

Mansi Singhania — a seasoned blog author dedicated to creating high-quality, research-driven content that informs, engages, and adds lasting value across a range of topics.
Coming Up with app ideas is easy. Anybody can think of it But getting a cost of that app is really very important thing to know before to hire a person who is taking so much charges than the other developer.
Mobile app developers from different countries charge different rates to the customers as per their living standards.
When you plan to develop an app via an offshore company, it is important to rely on a tech partner that has a team of experienced developers. Hiring a highly skilled and experienced mobile app developer with UI/UX experience having expertise in different languages and platforms turns out to be very beneficial. In addition to this, they will also advise you on how to scope your project and what developers you need.
So in this article we will let you know the a proximate cost of developer if you hire developer from india.
If you are finding an app developer for the purpose of developing your app, then you should be well aware of the fact that there is no shortage of app developers for hiring around the globe. But, you need to consider only those developers who possess the best price range as well as a skill set. It also sometimes comes with certain cautions and also downsides. So, you should always stick to these principles before starting your quest.
First of all, you should know the basics and learn about those programming languages that are mainly used for mobile apps. So, you should find out what those target platforms are for making an app. Another important thing is to check the rates, i.e. how much it would cost if you certainly hire an app developer. Additionally, you should be well aware of the fact that who would be the users of your app.
Another most important thing is to particularly check the experience. So, if you need to hire someone, then it is mandatory for you to consider whether the developer knows this stuff or not. The developer does not always need to be a top expert in the field but should be able to provide the previous portfolio of his/her works. In that case, you should definitely ask for the samples of his or her code.
You need to seek someone who is much reliable and also that particular developer needs to be very much interested in your business. To get your app going, you will generally have to get along for the purpose of building a relationship with each other. There should also not be the presence of cultural or language barriers.
The average time is taken to develop an app, according to research is 8 weeks i.e., 320 billable hours. A simple app will cost $4500 in India compared to $45,000 in the US.
A complex one might cost $38,000 in India and $250,000 in US. A lot of app development project is hence outsourced to India.
The core developing behind an app consists of the following:
|
Team Member |
Duties |
Hourly Cost |
|
Project Manager |
Communicates with the team and the client to avoid gaps and keeps a check on deadlines |
$20+ |
|
Developer |
Develops the app by writing the codes and fixes bugs |
$25+ |
|
Back End Developer |
Ensures effective and uninterrupted communication between servers and database for the proper functioning of the app |
$20+ |
|
Designer |
Responsible for the way the app looks and user-friendliness of the app. And Latest UI |
$15+ |
|
Tester |
Ensures the app performs as per requirements. |
$15+ |
As it may be clear, an average cost may not suffice the question. Your app will have obviously different functionalities and features, with some consistent pricing methods. And This cos can vary according to company standard but this list is basic
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In today’s AI-driven world, data is the new oil — but raw data alone has little value. The real transformation happens when raw, unstructured data is cleaned, categorized and labeled properly. That process is called Data Labeling (Data Annotation).
Whether you are building a Machine Learning (ML) model, training a Computer Vision system, developing a Large Language Model (LLM), or improving an AI-powered chatbot, high-quality labeled data is the foundation of accuracy and performance.
Let’s explore how data labeling converts raw data into smart insights — and how Srishta Technology can help you accelerate your AI journey.
Data labeling is the process of tagging raw data (text, images, video, audio) with meaningful labels so that machine learning algorithms can understand and learn from it.
For example:
Labeling images of cars for Autonomous Vehicles
Tagging customer sentiments for Sentiment Analysis
Annotating medical scans for Healthcare AI
Transcribing and tagging audio for Speech Recognition
Without labeled data, AI models cannot identify patterns or make intelligent decisions.
High-quality annotation reduces bias and increases precision in ML models.
Most AI systems rely on supervised learning, which requires structured and labeled datasets.
Clean and well-annotated datasets speed up training cycles and reduce iteration costs.
Proper labeling ensures your AI solution performs reliably in real-world conditions.
A strong AI pipeline often requires multiple annotation formats:
Bounding Boxes
Polygon Annotation
Semantic Segmentation
Object Detection
Used in: Computer Vision, Retail AI, Surveillance, Autonomous Driving
Frame-by-frame labeling
Motion tracking
Activity recognition
Used in: Smart Security, Sports Analytics, Robotics
Named Entity Recognition (NER)
Sentiment Analysis
Intent Classification
Chatbot Training Data
Used in: NLP, AI Chatbots, LLM Fine-tuning
Speech-to-text transcription
Speaker identification
Emotion tagging
Used in: Voice Assistants, Call Analytics
Despite its importance, data annotation comes with challenges:
Maintaining annotation quality
Handling large-scale datasets
Data privacy and compliance
Domain-specific expertise requirements
Turnaround time pressure
This is where an experienced data annotation partner becomes crucial.
With 11+ years of experience in Data Annotation and AI Support Services, Srishta Technology specializes in delivering high-quality, scalable and cost-effective labeling solutions.
We have worked across:
Healthcare AI
E-commerce & Retail
FinTech
Automotive AI
Conversational AI
Our trained data annotators follow strict quality control processes to ensure high precision and consistency.
We implement:
Dual-layer quality review
Random sampling validation
Automated consistency checks
We follow strict data privacy protocols and NDAs to protect sensitive datasets.
Whether you need 10,000 annotations or 10 million, we scale seamlessly to match your AI training requirements.
Poor-quality data can:
Investing in professional data labeling improves:
In short, better data = smarter insights = higher business growth.
With the rise of Generative AI, Large Language Models, and AI Agents, the demand for:
is growing exponentially.
Businesses that invest in structured, high-quality datasets today will lead the AI race tomorrow.
Data labeling is not just a backend task — it is the backbone of successful AI systems. From raw data to actionable intelligence, annotation plays a transformative role.
If you are building AI solutions and need a reliable data annotation partner, Srishta Technology is ready to support your journey with expertise, scalability, and precision.

In today’s data-driven economy, businesses are rapidly adopting Artificial Intelligence (AI) and Machine Learning (ML) to gain a competitive advantage. From predictive analytics and automation to personalized customer experiences, AI is transforming industries such as healthcare, real estate, and cosmetics.
However, the success of any AI model depends on one critical factor: high-quality data annotation. Without accurately labeled and structured data, even the most advanced machine learning algorithms fail to deliver reliable results.
High-quality data annotation is not just a technical requirement—it’s a strategic investment that directly impacts business growth, operational efficiency and long-term scalability.
Data annotation is the process of labeling raw data—images, text, video, or audio—so AI systems can understand and interpret it. High-quality annotation means:
Accurate and consistent labeling
Domain-specific expertise
Scalable and structured datasets
Strong quality control processes
When done correctly, annotated data becomes the foundation for building high-performing AI models.
AI models learn from annotated datasets. Poor-quality data leads to incorrect predictions, biased outputs, and system failures.
High-quality data labeling services ensure that machine learning models are trained on clean, accurate, and relevant datasets—resulting in:
Higher prediction accuracy
Reduced error rates
Improved customer satisfaction
Well-annotated data reduces the need for repeated model retraining. This means:
Faster AI development cycles
Quicker product launches
Reduced operational costs
For growing businesses, speed matters. Accurate data annotation enables faster innovation and deployment.
Different industries require specialized annotation expertise:
Medical image annotation for diagnostics
Clinical data labeling for predictive analytics
Accurate tagging ensures reliable patient insights
Property image tagging
Location-based data categorization
AI-driven property recommendations
Product categorization and attribute tagging
AI-based recommendation engines
Visual recognition for beauty and skincare products
High-quality annotation helps businesses create intelligent, industry-specific AI solutions that improve efficiency and customer engagement.
In sectors like healthcare and finance, incorrect AI predictions can have serious consequences. High-quality data annotation:
Minimizes bias
Improves regulatory compliance
Strengthens AI reliability
This builds trust with customers and stakeholders—essential for sustainable business growth.
AI development requires significant investment. Poor data quality wastes resources and delays results.
Accurate and scalable data annotation services ensure that your AI models perform optimally, delivering:
Better decision-making insights
Increased automation
Higher operational efficiency
Long-term return on investment
With 11+ years of experience in data annotation and data tagging, Srishta Technology is a trusted partner for businesses aiming to leverage AI for growth.
We provide customized, high-quality data annotation services tailored to your industry needs. Srishta Technology is one of the top data annotation service providers in India, delivering high-quality, scalable, and cost-effective AI training data solutions. With over 11+ years of experience in data labeling, image annotation, video annotation, text annotation, and audio transcription, the company supports startups, enterprises, and AI research teams in building accurate and high-performing machine learning models. Backed by a skilled annotation workforce, strict quality control processes, and secure data handling standards, Srishta Technology ensures precision, consistency, and fast turnaround times — making it a trusted partner for AI and ML development across industries.
We specialize in:
Medical Data Annotation
Real Estate Data Tagging
Cosmetic Product Annotation
Our team understands domain-specific requirements, ensuring accurate and meaningful annotations.
We follow strict quality control processes, including multi-level review systems to ensure high precision and consistency.
Whether you need small datasets or large-scale AI training data, we offer flexible and scalable annotation solutions.
We prioritize secure data handling and compliance with industry standards to protect sensitive information.
By partnering with Srishta Technology, businesses can develop robust AI models, reduce risks and accelerate digital transformation.
Businesses that invest in professional data labeling and tagging services gain:
Smarter AI systems
Better customer insights
Improved automation
Competitive market advantage
High-quality data annotation is not an expense—it’s a growth catalyst.
As AI continues to shape the future of industries, the importance of high-quality data annotation cannot be overstated. It directly impacts model performance, operational efficiency, and overall business success.

With over 11 years of expertise, Srishta Technology empowers businesses with accurate, scalable, and industry-specific data annotation solutions.
If you are looking to enhance your AI initiatives and drive measurable business growth, partnering with Srishta Technology is your next strategic move.

In the era of Artificial Intelligence (AI) and machine learning (ML), data is the lifeblood of innovation. But raw data alone isn’t enough. To make AI and ML models intelligent, data needs to be organized, structured, and annotated. This is where data tagging comes into play—a critical process that enables machines to understand and act on data effectively.
Data tagging involves labeling data such as text, images, videos, or audio with meaningful tags so that machine learning algorithms can recognize patterns, make predictions, and deliver actionable insights. From healthcare diagnostics to real estate analysis and cosmetic product recommendations, accurate data tagging is transforming industries worldwide.
Improves Model Accuracy and Reliability
Machine learning models learn from labeled datasets. Incorrect or inconsistent tagging leads to misclassifications, inaccurate predictions, and unreliable AI outputs. Proper data tagging ensures that models are trained with precision, enhancing overall performance.
Reduces Training Time
High-quality, well-tagged datasets reduce the number of iterations needed for training. This accelerates model development, allowing businesses to deploy AI solutions faster and more efficiently.
Enables Advanced AI Functionalities
Data tagging is essential for sophisticated machine learning applications such as:
Computer Vision: Identifying objects, facial recognition, and medical imaging
Natural Language Processing (NLP): Sentiment analysis, chatbots, and text classification
Recommendation Engines: Personalized product suggestions in cosmetics and e-commerce
Supports Industry-Specific Solutions
Every sector has unique data requirements. Tagged data allows ML models to deliver context-specific insights:
Healthcare: Annotating medical records and images for accurate diagnosis
Real Estate: Categorizing property listings by location, type, and features
Cosmetics: Tagging products for AI-powered recommendation systems
Image Tagging: Labeling images with objects, boundaries, or features
Text Tagging: Categorizing text, labeling sentiment, or identifying key entities
Video Tagging: Annotating frames for motion detection or action recognition
Audio Tagging: Transcribing speech, labeling sound types, or detecting emotions
Proper tagging transforms unstructured data into structured insights, making it actionable for machine learning algorithms.
With over 5 years of expertise in data annotation and tagging, Srishta Technology is a trusted partner for businesses looking to train high-performing AI models. Our team provides end-to-end data tagging services tailored to your industry needs.
Why Srishta Technology is your ideal partner:
Industry Expertise: Specialized in healthcare, real estate, and cosmetic product data
High Accuracy Standards: Rigorous quality control ensures error-free annotations
Scalable Solutions: We handle projects of all sizes, from small datasets to large-scale operations
Data Security & Compliance: Your data is handled securely and confidentially at every step
Partnering with Srishta Technology enables businesses to develop robust ML models, improve AI decision-making, and gain a competitive advantage in their market.
Data tagging is more than a technical process—it is the foundation for accurate, reliable, and efficient machine learning models. Without precise tagging, even the most advanced AI systems cannot perform optimally.
By leveraging Srishta Technology’s 11+ years of experience, companies can ensure their AI and ML initiatives are powered by high-quality, industry-specific data. Whether it’s healthcare, real estate, or cosmetics, our data tagging solutions help businesses achieve faster insights, better predictions, and smarter AI systems.