How to Build an App Like Grammarly: AI Writing Assistant Development Guide

Spell check used to be a red line under a word that was misspelled. Now, a computer program that helps with writing can rewrite a paragraph and catch a sentence that someone else wrote. All before you finish your coffee. This change from checking for mistakes to actually helping write is why a lot of startups and businesses are asking how to build an app like Grammarly.

It is not easy to make something like this. Grammarly, which is owned by a company called Superhuman Platform Inc., has more than 40 million people using it every day and makes over 700 million dollars every year, and it is worth 13 billion dollars. In May 2025, the company got 1 billion dollars from General Catalyst’s Customer Value Fund. By January 2026, it merged with Coda to do more than just help with writing. This is how big this kind of thing can get.

This guide will tell you everything you need to know about AI writing assistant development. We’ll also talk about the features, technology, how much it will cost, and how to make money from it. Therefore, Grammarly is an example of what you can make, and this guide will show you how to opt for Grammarly-like app development.

So let’s get started! 

Market Research: The AI Writing Assistant Industry

The global AI writing assistant software market size is projected to grow from USD 3.40 billion in 2026 to USD 20.78 billion by 2034 with a CAGR of 25.38%. 

Grammarly’s own growth tells the clearest story of what this category has become:

• 40 million+ daily active users as of 2026, up from just 1 million in 2015
• $700 million+ in annual recurring revenue, growing more than 40% year-over-year in recent reporting periods.
• $13 billion valuation, held since a 2021 funding round.
• $1 billion in non-dilutive financing secured from General Catalyst’s Customer Value Fund in May 2025.
• January 2025 merger with Coda, expanding the platform beyond writing into broader workplace productivity.
• In July 2026, rebrand of the parent company to Superhuman Platform Inc.

Business and enterprise adoption is a major growth driver. If you are willing to embark on a grammar checker app development journey, keep this figure in mind to aim big. 

What are the Benefits of Grammarly-Like App Development?

Building an AI writing assistant app like Grammarly is a real business decision, and it’s working for companies that are taking the risk. Here’s why AI writing assistant software development is working today. 

1: Recurring, High-Margin Revenue

One of the prime advantages of a good writing assistant is that it fits perfectly with a subscription model. When someone gets used to getting grammar and tone help while writing every day, they don’t want to lose that. Which is why free-to-paid conversion works well in this area and why Grammarly’s business plan reportedly brings in almost half of its new money. 

2: Sticky, Habit-Forming Product

Writing is something that happens all the time. Emails, messages, papers, social media. A tool that is part of that routine helps keep people coming back almost automatically. That is a benefit in software, and it’s a main reason why AI writing software development projects interest founders who want products that people really keep using. 

3: Multiple Ways to Reach Users

A Grammarly-like app development does not need to be in one place. Extensions for browsers, mobile keyboards, web tools, and API connections to programs all give the same product several different paths for users to integrate. This increases the number of customers without needing a completely new version for every place. 

4: Strong Enterprise and B2B Upside

Individual users are just the starting point. Teams and organizations want fewer errors in client-facing communication, and admin visibility into how writing tools are used. And they are willing to pay a lot for that. This is where AI writing app development can really become profitable because business and enterprise plans usually have better profits than individual subscriptions. 

5: Real Product Differentiation Through AI

A well-built writing assistant is hard to copy quickly because the main value is in NLP, machine learning, and generative AI working together. The accuracy of suggestions gets better with usage data over time, which means that an early company with good feedback loops creates a strong advantage that is hard for others to catch up to. 

6: Expansion Into Adjacent Markets

A solid writing assistant foundation makes it possible to go into more than just grammar checking. Summarization, checking for plagiarism, tools for citations, and full support for writing content all come naturally from the same base engine. This is exactly the path that Grammarly has taken, most recently expanding into wider workplace productivity through its 2026 merger with Coda. 

7: Global, Language-Agnostic Demand

Writing is something all age groups do. Whether the target market is students, business people, non-native English speakers, or a specific industry, the need to write clearly and in the right way for the audience stays the same. That makes AI writing software development one of the lasting areas to build in with space, for both big companies and small focused products to do well together. 

Key Features to Add While Developing a Grammarly-Like App

Not every feature has the same impact on your budget. Some are easy to build and don’t really change much, while others affect AI models and the underlying systems in ways that can significantly change the cost of building an AI writing app. Here is a list of the features that’re most important when planning the investment. 

1: Voice Typing and Dictation

Adding voice input means using speech-to-text technology and then sending that written text through the grammar and style system that is used for written content. It seems like a change, but making sure that accents are understood and natural pauses in speech are recognized takes real work by developers. That is why adding voice typing can increase the cost of custom app development a lot, especially if you want it to work well on different devices. 

2: Style and Tone Suggestions

This is one feature that really needs a lot of work from the software. To figure out if something is written casually or if it is too blunt, the model needs to be trained. Moreover, tone detection is also very important. It is something that the system has to be good at if you want the writing to sound right. 

3: Multi-Platform Integration

Users expect a writing assistant to follow them everywhere, like browser, desktop, mobile keyboard, and inside tools like Google Docs or Slack. Each of these platforms requires its own integration work, and browser extensions in particular come with their own technical complexity separate from the core app. The more platforms you support at launch, the more this single feature multiplies your overall Grammarly-like app development budget. 

4: Customizable Dictionaries

Letting users add their own terms like brand names or personal preferences sounds simple, but it means building a flexible data layer that the core grammar engine can reference in real time without slowing down suggestions. It matters a lot for business and enterprise users who don’t want the app flagging their own company name as a typo. 

5: Grammar and Spell Check

This is the baseline that every grammar checker app development project starts with. Grammar checker apps that use rule-based checking are relatively inexpensive to build. However, apps that use machine learning for context accuracy are more expensive to build. 

6: Real-Time Suggestions

Delivering suggestions as the user types is a UX expectation. It needs a backend setup that works with careful tuning to make sure the app does not slow down or feel unsmooth when someone types normally. This is one area where taking shortcuts becomes obvious in how the user feels about the experience. So it is not usually a place where businesses reduce their spending. 

7: Plagiarism Detection

Checking submitted text against a large database of published content usually means licensing access to a plagiarism-detection database or building and maintaining one yourself, both of which come with ongoing costs beyond the initial build. This feature adds real, defensible value for academic and professional users. 

• 8: Translation Features

Adding multilingual support or a built-in translation feature means the app can be used by a lot more people. It also means we have to add special tools that can translate languages or teach the app to understand each language. 

Together, these features form the foundation of any credible AI writing software development project. Let’s also see the advanced features of an app like Grammarly. 

Advanced Features for AI Writing Assistant Development 

When the core writing checks are good, the next layer of features is what makes a grammar checker into a real writing assistant. This is what turns it into a genuine AI proofreading software must-have. 

1: Paraphrasing 

When you use paraphrasing, you can pick a sentence or paragraph. Then get different ways to say the same thing. A good paraphrasing feature gives you options to choose from, like formal, casual, or shorter versions so the suggestion fits the way you are writing. This is better than getting one version that does not really fit what you are doing. 

2: Summarization

Summarization solves a different problem entirely. Instead of helping to write, it helps you read faster, condensing long documents, articles, or reports into their key points. This feature tends to matter most for professional and academic users who are drowning in long documents and just need the gist. It’s a smaller feature on paper, but it’s often what convinces a busy user that the app is worth keeping open all day. 

3: Plagiarism detection 

Plagiarism detection checks the text you submit against a collection of published work. It finds parts that match or are very close to existing content. Shows where those parts came from. This tool is very important for AI writing assistant software that is used by students and people working on projects. In those cases, having work isn’t just a good idea but a real must. 

4: Citation generation 

Citation generation pairs naturally with plagiarism detection, automatically formatting references in common academic styles like APA, MLA, or Chicago. It’s a small, almost administrative feature, but it removes a genuinely tedious part of academic writing, and it’s often the exact kind of convenience that makes a free user decide the paid tier is worth it. 

5: Generative AI writing suggestions 

Generative AI writing suggestions are at the edge of what a modern AI text editor can do. Beyond just pointing out mistakes, this tool changes whole sentences or paragraphs or even creates new content based on a short idea. It uses the powerful language models that make today’s best AI tools work. This is the part that changes a product from “tells me what I wrote” to “helps me write it from the start.” 

Each of these advanced features builds directly on the NLP and machine learning foundation. Adding these thoughtfully is what separates writing assistants that genuinely earn a subscription from ones that feel like a gimmick.

Step-by-Step Process To Build an App Like Grammarly

Organizations planning AI writing app development should expect the process to follow the steps:

Step 1: Discovery and Requirement Gathering 

• To start, figure out what the main purpose of the product is and who it is for. Is it for people who write on their own, students, or teams of people at work?
• Pick which intelligence models our product will use.
• At first, also try to start with an MVP with just a few features and then add more later. 

Step 2: UI/UX Design

• Design an inline suggestion experience that feels helpful rather than intrusive.
• Prioritize a clean, distraction-free writing surface, since the product’s core value depends on users trusting and actually reading its suggestions.

Step 3: AI Model Selection and Training

• Integrate the chosen LLM API or begin fine-tuning a model on relevant grammar-correction datasets.
• Build an evaluation framework to measure suggestion accuracy before launch, not just after

Step 4: Backend and API Development

• Build the microservices layer covering text analysis, suggestion generation, and user management.
• Design the public-facing API early if API licensing is part of the long-term monetization plan.

Step 5: Frontend and Extension Development

• Build the web application, mobile app, and browser extension in parallel where team capacity allows.
• Test the browser extension across multiple sites and text-input types, since DOM structures vary significantly across platforms.

Step 6:  Testing and QA

• Run functional testing across all writing surfaces (web, mobile, extension).
• Specifically test suggestion accuracy against a curated set of known grammar and style errors, not just general app functionality.
• Load-test the real-time suggestion pipeline to confirm latency stays low under concurrent usage.

Step 7: Deployment

• Launch with monitoring and alerting fully configured, particularly around LLM API usage and cost.
• Consider a phased rollout starting with the web app before extending to browser extension and mobile.

Step 8: Maintenance and Continuous Model Improvement

• Use accepted/rejected suggestion data to refine model performance continuously.
• Monitor LLM API costs closely as usage scales, since inference cost is an ongoing operating expense distinct from the initial build.

Grammar Checker App Development Team You’ll Need

For bundling a grammar check app, you need to hire an app development company. The company will form a team that will include:

Before commencing, always look into the portfolio and experience of each team member. Selecting the right partner helps you reduce overhead costs. 

Cost to Build an App Like Grammarly in 2026

Understanding the cost to build an app like Grammarly requires breaking the investment down by scope, since an MVP and an enterprise-grade platform are fundamentally different builds.

It is also important to factor in the hidden and ongoing cost of developing an application like Grammarly. Once you get in touch with the right mobile app development company, they will walk you through these things one by one. 

Challenges in Building an AI Writing Assistant

Building a Grammarly-like app looks straightforward from the outside. In practice, most of the real difficulty in AI writing assistant development shows up after launch, once real users with messy, unpredictable writing habits start using the product at scale. Here are the challenges that matter most, and how to think through each one.

1: Language and Context Accuracy

Problem: Getting grammar rules right is the easy part. The hard part is handling everything that doesn’t follow the rules cleanly, like idioms, informal writing, slang, and the natural patterns of non-native English speakers. 

Solution: The most reliable way to manage this is to continuously test suggestion accuracy against real user writing samples rather than relying only on clean, textbook test datasets, since real-world writing rarely looks like a grammar exercise.

2: Latency in Real-Time Suggestions

Problem: Users expect suggestions to appear the moment they stop typing. That expectation puts genuine pressure on the backend, especially once the app is running multiple checks. 

Solution: Split the workload by weight: lightweight models handle the instant, real-time checks, while heavier LLM-powered features like paraphrasing are reserved for on-demand actions the user explicitly triggers.

3: Data Privacy and User Trust

Problem: People paste personal and business documents into writing assistants without a second thought, which means the app is often handling far more sensitive content than users consciously realize. A single data-handling misstep can quickly erode trust in a product built around helping people communicate. 

Solution: Encryption and clear data-handling practices need to be built in from day one. Also, the app should be transparent about exactly what is and isn’t sent to third-party AI providers during processing.

4: Scaling AI Inference Costs

Problem: Every AI-powered suggestion that calls an LLM API carries a per-request cost, and as user volume grows, that cost can climb faster than revenue if it isn’t watched closely. 

Solution: Track cost-per-user closely from early on, and introduce fine-tuned, smaller models for high-volume, lower-complexity checks once usage data makes it clear where a general-purpose LLM is overkill for the job.

5: Avoiding Suggestion Fatigue

Problem: An app that flags every possible issue, all the time, quickly becomes exhausting. Suggestion fatigue happens when users are shown so many corrections that they start ignoring the feature entirely, which defeats the entire point of building an AI proofreading software in the first place. 

Solution: Prioritization: suggestions need to be ranked by real impact, with minor stylistic nudges shown less aggressively than genuine grammar errors, so the app feels like a helpful editor rather than a nagging one.

Monetization Models and Revenue Streams

Getting the AI right is only half the equation; the other half is figuring out how the app actually makes money. The good news is that AI writing assistant development lends itself naturally to several proven revenue models. Here they go: 

1: Freemium Model

The freemium model is where most AI writing app development projects start, and for good reason. Free basic grammar and spell checking gives users enough value to make the app part of their daily routine, while more advanced generative features like paraphrasing and tone rewriting sit behind a paywall.

2: Subscription Tiers

Once a freemium base is established, individual, business, and enterprise subscription tiers each unlock progressively more advanced features and administrative controls. This is where the real revenue tends to concentrate. Grammarly’s own business tier reportedly drives close to half of the company’s new revenue. A well-designed AI writing software development product should treat the business tier as a first-class priority from early on.

3: API Licensing

Not every user of your writing engine needs to be a person typing into your app directly. Exposing the core grammar and suggestion engine as a licensable API lets other software products embed writing assistance directly into their own interfaces. This opens a genuine B2B revenue stream that runs parallel to the standalone app. It’s often more scalable than consumer subscriptions, since a single API customer can bring thousands of end users without the cost of acquiring each one individually.

4: Education and Institutional Licensing

Schools and universities represent a high-volume customer segment available for a writing assistant software product; this is where citation generation and plagiarism detection genuinely earn their keep. Educational institutions buy in bulk and are a dependable revenue base that smooths out the ups and downs of individual subscriber growth.

What are the Popular Grammarly App Alternatives?

Looking at how competitors have carved out their own space is genuinely useful before starting any Grammarly-like app development project. Each alternative below has found success by doing one thing particularly well. Let’s explore them:

The clearest gap in this comparison is that almost every alternative specializes in a single strength, such as paraphrasing, style analysis, translation, or multilingual support. That specialization gap is exactly where new niche entrants tend to find real traction. An AI writing assistant software built specifically for a single industry just needs to be the best choice for one well-defined audience Grammarly and its broader competitors haven’t fully nailed yet.

Why Choose Apptunix for AI Writing Assistant Development?

Building a genuinely useful AI writing assistant requires real experience with NLP, LLM integration, and the specific engineering challenges. Apptunix, with 13+ years of experience in the field, fulfills clients’ expectations with flying colours. 

We have proven AI and NLP development experience across SaaS and enterprise products. Our team builds scalable, cloud-native architecture engineered to handle real-time suggestion delivery without latency issues as usage grows. 

If you’re evaluating building an app like Grammarly, our AI writing assistant development services can scope your specific requirements and provide a tailored plan and estimate.

Ready to build your AI writing assistant? Talk to our team for a consultation tailored to your target users, feature priorities, and budget.