In 2025, AI is no longer just a term; it is the driving force behind today’s innovations. AI apps, ranging from personal assistants to smart search engines, are changing the way we connect and interact with cutting-edge technology. Standout in this revolution is Perplexity AI, a search engine powered by AI that leverages the strength of large language models with real-time web information to deliver accurate, cited responses.
Based on recent PYMNTS reports, Perplexity AI is in advanced negotiations with Samsung to merge its AI technology into the smartphone ecosystem of Samsung, which could put Google’s Gemini assistant out of business on Galaxy phones. This latest stat highlights the increased demand and importance of AI-powered apps in the current market.
What Is an AI App and Why Are They Important?
AI-powered apps use artificial intelligence technologies to perform or do the tasks that are normally done by humans or human intelligence. These tasks are – understanding natural language, finding patterns, making decisions, learning from data, and more.
The popularity of AI apps stems from their ability to –
- Improve the User Experience: AI apps provide personalised and effective interactions.
- Automate tasks: AI apps can do repetitive or complex tasks automatically.
- Provide Valuable Insights: AI programs can process large amount of data and provide substantial insights.
Perplexity AI, for example, uses AI models such as GPT-4o and Claude to provide quick & valuable insights, which differs from standard search engines.
Want To Build An AI App Like Perplexity?
Step-by-Step Process to Build an AI App Like Perplexity
Developing an AI-based app such as Perplexity isn’t an easy plug-and-play endeavor — it is a strategically tiered development process that involves AI experience, data science, infrastructure, design, and continuous optimization. What follows here is a step-by-step breakdown of every stage of such an intelligent, real-time AI assistant development:
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Market Research and Planning
Before you ever write a line of code, it’s important to know who you’re creating the AI app for. Are you building an app for students? researchers? Business users? Knowing your user base informs everything from design to prioritization of features.
This stage includes:
- Competitor research (such as ChatGPT, Perplexity, Gemini)
- Establishing your value proposition
- Establishing workable budgets and timelines
- Planning critical user flows and use cases
Why it matters: A well-crafted product strategy guarantees your AI app addresses actual issues — and not just mimics what someone else is doing.
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Data Collection and Preparation
This is a critical stage because your AI apps are only as intelligent as the data they are trained on. As a result, you must determine or confirm the type of data required by your app, as well as where it will come from.
This may include:
- Curating web-scale datasets (such as open-source corpora)
- Purchasing proprietary datasets for particular domains (e.g., finance, healthcare)
- Cleaning, deduplicating, and formatting the data
- For an app like Perplexity, data quality is paramount since it has a direct impact on the accuracy, tone, and relevance of the AI responses.
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Model Selection and Training
With the proper data in hand, the next step is selecting the AI model architecture that underpins your app.
Some typical options:
- Large Language Models (LLMs) such as GPT-4, Claude, or LLaMA
- Fine-tuning a chosen model with transfer learning
- Building a custom transformer-based model, if required
Once a model is chosen:
- Train or fine-tune your preprocessed data sets
- Watch the model for bias, hallucinations, and performance mismatches
- For a Perplexity-style app, adding real-time retrieval-augmented generation (RAG) or web-browsing functionality might be necessary.
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Backend Development
Here’s where your app’s technical skeleton is established. The backend manages:
- Query processing
- API integrations with the AI engine
- Database management (user data, prompt history)
- User authentication and security
You can also configure:
- A cloud infrastructure on AWS, Google Cloud, or Azure
- A scalable system for real-time AI inference
Want to Implement These Features in your AI App?
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Frontend Development
We all know how crucial user experience is. The front end is what connects the user to AI magic, and it must be fast, simple, and accessible.
The primary concerns are:
- Clean chat interface
- Responsiveness on both mobile and desktop devices
- Quick loading times.
- React or Flutter may be used in the front-end technology stack.
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AI & System Integration
Now it’s time to tie everything together – integrate the trained AI model with backend services and render results on the front end.
You’ll develop:
- Real-time streams of prompts and responses.
- Query routing to several models or APIs.
- Error handling for failed or ambiguous queries.
- Consider this stage to be the equivalent of plugging in a high-level brain with a voice and body.
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Testing and Deployment
Now, test your app properly. Various forms of testing include –
- Functional testing to check the working of features.
- Model Performance Test for Accuracy
- User testing to provide feedback on the UI and UX
- Security testing to ensure user safety and prevent abuse.
- Deploy your AI application to your cloud infrastructure using CI/CD pipelines, after stabilisation.
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Maintenance and Continuous Improvement
Launching the app is only the beginning. After launch, your app needs adequate maintenance and continuous enhancement.
- Regularly update AI models when new data becomes available.
- Introduce new features based on user suggestions.
- Prepare reports on regulatory and ethical compliance.
- Scale infrastructure based on consumption trends.
- Ongoing development makes your software smarter, safer, and easier to use over time.
Step-By-Step Development Stages
| Stage | Key Actions | Tools/Tech Stack |
| Market Research | Competitor study, audience profiling | Surveys, Google Trends, App Radar |
| Data Preparation | Collection, cleaning, formatting | Python, Pandas, web scraping tools |
| Model Selection & Training | Choose/fine-tune LLMs | OpenAI API, Hugging Face, PyTorch |
| Backend Development | APIs, servers, and user logic | Node.js, Flask, AWS/GCP/Azure |
| Frontend Development | UX/UI, chat integration | React, Flutter, Figma |
| Integration | Connect frontend, backend, and AI | REST APIs, WebSockets |
| Testing & QA | Performance, UX, functionality | Postman, Selenium, unit testing tools |
| Maintenance & Scaling | Updates, scaling, analytics | CI/CD, APM tools, monitoring dashboards |
What Are The Key Components Of An AI App Like Perplexity?
To build a sustainable AI application, incorporate the following in your creation:
- Natural Language Processing (NLP): Enables the app to understand and process human language.
- Real-Time Data Fetching: Pulls out the latest information from the internet.
- Cited Sources: Includes citations for the information displayed.
- Friendly Interface: Facilitates ease of use on any device.
- Voice Input Support: Enables users to command by voice.
- Multi-Model Integration: Merges different AI models to give a performance uplift.
- Personalized Responses: Tailors responses according to user history and preferences.
Perplexity AI’s capacity for real-time searches online to find information on demand, crawling news websites, forums, social media, and news feeds, is a testament to the relevance of these features.
What Is the Cost to Build an AI App Like Perplexity?
The cost of developing an AI application like Perplexity can vary widely based on complexity, features, and development resources.
| Estimated Development Costs | Average Cost | Cost At TechBuilder |
| MVP (Basic Search + GPT-4 API) | $40,000 – $60,000 | $20,000 – $40,000 |
| Mid-Level App (Voice, History, Pro Mode) | $60,000 – $90,000 | $40,000 – $70,000 |
| Advanced Platform (Multiple AI Models + SaaS Dashboard) | $100,000 – $150,000+ | $80,000 – $130,000+ |
These figures are approximate and can vary based on specific project requirements.
What Are the Factors Influencing AI App Development Costs?
There are various factors that determine the total cost of building an AI app like Perplexity. Let’s understand these factors –
- Feature Complexity: Sophisticated features such as real-time data extraction and multi-model support lengthen time and expense for development.
- AI Model Selection: Proprietary models can have licensing costs.
- Data Needs: Diverse, high-quality datasets are needed to train powerful AI models.
- Location of Development Team: Fees differ geographically; offshore teams can provide cost benefits.
- Maintenance Requirements: Regular support and maintenance contribute to long-term expenses.
How to Lower the Cost of AI App Development Like Perplexity AI
To control and even minimize development costs:
- Begin with an MVP: Prioritize essential features to launch fast and collect user input.
- Use Open-Source Models: Leverage available AI models to save time and money on development.
- Development Outsourcing: Look for existing development teams in inexpensive locations.
- Cloud Services: Leverage scalable cloud infrastructure to effectively manage resources.
How TechBuilder Can Help You Develop Smart AI Apps
TechBuilder is a leading app development company that builds AI-based apps, tailor-made for your business’s requirements. Our team can guide you through every step of app development, from creation to upkeep, to develop a quality app that will serve your purpose.
As ScienceDirect indicates, with the AI app market growing to $126 billion by 2025, now is the time to invest in innovative solutions that set you apart from the rest.
Ready To Build an AI App Like Perplexity?
FAQ’s – Frequently Asked Questions About AI apps like Perplexity
Q1: How long does it take to develop an AI app like Perplexity?
A: The time required to develop an AI program such as Perplexity is governed by a range of factors, including intuitive UI/UX designs, features, integrations, complexities, and so on. At TechBuilder, we develop an AI app like Perplexity within a mere 2-3 weeks – not months.
Q2: What is the cost involved in developing an AI app like Perplexity?
A: The amount of money it takes to develop an AI app such as Perplexity varies based on a number of things: advanced tech and stacks, integrations, complexities, features, and so much more. At TechBuilder, we build an AI app like Perplexity at a reasonable price.
Q3: Can I use pre-trained AI models for my app?
A: Yes, it can enable quicker and less expensive development.
Q4: How can I protect my AI app’s data?
A: You can protect your AI app’s data by implementing strong security controls and following data privacy regulations.
