Remarkable Product as a Service

Product development agency for startups and growth-stage teams. Senior engineers, product managers, and designers who deliver – from discovery to scale, with AI woven into how we build.

End-to-end product development

Railsware integrates into your business as a cohesive team of product managers, engineers, and designers. We combine our knowledge with AI capabilities to bring value to end users and support steady growth.

AI power, human-guided

AI tools are now part of how every Railsware team works. We use it to ship MVPs faster, integrate AI into existing products, and rescue codebases that other teams generated with AI and couldn't ship.

Lean engineering

We cultivate a genuine engineering culture focused on smooth scaling, easy maintenance, and clutch performance. Senior engineers own architectural decisions, and AI-assisted coding accelerates the build without trading off code quality.

Product and design

Engineering alone doesn’t make a product. We pair build capacity with product management and user-centric design – running discovery, shaping the MVP, and tailoring experiences that solve real problems for real users.

Featured
technologies

Ruby on Rails and Node.js are our main backend choices. React.js leads on the frontend, with React Native for mobile. Several projects also use Go and Python. We build on AWS and Google Cloud. Across every project, we use AI tools to accelerate engineering and to build AI-native product features.

AI tools to accelerate development: Cursor, Claude Code, and GitHub Copilot, used by our engineers under senior review, not in place of it.

Our product development cycle

Ideation & Discovery

The first phase answers two questions: is this the right product, and who is it for. We run BRIDGeS, a product discovery framework Railsware has been using for over 19 years, to map the problem space and define the ideal customer profile in parallel.

Surface and rank product hypotheses worth betting on
Define the ICP: who feels the pain, where they are, what they use today
AI accelerates competitive research, market analysis, and customer interview synthesis
Output: product strategy, roadmap, and a team aligned on both

MVP & Release

AI-accelerated product development shortens the path from idea to launched MVP, often by weeks. We start with clickable prototypes, validate with real users, build with AI-assisted coding under senior engineering review, then land the public release – loud or quiet, depending on the product.

Clickable prototypes and user validation
AI-assisted development, senior review
Production-ready polish and tuning
Public release and launch motion
Analytics instrumented for what comes next

Iteration, Growth
and Scaling

The product is live, the analytics are running, and the real work starts. We track metrics, talk to users, and run experiments, but we don’t ship every feature request on first demand. The fastest way to ruin a product is to bolt on whatever the loudest user asked for last week.

Every hypothesis gets stress-tested against data and strategy before hitting the roadmap
AI clusters feedback from tickets, interviews, and surveys, so patterns surface fast
Metric anomalies caught earlier, and hypothesis testing runs on evidence, not instinct
Output: steady growth driven by what the data says, not what the inbox says

What we do

Fix Vibe Code

Refactor AI-generated code into production-ready, scalable codebases.

AI Integration

Add LLM features to existing products using OpenAI and Anthropic APIs.

AI-Driven MVP

Fixed-scope MVPs built with AI-assisted coding and senior review.

Web

Web app development on Ruby on Rails, Node.js, Python, and React.

Mobile

Native and cross-platform mobile app development for iOS and Android.

Product Design

Clickable prototypes, UX research, and UI design for shipping products.

Tech Audit

Independent audit of your codebase, architecture, and engineering team.

Our Products

Email Delivery platform

mailtrap.io

Set of tools for Jira

titanapps.io

Data Integration Platform
& AI Analytics

coupler.io

Clients about Railsware

Frequently asked questions

A product development agency designs, builds, and ships software products on behalf of clients. The scope typically covers product discovery, UX and UI design, engineering, QA, and post-launch iteration. A good agency contributes its own product and engineering judgment rather than just executing a spec – meaning it pushes back on weak hypotheses, recommends architectural choices, and takes responsibility for outcomes alongside the client.

Railsware offers two pricing models. AI-accelerated MVP engagements are scoped as a fixed amount based on the product spec – useful when the goal and timeline are clear. A release-ready MVP will cost between $50–100K. Longer-term product development runs on a time-and-materials basis at $120 per hour. Team size is flexible. Usually, we start from two full-stack engineers and scale the team depending on your needs, requirements and timelines. Most product development agencies in the US and EU charge somewhere between $100 and $200 per hour for senior teams.

Hire an agency when you need to ship a product faster than hiring allows, when you don't yet have a senior engineering leader to make architectural decisions, or when the work is well-defined but not core to your long-term team. Build in-house when the product is your company and you're ready to invest twelve-plus months in recruiting, onboarding, and managing a team.

We build products across SaaS, fintech, edtech, and consumer applications. Past work includes scheduling software (Calendly), trading analytics (TradeZella), K-12 communications (ParentSquare), education data analytics (BrightBytes, acquired by Google), and consumer apps (Seedtime). We work with regulated and domain-heavy products as well as founder-led startups.

AI-assisted coding is the practice of writing software with the help of AI tools that generate, suggest, refactor, or review code (such as Cursor, Claude Code, and GitHub Copilot). The engineer stays in charge: they direct the work, review every output, and own the architectural decisions. The AI handles the parts that used to eat hours (boilerplate, repetitive patterns, test scaffolding, documentation, routine refactors) and lets the engineer spend that time on the parts that actually matter, like system design, edge cases, and product judgment.