness of its platform (such as the reductions in HbA1c 14) and costs), giving it data-backed legitimacy. Technically, it had to ensure HIPAA compliance and high data security – health data is highly sensitive. Livongo’s platform was likely audited and built to healthcare standards (encrypted data storage, strict access controls, etc.).
The company also hired experienced executives as it prepared for IPO, notably bringing on Zane Burke, former president of EHR giant Cerner, as CEO in 2018. This kind of leadership change often signals to the market that the startup is ready for prime time. Burke’s experience would help ensure Livongo’s processes (from product development to sales) met the standards of large hospital systems and insurers.
On the software side, as Livongo scaled to serve hundreds of clients and prepared for public markets, they would have needed to produce rigorous reports on outcomes and usage for clients likely facilitated by custom analytics dashboards and reporting tools in the product. By the time of IPO, Livongo was not just a tech solution but a platform demonstrating it could integrate into the healthcare system and improve outcomes at scale – a narrative investors bought into. (And indeed, the IPO was successful, and soon after, a merger showed its value.)
Operational Maturity: Enterprise readiness isn’t only about the product; it’s also about the company’s internal systems and processes. To run as a public company, certain operational capabilities are essential. According to a 2025 IPO readiness analysis by PwC, “operational maturity is the foundation for a successful IPO,” and many tech companies need to shore up people, processes, and systems in advance.
For example, 65% of companies disclosed material weaknesses at IPO filings, often due to lack of financial reporting oversight or inadequate systems. Startups can avoid that fate by investing in internal software like ERP (Enterprise Resource Planning) systems for finance, CRM systems for sales, and robust HR/payroll systems. In fact, 83% of companies had their ERP in place at least a year before IPO, showing the importance of scalable internal tools. This might mean the startup moves off of Excel sheets and QuickBooks to something like NetSuite or SAP, possibly with custom integrations.
Tech startups often have to build custom connectors between their product and financial systems to accurately track revenue (especially for SaaS metrics like ARR, deferred revenue, etc.). They also may implement data warehousing and business intelligence tools to have a clear picture of KPIs for investor discussions. Essentially, the back-office tech stack becomes as crucial as the customer-facing stack.
On the people side, enterprise readiness involves hiring key leaders and teams: IPO-bound companies usually bring in an experienced CFO (indeed, 75% of tech IPO CFOs had prior public-company experience) and build out legal, compliance, and investor relations functions. But even in these areas, custom
Software can play a role – for example, using tools to manage cap tables, stock option grants, and compliance workflows. Modern startups might use platforms for SOC compliance readiness or automated audit trails. The more you can systematize and automate, the smoother the IPO process. A tip from PwC’s analysis is that companies that started formalizing internal controls 1–2 years in advance had much better outcomes than those who scrambled in the last six months.
Performance and Reliability at Scale:By phase 3, the expectation is that your platform “just works.” Downtime or major bugs can severely damage credibility (and share price). Startups heading to IPO invest in site reliability engineering (SRE) and rigorous testing. They might establish multiple geographically distributed data centers or cloud regions for redundancy. Global CDN and failover systems often come into play to ensure even if one region goes down, users aren’t affected.
For example, Zoom, by the time it was an enterprise staple, had data centers across the world and also leveraged multiple cloud providers (AWS, Azure, and Oracle Cloud) to dynamically scale and provide redundancy. This multi-cloud strategy was essentially custom orchestrated by Zoom to ensure service continuity. Similarly, Wise operates across continents and must ensure money keeps moving even if one banking partner or data center has issues—so they likely have redundancy and fallback mechanisms coded in (like rerouting transactions through alternate corridors if needed).
For SaaS like Slack, uptime commitments become part of enterprise SLAs. Slack improved its resilience over time by, for instance, building the ability to gradually degrade features rather than total outages – e.g., if a service is under heavy load, maybe file upload is temporarily paused but messaging still works. These kinds of graceful-degradation features are custom engineering solutions that indicate a mature platform. Slack also had to prepare for huge spikes in usage (say, Monday morning at 9am across millions of users) their solution involved capacity planning and performance tuning (one stat: Slack handled 5+ million simultaneous WebSocket connections at peak times by 2020, a challenge they met through efficient code and lots of servers). As a result, by 2020 Slack was delivering billions of messages a week reliably.
Transparency and Reporting: Public companies must report financials quarterly and are scrutinized on metrics. The custom software’s role here is maybe less obvious but still present. For example, a SaaS company may build internal dashboards to accurately count daily active users, paying customers, churn, etc., which become the numbers executives share with Wall Street. Any errors in these systems can cause misreporting – a nightmare scenario. So startups put effort into data accuracy and single sources of truth for key metrics as they approach IPO. Automation in financial reporting (closing books quickly) is also important; interestingly, 66% of companies were able to close their books within 15 days post-IPO closes, indicating good systems), whereas pre-IPO many took much longer. This improvement often comes from better software/process—maybe implementing an ERP and using custom scripts to consolidate data from the product, billing, and accounting.
Culture of Quality: By this stage, the mindset shifts to continuous improvement and risk management. Startups may institute bug bounty programs, external security audits, and performance benchmarking. It’s not that innovation stops far from it, companies continue to innovate (Zoom, for instance, started adding AI and new communication features even as it matured) but changes are more carefully rolled out, and behind the scenes there are rollback plans and safety nets for any deployment.
At this point, Empyreal Infotech’s role as a scaling partner can be to provide specialized expertise to ensure the startup’s software meets enterprise standards. For example, Empyreal could conduct a thorough code review and refactoring sprint to harden security or develop additional modules needed for compliance (say, an EU GDPR data export feature or audit log system). If the startup is entering a new market segment that requires specific functionality, Empyreal’s team with its diverse industry experience can likely jump in to build that out quickly. As a “London-based custom software company that operates across multiple geographies,” Empyreal Infotech is positioned to integrate solutions globally.
Crucially, Empyreal has experience with both startups and enterprises, meaning they understand the journey and the technology evolution required. They “provide engineering solutions for businesses across sectors” and can thus bridge the gap as a startup transitions to enterprise. Whether it’s scaling cloud infrastructure or implementing an enterprise-grade mobile app interface, a partner like Empyreal ensures the startup’s tech can stand up to the scrutiny of big customers and the public market.
Real-World Check – The IPO Moment: When a tech startup finally rings the bell at the stock exchange, it’s a highly public endorsement of its journey. But interestingly, studies find that more than half of U.S. IPOs saw negative first-day returns – meaning hype doesn’t always match reality. The ones that succeed in the long run are those that prepared thoroughly. They acted “like a public company” well before being one by having the right systems and governance in place.
As an example, consider Zoom’s IPO in April 2019: It was one of the 80 highest-performing tech IPOs of 2019 (stock up 72% on day one). Analysts attributed that to Zoom’s solid profitability and reliability metrics. Zoom had spent years building a reputation for a high-quality, easy product, and by IPO time, it had the numbers to back it (e.g., 99.99% uptime, strong revenue growth, and profitable unit economics). Its custom-built tech was its moat. On the other hand, some startups that IPO with unresolved tech issues or a lack of clear profitability have struggled. The lesson is clear: “enterprise readiness” is not just buzzwords – it materially affects market confidence. By ensuring their software, security, and operations are rock-solid, startups set themselves up for a smoother IPO and sustainable growth beyond.
Empyreal Infotech—A Partner in Every Phase of the Journey
Throughout the phases from idea to IPO, one common thread is that having the right technical expertise at the right time is crucial. Startups often find that while their core team is great at innovation, scaling the product and polishing it for enterprise use can benefit from outside help. This is where Empyreal Infotech positions itself as a reliable scaling partner. As a custom software development company with global experience, Empyreal Infotech has “served clients ranging from startups to enterprises” and thus understands the evolving needs across this spectrum.
In the MVP phase, Empyreal can provide rapid prototyping and development services—turning an entrepreneur’s vision into a functional app or platform in record time. With a “Quick Turn Around Time” and “Utmost Quality Delivery” as part of their ethos, they help startups get to market faster while ensuring the codebase isn’t a spaghetti mess. This means fewer headaches in Phase
During the scaling phase, Empyreal’s team can augment the startup’s engineering capacity with “Dedicated Resources,” like extra developers, QA analysts, or UX designers on demand. This is incredibly valuable when a startup needs to accelerate feature development or tackle a major refactor. Empyreal’s experts have likely seen many of the scaling challenges before, so they bring proven solutions to the table – whether it’s setting up a microservices architecture, optimizing cloud deployments for cost and performance, or improving application security. For instance, if a startup’s platform needs to integrate with a third-party system or build a new mobile app for a growing user base, Empyreal can take on that project, delivering a high-quality result while the startup’s internal team focuses on core product improvements.
At the enterprise readiness stage, Empyreal Infotech can act as a consultant and implementer for the robust features and processes needed. They might assist in building out an admin portal with advanced analytics for enterprise clients, ensuring the startup’s product has the reporting and management tools corporate customers expect. Or they could help with data migration and integration when the startup implements an ERP system to clean up its internal operations. Given Empyreal’s broad experience (systems, cloud-native apps, mobile platforms), they can advise on best practices for reliability (e.g., setting up proper load balancing, disaster recovery) and even help with code audits to identify any lingering issues pre-IPO.
Perhaps most importantly, Empyreal Infotech prides itself on a partnership approach. They’re not just a dev shop that churns code – they collaborate closely to align with the startup’s goals. In fact, in a recent strategic move, Empyreal allied with design and branding firms to offer “unified digital solutions,” which means they recognize that a holistic perspective (technical + design + strategy) is needed to scale brands globally. This kind of integrated thinking is exactly what a scaling startup needs as it navigates new challenges.
To sum up, custom software is the backbone of a startup’s scale-up journey. It provides the differentiation at the MVP stage, the elasticity and power during growth, and the trust and reliability at the enterprise stage. By examining real journeys the way Wise revolutionized currency exchange with a custom platform, how Slack reimagined workplace communication and then re-engineered itself for the Fortune 100, how Livongo’s data-driven health solution scaled to improve outcomes, and how Zoom’s focus on quality helped it dominate a suddenly remote world we see that technology decisions are inseparable from business outcomes. Each of these companies identified what needed to be built versus what could be borrowed, and they invested in custom-building the critical pieces that made them global winners.
If you’re a startup founder or an innovator, take these lessons to heart: build fast, but build smart. Use custom software to bring your unique value to life. Continuously refactor and strengthen your tech as you grow. Don’t cut corners on security and user experience; those are investments in your reputation. And remember, you don’t have to go it alone. Partners like Empyreal Infotech are there to lend their expertise, having “witnessed transformative results firsthand” by scaling startups into success stories. With the right tech partner and strategy, your startup could be the next to go from a spark of an idea to ringing the IPO bell, all while scaling gracefully on the sturdy rails of well-crafted custom software
