Railsware is a product studio, a tech company that builds, owns, and grows multiple software products as a core business, not a side project. Unlike an agency (which builds for clients) or a single-product startup (which bets everything on one idea), a product studio ships multiple products it owns while also doing consulting work that keeps the engineering team sharp.

Our team has been scaling our own products: Mailtrap, Coupler.io, and TitanApps, which are trusted by over six million users. Although it looks like a step-by-step journey, each product actually started as a small internal fix for problems we couldn’t solve with existing tools.

Below, we share how each product began, what it does today, and how it gradually shaped our product studio model.

What a product studio is (and isn’t)

The easiest way to understand a product studio is by what it’s not. An agency builds software for clients, so the client’s product is the business. A SaaS company typically goes all-in on a single product, and that product is the entire business. A product studio builds multiple products it owns, while also doing helping clients develop and grow their products. Railsware has already created products for dozens of partners, including the famous case of Calendly that became a several-times unicorn.

What ties it together is one team. There are no separate product benches. The same engineers, designers, and product managers who consult on partner projects can also be involved in building and running the product studio’s own products. That dual exposure, shipping for clients and shipping for yourself, creates a feedback loop that’s hard to replicate in either a pure agency or a pure product company.

How the product studio model works at Railsware

At Railsware, we have three own products: Mailtrap, Coupler.io, and TitanApps (and more coming soon!) Each product has its own squad, a dedicated team with its own roadmap and priorities. At the same time, specialists like designers or QA engineers can support several squads when needed, so products get focused expertise without every team maintaining a full bench of every role. We share common approaches to how we deploy, test, and run experiments, which means a solution one squad builds today reaches another squad by the end of the week.

Here are the benefits of the product studio model. When Mailtrap’s team solves a tricky infrastructure problem, that knoweldge reaches the Coupler.io team within days. When TitanApps figures out a marketing life hack, the whole company gets to know about the new insight. Expertise doesn’t stay locked inside one product. It moves sideways, fast. That cross-pollination is one of the main reasons a studio can compete with larger, single-product companies on an individual product basis.

Mailtrap: a sandbox turned delivery platform

Mailtrap started when a Railsware engineer built a fake SMTP server in his spare time to stop test emails from reaching real inboxes. No business plan, just a sandbox for inspecting outgoing messages safely. We shared it publicly, developers recommended it to each other, and Mailtrap grew to nearly a million users through word of mouth alone.

Over time, users asked for more than testing. They wanted transactional sending and marketing in the same tool. That demand shaped what Mailtrap is today.

What is Mailtrap, and how does it work?

Mailtrap is an email delivery platform that combines email testing, email sending, and email analytics in one tool. It is built for development and product teams that want to manage the full email workflow without switching between providers.

1. Email sandbox

One of the easiest mistakes during development is forgetting that staging environments can still send real emails. A developer tests a signup flow, password reset, or notification system and suddenly test messages land in actual customer inboxes. Sometimes it’s harmless. Sometimes it creates confusion, support tickets, or trust issues.

Mailtrap was originally built to prevent exactly that scenario. Instead of allowing emails to leave staging environments, Mailtrap team captures outgoing messages inside a safe sandbox where teams can inspect and debug them before release. Developers typically use it to:

• preview how emails render in HTML and CSS,
• inspect headers and attachments,
• check responsiveness across email clients,
• review spam scores,
• and verify dynamic content before emails ever reach real users.

2. Email API/SMTP

Once emails move from staging to production, a different set of problems usually appears. Emails may technically be “sent,” but teams still struggle with delivery issues:

• password reset emails arrive late,
• authentication codes land in spam,
• order confirmations disappear,
• or users never receive critical notifications at all.

What makes this difficult is that many email providers only confirm that an email left their server, not whether it actually reached the inbox. Mailtrap’s sending infrastructure was built around that gap in visibility. The platform supports transactional emails such as:

• password resets,
• auth codes,
• order confirmations,
• shipping notifications,
• and other system-generated messages.

It also includes tools commonly needed to improve deliverability, including SPF, DKIM, and DMARC authentication, IP warm-up support, bounce tracking, and delivery analytics. Instead of only showing that an email was processed, the system helps teams understand what happened afterward, whether the message bounced, landed in spam, or successfully reached users.

3. Email marketing

Many companies eventually end up managing transactional emails and marketing campaigns in completely separate systems. That usually creates small but frustrating operational problems:

• different analytics dashboards,
• duplicated contact management,
• inconsistent sender reputation,
• and disconnected reporting between product and marketing teams.

Mailtrap added email marketing after analyzing similar complaints from users who wanted fewer tools to maintain. So, the platform includes:

• a drag-and-drop email editor,
• reusable templates,
• audience segmentation,
• campaign scheduling,
• and campaign analytics for opens, clicks, bounces, and spam complaints.

Therefore, teams can manage sending reputation and reporting in one place, rather than piecing data together across multiple providers.

Why do teams choose Mailtrap?

Preventing test emails from reaching real users. 

Mailtrap’s email sandbox intercepts every outgoing message from staging and development environments. Developers can inspect HTML rendering, check spam scores, validate headers, and preview emails across clients without any risk of test messages reaching real inboxes..

Testing and sending from one platform. 

Many development teams use one tool for email testing and a different one for production sending, which means separate credentials, separate dashboards, and no shared view of what’s happening between staging and production. Mailtrap combines email testing, transactional sending, and email marketing in a single platform with one sending reputation and one analytics dashboard.

Diagnosing deliverability problems, not just confirming sends. 

A common frustration with email services is that they confirm an email was sent but don’t explain what happened next. Mailtrap’s analytics break down delivery into specifics — bounces, spam complaints, opens, clicks, so teams can see where emails drop off and why, rather than troubleshooting blind.

Access to deliverability specialists.

Email deliverability involves ISP reputation algorithms, authentication configurations, and feedback loops that are hard to debug without experience. Mailtrap’s support team includes email deliverability experts who help with inbox placement issues, domain reputation, and sending configuration, not just general product questions.

What is the future of email workflows?

Fast forward to today, and Mailtrap has matured into a comprehensive platform handling the entire email lifecycle: testing, sending, and now marketing.

We are also adapting to how developers work today by introducing Mailtrap MCP. This is a server that allows developers to seamlessly integrate our testing and sending APIs directly into AI coding assistants like Cursor, Claude, and Windsurf. Alongside a newly released CLI, we are also putting the finishing touches on a native desktop application, a project that surprisingly began as a weekend vibecoding experiment by our co-CEO.

Coupler.io: a spreadsheet error turned data powerhouse

Coupler.io started after a billingmistake caused by a human error in an Excel formula. At the time, Railsware teams were manually combining revenue, payroll, and operational data across spreadsheets. One typo in a formula cost us $120,000.

The issue was not the spreadsheet itself, but the process behind it: manual exports, copy-paste workflows, and reporting spread across disconnected tools. The first fix was a small internal script that automatically moved data between systems. Later, we realized most companies had the same problem:

• marketing data in ad platforms,
• sales data in CRMs,
• finance data in billing tools,
• and reporting manually stitched together in spreadsheets.

By 2020, that internal tool became Coupler.io.

What is Coupler.io and how does it work?

Coupler.io is a data integration and AI analytics platform that connects over 400 data sources (including Google Sheets, Excel, HubSpot, Salesforce, Shopify, QuickBooks, Facebook Ads, Google Ads, and Jira) and automates data transfers to destinations like Looker Studio, Power BI, BigQuery, Snowflake, PostgreSQL, and Google Sheets. Teams set up data flows once, and Coupler.io refreshes them on a schedule without manual CSV exports, copy-paste, or formula maintenance.

Between source and destination, Coupler.io can transform data in transit: filtering, aggregating, joining, appending, and applying formulas. So needed information gets structured and ready for reporting rather than as a raw dump that needs cleanup.

Coupler.io also includes a dashboard builder, reporting templates, and AI-powered analytics that let teams analyze connected data using natural-language queries rather than SQL or manual spreadsheet work.

Why do teams choose Coupler.io?

Coupler.io is typically used by marketing, sales, finance, operations, and analytics teams that work with data spread across multiple tools but do not want reporting to depend entirely on engineers or manual spreadsheet work.

Keeping reporting updated without manual exports

Despite a variety of automation tools,  marketing, sales, and operations teams still rely on repetitive reporting workflows:

• exporting CSV files,
• updating spreadsheets,
• fixing broken formulas,
• and combining data from multiple platforms manually.

As companies grow, this process gets slower, more fragile, and harder to hand off. Coupler.io automates the data movement so dashboards and reports update on their own, without someone rebuilding them from scratch each cycle.

Making reporting less dependent on technical teams

Usually, even basic business questions require analyst support or SQL queries. 

• “Which campaigns generated the highest ROI?”
• “How did churn change last quarter?”
• “What affected revenue this month?”

These questions can involve multiple exports and several teams before anyone gets an answer. 

Coupler.io connects directly to reporting tools like Looker Studio, Power BI, and Google Sheets, so business teams can build and maintain their own dashboards from live data without routing every request through a technical team.

Understanding what the data means, not just collecting it.

The harder reporting problem today isn’t access to data. t’s making sense of what the data says. Teams can pull numbers from every tool in their stack and still not know what changed, why performance dropped, or where to focus next. 

Coupler.io’s AI-powered analytics let teams ask those questions in plain language and get answers grounded in their actual connected data, rather than building a new report every time a question comes up.

What’s next for AI in data-driven decision-making?

Coupler.io uses AI to bridge the final gap between having data and actually understanding it. Therefore, our team has already introduced AI Agent to allow users interact with their business metrics using plain English. Instead of building complex dashboards or writing SQL, non-tech specialists can simply ask what their churn rate was last quarter. Our engine validates the facts server-side before feeding them to the AI, ensuring the answer is based on hard numbers rather than hallucinations.

Through MCP integration and AI-driven insights, we’ve made live data directly accessible within assistants like Claude. The principle remains the same as it was five years ago: not just move data, but to make it useful, clear and trustworthy.

TitanApps: the Jira plugin turned productivity suite

TitanApps started in 2017 when Railsware needed structured checklists in Jira, and nothing on the Atlassian Marketplace fit. We built Smart Checklist internally, published it, and it picked up 500 paying clients on its own with ZERO marketing.

As it grew, we heard from HR, finance, and marketing teams who found Jira powerful but overwhelming. Their onboarding, approvals, and campaign workflows were scattered in docs and Slack threads. The problem wasn’t missing technology. It was missing processes. That shifted TitanApps from a checklist plugin toward codifying the workflows teams run daily but never formalize.

At the moment, TitanApps is trusted by 2.7 million users across Jira and monday.com.

What is TitanApps and how does it work?

TitanApps is a suite of productivity tools for Jira and monday.com that adds structured, repeatable processes to project management workflows.

• Smart Checklist adds multi-level checklists to tickets with automation, mandatory items, status tracking, and assignees. It helps turn tickets into step-by-step workflows for QA, onboarding, approvals, and other repeatable tasks.
• Smart Templates lets teams create reusable issue templates with pre-filled descriptions, checklists, sub-tasks, and custom fields to eliminate repetitive ticket setup.
• Smart Hierarchy shows complex issue structures (epics, stories, sub-tasks) in a single visual view instead of requiring teams to jump between screens.
• Smart Productivity Dashboard gives managers real-time visibility into workload, progress, and bottlenecks across Jira projects.

Why do teams choose TitanApps?

Reducing repetitive ticket setup. 

In many teams, ticket creation becomes repetitive: sprint tasks, QA steps, onboarding flows often follow the same structure but still get built each time manually. TitanApps Smart Templates helps standardize that repetition by letting teams apply pre-defined ticket structures instead of rebuilding the same fields and subtasks over and over.

Helping non-technical teams work in Jira. 

Jira is often not used only by engineering teams. HR, finance, and marketing teams still need to manage structured workflows such as approvals, hiring steps, and campaign tracking. The challenge is that these teams usually don’t work in Jira daily, so simple processes become time-consuming to set up or maintain. TitanApps is used in this context to run structured workflows inside Jira or monday.com without requiring deep platform expertise.

Codifying processes that otherwise live in people’s heads.

A lot of operational work inside companies isn’t documented, it lives in people’s heads or shared informally between teammates. That works until teams scale or change, and the process becomes inconsistent or lost. TitanApps helps convert these informal workflows into repeatable templates inside Jira and monday.com, so processes are easier to follow and less dependent on individual knowledge..

What’s next in productivity tools: automating the work teams skip

The next wave of productivity tools is shifting from adding workflows to removing repetitive, low-value work teams keep postponing.

In TitanApps, Smart AI Release Notes automates release summaries by pulling Jira issues via JQL (epics, stories, bugs, sub-tasks), collecting context from tickets and comments, and generating structured notes with AI. The same pattern applies to status updates, documentation, and other routine work that teams rarely prioritize.

Overall, productivity tools are moving from helping teams manage work to doing the repetitive parts of the work itself.

What we’ve learned about making the product studio model work

Running three products inside one company sounds neat on paper. In practice, it’s an ongoing negotiation between shared culture and separate priorities, between efficiency and focus, between what the studio needs and what each product needs right now.

Here’s what years of building damn good products have taught us.

Share the craft, not the priorities

Each product needs its own team, roadmap, and accountability. What’s shared is the engineering culture: standards, processes, and ways of working. That’s where the studio model creates leverage.

Build from real problems

All our products started as internal tools. The real validation came when people outside Railsware wanted them too. Solving your own pain creates a faster, more honest feedback loop than most research ever will.

Pay attention to mistakes

A broken workflow, a costly spreadsheet error, a missing Jira feature, each product started with friction worth fixing. Problems are often better product signals than ideas.

FAQ

What is a product studio and how is it different from an agency?

A product studio is a tech company that builds, owns, and operates multiple software products as its core business. An agency builds software for clients and hands it off. A product studio keeps its products long-term: handling development, growth, support, and iteration internally. Railsware is a product studio running three products: Mailtrap (email delivery platform), Coupler.io (data integration and analytics), and TitanApps (productivity tools for Jira and monday.com), serving over six million users.

Can you give me an example of a product studio?

Railsware is a product studio that builds and operates three software products alongside consulting work. Mailtrap started as an internal email sandbox after test emails accidentally reached real users. Coupler.io started as a script after a $120,000 spreadsheet error. TitanApps started as a Jira checklist plugin the team built for its own migration. All three grew from internal tools into standalone products, now trusted by six million users combined.

How do you turn an internal tool into a real product?

Based on Railsware’s experience with three products: build a tool that solves your own problem, share it outside your team, and watch for unprompted demand. All three Railsware products — Mailtrap, Coupler.io, and TitanApps, followed the same pattern: someone outside the company asked to use the tool before it was ever offered as a product. That organic pull, not internal enthusiasm, was the signal worth following.

How do you run multiple products inside one company without losing focus?

Railsware runs three products by keeping engineering culture shared but product ownership separate. Each product ( Mailtrap, Coupler.io, TitanApps)  has its own team, roadmap, and accountability. The coding standards, deployment practices, and experimentation methods are consistent across the company, so solutions travel fast between teams. But no product waits on another team’s priorities.

Is it worth building your own product if you’re already doing consulting work?

Railsware started as a consulting team before building any products. The first product, Mailtrap, came from an internal mistake, not from a deliberate product strategy. The lesson: consulting work gives you hands-on exposure to real problems, and sometimes the tools you build to fix those problems have a bigger market than you expect. All three Railsware products started this way — as internal fixes that turned out to solve problems every company deals with.

Treat “product studio” as a direction, not a label

Calling yourself a “product studio” takes five minutes, but building one is a perpetual work in progress. Running a shared engineering culture across multiple products is genuinely difficult. We’ve been doing it for years, and we’re still adjusting our approach every day.

That ongoing friction is the whole point. If the model ever feels settled or easy, you’re likely not paying close enough attention to the needs of the products. For us, a product studio isn’t a static label. It’s a commitment to keep building what we need and learning from the mistakes we make along the way.