Impact Stories- (Phase 2)

Karanjot Singh is from Ludhiana, where his father works as a carpenter and his mother is a homemaker. Growing up in a household where income depended on physical labour, Karanjot saw the value of hard work early. After completing his 12th, he joined a programming course in Ludhiana with zero computer knowledge; inspired by his father, who believed AI would shape the future.

His hard work and practice made him a topper of the course. The step-by-step curriculum introduced him to Python, MySQL, data science, machine learning, deep learning, NLP, and computer vision. What makes his journey stand out is how he translated these skills into real-world applications. He had been trading stocks for some time, but he struggled to analyze markets efficiently. Using the machine learning concepts from the program, he built a trade analysis AI model that predicts buying or selling signals from chart screenshots; directly applying machine learning to his own trading experience.

The employability skills sessions gave him the confidence to communicate professionally and apply his technical learnings as an entrepreneur. Karanjot did not wait for a job to define his future. He started an e-commerce shoe business, wrote an e-book on trading, and began teaching trading courses on weekends. Each of these ventures was built on the foundation of Python, machine learning, data analysis, and the confidence to share his knowledge with others.

At just 18, Karanjot is financially independent, supporting his family while pursuing a distance BCA degree. He earns enough to contribute to household expenses, a responsibility he takes seriously. He believes skills matter more than degrees and he has proven it. His father, a carpenter, saw the future in AI—his son is now showing him what that future looks like.

Pramod Kale, a Mumbai-based entrepreneur and student at NSTI Mumbai, did not enrol in the program looking for employment. He was already running his own business—an audit and election consultancy that required him to interact with clients, manage teams, and deliver results. What he needed was an upgrade: better communication, sharper presentation skills, and the ability to use technology to improve his services. While joining the program gave him practical experience with Python, SQL, and Power BI, it was the parallel Employability Skills sessions that provided a practical framework for elevating his existing business. The course pushed him to look at his professional interactions with a new level of awareness, focusing on personal branding, social awareness, and the nuances of communication.

The skills transferred directly to his business model. The training gave him the methodology to apply these enhanced soft skills directly to his audit and election business. Pramod is now leveraging his learnings to conduct more effective client meetings, build a stronger personal brand, and even explore a new venture: developing software for housing societies with friends in the tech sector. He is no longer just offering audit services—he is building a tech product that solves real problems for his community.

This comprehensive experience not only honed Pramod’s ability to blend technical know-how with professional conduct but also provided him with a refreshed approach to leadership. It has given him the confidence to handle hiring, manage training staff, and pitch new ideas, clearly showcasing his capacity to deliver practical, end-to-end solutions that solve genuine problems for his community and clients.

The security guard at NSTI Vidyanagar did not know Shyamala's name. He did not know she was a farmer's daughter or a mother of two. He simply told her about a course. That was enough.

Shyamala grew up in a middle-class farming family. She studied in a Telugu medium government school, earned a BSc in mathematics, and later completed a Bachelor of Education. For two years, she worked as a maths and computer teacher. Then life took a different turn. After marriage and children, financial struggles, health issues, and mental stress became a harsh reality. She wanted to work, but opportunities felt out of reach.

One afternoon, she walked into NSTI Vidyanagar looking for any job. A security guard mentioned an AI course running there. She applied on her own and was selected. The first week, nine-to-six classes felt impossible with two young children and a household to manage. She decided to adjust; waking earlier, preparing meals in advance, using every spare minute to catch up. Through role-plays and group activities, she learned how to introduce herself professionally, maintain eye contact, and respond to unexpected questions. Her classmates, many of whom were younger and had technical backgrounds, became her support system—they helped her debug code, explained concepts patiently, and encouraged her when she felt like giving up.

Before the course even ended, Shyamala secured a position as an AI Co-Trainer Assistant. She applied independently on LinkedIn, but the confidence and technical skills from the program gave her a competitive edge. She now supports university students in learning Python, SQL, and data science—the same concepts introduced to her just months earlier. Her students, many of whom come from similar backgrounds, see themselves in her. Her story is proof that a Telugu medium student, a mother with no prior coding experience, can master these skills and build a career.

In a robotics lab in Jaipur, Muktesh is learning to wire a flight controller. The work is new — circuits, calibration, the precise logic of stable flight. But the problems it will solve are old. He grew up watching his father farm in Bareilly, walking fields that either had too much water or too little, applying pesticide by instinct because there was no sensor to say otherwise.

He is a BSc Agriculture student, learning agronomy, crop cycles, hydroponics. The course explained soil composition and water stress and generally improved his farming skills, but not his technological skills. That bridge came from his elder sister, who had completed an AI program at NSTI Haldwani and told him it would fuse with what he already knew. He enrolled during his fourth year, when practicals left a window in his schedule. He had never used a computer before. The trainer started him on the power button, the desktop, the hardware inside the casing. From there, Python and machine learning, algorithms he trained on data, models that learned from input.

The pivot point was not a single class but a growing recognition: sensors could read soil moisture the way his fingers once did. A drone could carry water, pesticide, fertiliser to a specific patch instead of blanketing a whole field. To move from concept to prototype, he enrolled in a robotics program in Jaipur with an organisation called Robo Sithi, learning the hardware side of the drone-AI integration he had mapped in code.

Muktesh is now a student of both soil and servo motors, he plans to return to Bareilly with a working prototype. He wants to create solutions at the intersection of agriculture and technology, solving the real problems he has seen on the ground.

Rohini Hazra deep breathes when the frustration rises — a deep inhale, a slow exhale — a grounding exercise from the program's employability sessions that she once thought she'd never need. She had arrived at NSTI Howrah with a Master's in Geography and a short temper, convinced her biggest challenge would be Python. It wasn't.

She was from Howrah itself, where studying Geography had been her world. But after her Master's, the job opportunities felt narrow. Technology was everywhere, a low-hanging fruit she hadn't reached for. She decided to upskill, to fuse her knowledge of land and location with the tools of AI and data science. That fusion, geo-spatial problem-solving; was the vision she carried into the classroom.

The early days were disorienting. She had no technical background. Python, databases, Pandas every concept was new. The team project pushed her further: building a Learning Management System as a website, documenting student journeys through code. But it was the soft-skills sessions that reshaped her. She had struggled with anger — flashes that burned and vanished — and the trainers offered methods she hadn't expected: breathing techniques, focusing on positives, grounding herself in the moment. She practiced, and slowly noticed herself managing the anger that now didn't rise as often.

Rohini is aiming for data handling and data scientist roles. But her larger vision, inspired by her business family, is to start her own venture — one that uses technology to solve geo-spatial problems. The girl who once lost her temper now grounds herself before speaking and the geography student who once saw no path now maps her own.

Abhisudh had the idea before he had a team. It was a delivery platform, something like Porter, but faster, leaner, built around what people need in a hurry. He talked to friends. Some were interested. Others were hemmed in by their own pressures — family, time, the same invisible walls that keep good ideas from becoming real. After six months of trying to assemble a group, the pressure got to him. He took a job as an electrical design engineer. The salary was low. The work wasn't interesting and the idea kept circling in his head.

He had done a BTech in electrical and electronics engineering, but his interest had always been computer science. As a child, he'd take apart toys just to understand their wiring. Now he was a graduate with a startup idea, no team, and a father running a metal fabrication business. That's when he joined an Ai program he saw a notice for in the newspaper, and there he finally found what he'd been missing: like-minded people. They were younger than him, three or four years, but they shared a passion for building something from scratch. They started meeting twice a week, solving problems point by point. With AI tools, they pushed software projects forward faster than expected. But the project that most defines where he's headed is an AR prototype he's building himself: a tool that can take a simple photo of a gate or a roof and extract its dimensions.

He now has a team, a working partnership with his father's industry, and a quiet conviction that the idea he once carried alone is finally gaining shape. He plans to launch a pilot for his project in his local area once the team secures initial funding, testing the on-demand model with a small fleet of drivers. In the meantime, he's refining the AR prototype to pilot with his father's clients, turning a simple photo into a precise quote without anyone leaving their desk.

Vibha is from Gujarat, where her father works as an operator at a CNG gas line and her mother is a supervisor at a food company. She grew up in a household where education was valued, but financial stability was not always guaranteed. They worked hard to provide for Vibha and her younger brother. After completing 10th, she took a job as a lab technician. She worked there for a year, gaining experience and understanding the demands of a professional environment. But she realized that without higher education, she would not be able to progress in her career. She decided to pivot to IT by enrolling in the COPA trade at NSTI and after its completion, she learned about an AIPA program through her trainers. She saw it as an opportunity to deepen her skills in AI and machine learning—areas she knew would shape the future.

The program gave her a deeper understanding of AI and machine learning. She worked on a finance-based project using API keys, learning how to build and deploy AI models. She also improved her communication skills and confidence through the employability skills sessions. By the end of the program, she was no longer the shy student who had struggled to speak in front of others. She was someone who could stand on stage and present her work with conviction.

Vibha's hard work paid off. She applied to HCL and went through three rounds of selection. The technical round tested her knowledge, and the personal interviews gave her the opportunity to showcase her confidence and communication skills, gained through the program. She was selected as a Data Process Associate. Her parents were overjoyed. "They were excited that their daughter has been placed in one of India's top companies, " she recalls.

Yogesh thinks about the family function where he had arrived in mismatched colours — a red shirt, trousers that didn't match; and felt, that his clothes were speaking before he could. The gap wasn't just about dressing. It was about not knowing how to present himself, how to speak, how to walk into a room and belong.

He had come from Nainital, the son of a property dealer and a homemaker, with two older brothers already settled. In school, a short introduction to Java and HTML had sparked a curiosity about how machines learned. After his 12th, he searched for the most advanced AI program in Haldwani on Google Maps and found NSTI. The syllabus listed Python, machine learning, deep learning — all the technical skills he wanted. But it also offered something unexpected: employability skills, the very thing he knew he lacked. He couldn't speak in front of classmates without looking down. The confidence that came naturally to others was a locked door.

The ES sessions began with the basics: how to introduce yourself, how to structure thirty seconds of self-introduction so it didn't collapse into mumbling. Then came mock interviews, storytelling drills, grooming guidance. He learned to press his shirts, to match his belt with his shoes, to stand straight. The fear of speaking didn't vanish, but it became manageable — something he could push past. He discovered that communication wasn't a talent but a skill, and skills could be built.

He is now pursuing a B.Sc. alongside the program, aiming for a data analyst role. The technical skills are on his CV. But what has truly shifted is how he presents them — in interviews, in conversations, in the quiet confidence of knowing he belongs in the room. The mismatched wedding clothes are a memory now. He dresses for the role he wants, and speaks like someone who knows where he's headed.

Mohsin used to start his day at the gym down the road from his house in Srinagar — opening the shutters, keeping the register — before most people were awake. It was work he'd taken on young, after his father died and his elder brother, still a teenager himself, set aside his own studies to start earning. He finished a degree in public administration and Urdu in the gaps between gym shifts, without any clear sense of where it was leading.

A neighbour mentioned a new AI programming course at the local NSTI. Mohsin joined out of curiosity more than conviction. What kept him there were the way he learnt- he learnt through building things he wasn’t even aware about. He is, by his own cheerful admission, "not too good in English, ” and then, without noticing the irony, describes the voice assistant application he has since built, in English, from scratch. He began staying up most nights with his laptop, working through Python on his own time, and within the year had built several small applications, among them a voice assistant of his own design.

There were low points. "Not one moment — too many, " he says. Nights spent doing the arithmetic of his own life the way he'd once done it for the gym's ledgers: no job yet, a mother and brother to think of, and a creeping doubt about whether any of it; the Python, the late nights, this whole detour into machine learning, would come to anything. “I want to be an active contributor to my family, that is what

He wants to be a data scientist now, a word that meant nothing to him a year ago. Asked what he'll do with his first salary, he doesn't pause: it goes toward sending his mother and brother on Umrah.

Amina answered the question about her typing speed before the interviewer finished asking it. She spoke clearly, without looking down, without the long pause that used to swallow her sentences. The interview was for a data entry operator position at Quess — the first job she had ever applied for. She walked out knowing she had done her best. A week later, she was selected.

Behind that interview lay a quiet rebuilding. She was from Kupwara, Jammu and Kashmir. Her father died when she was very young. Her mother raised her and her brother alone. Then, a few years ago, her brother went missing. Amina had been preparing for her JEE exams, but grief broke her focus. She became shy, timid, and the sound of her own voice in a group felt foreign.

The program at NSTI arrived as a way back; a structured path. The deeper shift came through the employability sessions: how to dress, how to make a first impression, how to structure a thought for clarity and conciseness. She practiced speaking in front of the class. She participated in mock interviews and group discussions, learning to organize her words so they landed. She rebuilt her voice the way one rebuilds a habit — through daily, deliberate practice.

When the interview call came, she prepared her answers and walked in knowing what she wanted to say. Her mother, who had lost a husband and a son, saw her daughter stand on her own feet and wept with joy. Amina now works at Quess, building a career in tech. The girl who once could not speak in a group now answers questions before they finish. The silence that once defined her is now replaced with her own voice.

Senseni Khiangte is from Mizoram, where her father works as a laboratory technician in a government department and her parents are divorced. She comes from a family of five sisters—three elder sisters who are married, one pursuing FDT in Kolkata, and a younger sister studying law in Delhi. She grew up surrounded by strong women, but she wanted to create her own path.

After completing her 10th, Senseni made a bold decision. She dropped out and started working; first as a kindergarten teacher, then in a vehicle insurance office. Her father, who had always believed in her potential, learned about the AIMD program through a friend at the ITI in Mizoram. Senseni joined the program, leaving her hometown to study in Kolkata. The first few weeks were difficult. Returning to a classroom after nearly a decade was overwhelming. She struggled to adapt to the structured environment and the technical demands of the curriculum.

The program taught her Python, SQL, and AI fundamentals, functions, objectoriented programming, file handling, libraries, and MySQL databases. But it was the capstone project that brought everything together. She worked with a team to build an AI-powered airport assistance system—a project designed to make airport services easier and more accessible for passengers. They decided to create a solution that combined a user-friendly front-end interface with an intelligent chatbot. The project taught her about teamwork, project management, and the importance of user-centered design. Most importantly, it showed her that she could learn complex technologies and apply them to solve real problems.

Her journey is a reminder that it is never too late to return to education. She dropped out of school, worked for years, and then took a leap of faith. Today, she is continuing to build projects, learning new technologies, and preparing for a career she never imagined possible.

The first time Nikita shared an idea in a team meeting, the silence that followed wasn't the familiar one of her own throat closing. She had just suggested a new way to clean a messy dataset, and her voice had not broken. It was a small moment — a single sentence in a room full of data — but it marked the quiet end of a years-long silence.

She had grown up in Patna, a government officer's daughter, fascinated by computers but never imagining herself behind one. After 12th, she tried for NEET, failed, finished a general graduation, and found herself adrift. A friend told her about a governmentrecognised AI program at NSTI Patna

Nikita found the initial days challenging but rewarding. She received deep-dive training in Python, Excel, and Power BI. Learning how to manage large datasets and work with databases gave her a new sense of direction. She would freeze during conversations, second-guess her words, and avoid speaking up in groups. These feelings had followed her for years, affecting her confidence and her ability to connect with others. Through grounding exercises and meditation practices taught in the program, she learned to manage these feelings better. For the first time, she also got exposure to collaborative projects, which helped her understand teamwork and coordination. Working with peers from different backgrounds taught her how to share ideas, listen to feedback, and contribute meaningfully to a group effort.

Nikita's goal is to become a data engineer. She wants to keep improving her technical and communication skills and implement everything she has learned. She carries with herself, something deeper - the knowledge that she can overcome her fears, and that she belongs in the tech world she once felt excluded from.

Gauri still thinks about the time her neighbours back in Bihar were stunned by an electricity bill that arrived far higher than anyone expected. Consumption had spiked, and no one had tracked it. That memory surfaced months later when, midway through an ai program, she sat down to build a smart house electricity consumption dashboard: a tool that could track usage patterns, flag anomalies, and help a household manage its energy before the bill ever arrived.

She had come from a family that valued education deeply; both her parents worked in schools, so mathematics and discipline were part of the atmosphere at home. She had chosen data science because it felt like the natural extension of her love for math and physics. But when she enrolled in a BSc in Data Science at IIT Madras, the first semester arrived with code. The panic was immediate. She had no technical background, and the terminal felt like a locked door.

The programintroduced her to Python, SQL, and Power BI in its early phases. She built a hotel management dashboard first, then a smart bank transaction system with a team. But the electricity project was where everything converged. She merged Python and data science skills, applied machine learning concepts, and designed a dashboard that could visualise consumption patterns and predict spikes. It wasn't just an assignment; it was a direct response to the kind of real-world pressure her own community had faced.

Soon, she was presenting her work to senior professionals from Germany and Singapore as part of the program, answering questions about her approach. She is now looking for a data analyst internship. But the dashboard remains her clearest proof: a girl from a non-technical background, once scared of a terminal, building tools that might one day help a household see its energy future before the bill arrives.

Every morning, Vandhana stands in front of her classmates and introduces herself. Not just her name, not just her district. Something new each time — a story, a failure, a 100-metre race she lost by half a second. At first, her voice shook. She spoke to the floor. Now, she holds the room.

She is from Kollam, Kerala, the daughter of a distribution businessman and a school teacher. She chose Bio Science in her 12th but didn't follow it to a conventional degree. Instead, she enrolled in an ai training program at NSTI Trivandrum — a direct path to the kind of career she'd imagined when she first became curious about cybersecurity, robotics, and the systems that run the world behind screens. She had never touched a computer in any real way. The early days were disorienting: the command line, the logic, the vocabulary of a field she'd admired only from a distance. But the hardest part wasn't the code. It was the silence she retreated into whenever someone asked her a question.

The program required daily self-introductions. Not as a formality, but as a drill. Speak about yourself. Structure a thought in thirty seconds. Tell a story that makes people listen. Vandhana, who was shy, who had never worked in a team, found this harder than any technical module. She stumbled through the first weeks, voice thin, eyes down. But the repetition did what repetition does — it sanded away the fear. She began to notice how stories worked, how a pause landed, how confidence sounded. She started speaking not just about herself, but about her work, about a project, about what she was learning.

That skill now sits at the centre of her ambition. She wants to become a technical officer in the military — a role that demands both technological fluency and the ability to stand in front of a formation and command attention.

Sanjana grew up believing the path was simple: finish a BSc in IT, walk into a tech company, start building. But the line between a degree and a job turned out to be a wall. After graduation, she sent applications into the void and heard nothing. She tried LinkedIn, job portals, and every opening that matched her skills. The replies, when they came, were versions of the same sentence: you don't have experience. She had the degree; she didn't have the proof. The gap on her CV widened with every month that passed, and soon that itself became the reason she was rejected.

She took a BPO job to keep herself afloat. It paid, but it was never meant to last. The dream of IT receded behind a screen of daily targets and graveyard shifts, and the longer she stayed, the harder it became to imagine ever leaving.

Her family had just moved to Bengaluru, and while hunting for a rented room, they walked past a flyer for an artificial intelligence course. It wasn't a job, but it was possibly her final chance to build a career in the IT field. The training began with the absolute basics; motherboards, disk drives, before crawling into Python, databases and machine learning. She built a project: an energy efficiency dashboard that tracked factory machines, their consumption, their costs. When it was done, she had a working proof that she could analyse data and build something from scratch — the kind of evidence no recruiter had ever seen in her before.

She has since been hired as a data analyst intern. Her father, who is still managing a farm and a long court case back in Raichur, now proudly calls relatives to tell them his daughter has an IT job. The gap on her CV hasn't disappeared, but there's now a line after it and that's all she needed to start rewriting the rest.

Riya's smart water leakage chatbot doesn't guess. It asks the user a few questions — where the damp patch is, how long it's been there, whether the tap has been left on — and then it reasons. It understands the problem and suggests what to do next. She built it on Microsoft Copilot Studio, a tool she didn't know existed when she enrolled. The project was designed to reduce water wastage and encourage timely action. It was the first working system she had built from scratch, and it wasn't theory. It was code that responded to a real household issue.

She started with the basics; python, SQL, Power BI, and then generative AI and prompt engineering. The soft skills sessions rebuilt a confidence that had taken a hit in the years of false starts. The water leakage chatbot came together in the final phase, a capstone that demonstrated how AI could step into the mess of a household and offer practical help. Riya Gawad is from Pahalgarh, where her father runs a small vehicle rental business. After completing her schooling locally, she began a BSCIT degree but had to pause her studies due to medical reasons. Her interest in technology, particularly data science, began in 11th grade through coding, which she enjoyed for its future scope and intellectual challenge.

While exploring courses at her local NSTI, a teacher introduced her to the AIMD program. She applied, hoping to refresh and advance her technical knowledge. The curriculum went beyond her expectations of a basic introductory course. Riya’s goal is clear: to secure a job as a data scientist. The technical tools from the program have directly equipped her for such roles. She views this training as the vital step to re-enter the educational pathway she had left, now with stronger, more applicable skills for the future she always envisioned.

Samir was 34 when he decided that artificial intelligence was coming for his job, and the only way to survive was to learn how to build it himself. He had already done what no one in his family had: a B.Sc. in Physics, then seven years at Accenture processing health insurance claims, understanding how platforms and policies worked at scale. But he had no coding background. So, when a friend with exhausted eyes told him about a free Ai program, he became the first person to enrol that year.

The first weeks felt like stepping into a dark room. Python was a language he had never spoken. His trainer, kept repeating one thing: step by step, the way will open. Slowly, he moved from Python and databases into machine learning, where the real shift happened; he saw that a model could be trained on data, that intelligence wasn't magic but pattern recognition. Deep learning, NLP, computer vision followed.

His capstone project, FinX, took that understanding and gave it a shape. As team leader, he guided a group of five to build a generative AI platform for finance — a single website that would bring together services scattered across the market. Together, they built something the curriculum didn't require: fifteen web pages from scratch using HTML, CSS, Java, and customized datasets because public ones didn't fit their use case. They embedded a chatbot using Copilot Studio, designing eleven topic flows so a user could be guided from registration to portfolio management. When the backend crashed repeatedly, the team stayed up until 4 a.m. troubleshooting free APIs. Now Samir is building something larger — a startup that would train people like him, non-technical and underestimated, and show them that the fear of being left behind doesn't survive the first real project.

Ritesh still remembers the smell of the fields after a rain, back in Mandan, the village on the Rajasthan-Haryana border where he grew up. His father farmed and worked as a welder, and as soon as his classes ended, Ritesh was beside him; not because he had to be, but because he wanted to share the weight. He studied humanities until the end of 11th. Technology entered his life sideways, through an older brother in IT, and he found himself drawn to computers.

After 12th, he worked as an executive, separating and categorising orders. The job paid, but it made visible the technical gaps he carried. His family suggested COPA at NSTI Bengaluru, and when a faculty member mentioned an AIPA program, he joined that, hoping it would fill those gaps. His parents were hesitant, Bangalore was far, but his determination convinced them.

The program started with Python data types, then casting, dictionaries, loops, functions. SQL came next: inserting, updating, querying. Power BI taught him to analyse large datasets. But the turning point was a URL extraction project with his team. Different members brought different ideas, and Ritesh found himself not just coding but listening, communicating, managing conflicting perspectives. The employability sessions; storytelling, public speaking, growth mindset exercises — had given him the confidence to speak fluently, to present without fear. Before, he couldn't talk so easily. Now he could lead a discussion.

He wants to become a teacher. Not just any teacher, but someone who brings the tools and techniques he learned to people who, like his parents, never had access to education. He sees now that the same patience he learned helping his father weld — applies to code. He wants to pass that on.

Ashi's path into technology started unexpectedly at NSTI Noida, where she worked as a data entry operator. Her job was administrative—entering data, managing records, supporting the daily operations of the institute. Every day, she saw AIPA students arrive in the morning, laptops in hand, discussing code, collaborating on projects, and growing visibly more confident with each passing week. She overheard their conversations about Python, machine learning, and data science—terms that were unfamiliar but intriguing. Students building projects and moved into good jobs, she wanted this for herself.

The technical training gave her a foundation, helping her build projects, one she is especially proud of is a Bank Churn Prediction model, where she trained a model to analyze customer data and predict customer retention. She learned how to communicate professionally, maintain eye contact, groom appropriately, and present herself with confidence. These lessons went beyond the classroom— they helped her understand how to carry herself in any professional setting.

A turning point was the visit to the Microsoft office. For someone who had only observed tech careers from a distance, walking into a corporate workspace was eye-opening. The mentors helped her strengthen her resume and taught her to avoid filler words, speak clearly, and always be prepared. What stayed with her most was the workplace culture. Employees worked hard but carried themselves calmly, helping each other without any visible stress. They showed her how to structure her thoughts, how to tell her story in a way that employers would remember. For Ashi, it was a glimpse into how a good team operates—something she now wants to replicate in her own career. Ashi is preparing to build a career in tech, carrying with her a clear lesson: consistency and the right mindset make anything possible.

Every morning, Garima Srivastava leaves home early enough to catch the auto, then the metro, traveling nearly two hours each way to her internship at IIT Kanpur. She is a Data and Automation Intern, the first person in her family to step into the IT field. Two years ago, she did not know what Python was. She had grown up in Kanpur, studying Hindi medium from class nine onward, after a brief, difficult stretch in an English-medium ICSE school. There was no computer subject after class six, though she always felt drawn to technology. After her 12th, she enrolled in a BA in journalism on her father's insistence; he is a criminal lawyer and wanted to see her as a reporter. She dropped out within a year. A general BA followed, but she knew it was not building any real skill.

The path shifted when her father spotted a newspaper advertisement for an artificial intelligence course. She discovered machine learning, deep learning, Python, SQL; concepts she had never heard of before. But the deeper transformation came through the employability skills sessions. She had always hesitated to speak with strangers; English felt like a locked room. The program taught her to communicate, to groom herself, to walk into a room with her shoulders back. She learned that confidence is not a talent but something you practice, and she practiced it in mock interviews and team meetings. When she led her capstone project presentation, her trainer was visibly pleased.

That confidence carried her through the placement process. The IIT interview was not just about technical knowledge; the panel noticed her presence, her clarity. Now, three hours of daily travel do not deter her. The lawyer's daughter who once drifted through degrees has become an intern at one of India's premier institutes, convinced that she can build a career on her own terms.

Riya still opens the unfinished project on her laptop, the AI surveillance system that would use computer vision to read a CCTV feed and flag a crime before someone notices it's happening. It isn't complete yet. She keeps working on it. But the fact that she started it at all, that she could identify a problem in her surroundings and begin building a solution from scratch, is a shift she can trace back to a single project.

It was the AI logistics system, her capstone. Before a shipment left, it predicted weather and traffic along the route, rerouting goods to avoid delays. Before that project, she understood AI on a surface level. Algorithms existed; problems could be solved. But she had never felt, in her own hands, that she could be the one solving them. The logistics system changed that. With a working model deployed, she turned next to a malaria detection tool during her ML module. That one earned her the best performer award from Microsoft. Then the surveillance idea arrived: cameras everywhere, but they only record. What if they could alert?

She is from Bihar, and after a diploma in computer science, she joined a one-year AI program at NSTI Chennai, wanting a short, deep immersion rather than a long degree. The program's mentors constantly linked algorithms to real-world contexts. That practice rewired her: she stopped seeing problems as things to read about and started seeing them as things she could build for. She has applied for data analyst roles now, but alongside the job hunt, she is still working on that surveillance system in her own time. The capstone didn't just teach her to code a model. It taught her that a solution begins when someone stops waiting and starts building. She has three projects now and counting; the unfinished one is her next proof.

A neighbour in Lucknow stopped Anju to ask why her phone kept buzzing with interview calls. "Which course did you do? Is it expensive?" Anju shook her head and began to explain. The girls of her locality see her schedule and want to know what changed. What changed was a decision, but beneath it was a childhood rearranged by loss. Anju's mother passed away in 2011. The family split apart — her brothers moved away, her sister went to live with their aunt. Anju stayed with her grandmother in Kumaon, learning how to adjust, how to communicate, how to stand alone without breaking. That early fracture is why she now guides others. She knows what it means to have no one explaining the way.

When she joined the AI program at NSTI Jaipur, she had never touched a computer. The first lessons were painfully small: how to access a file, how to open Excel, how to copy-paste. But once the basics clicked, she began experimenting — Python scripts, machine learning models, and computer vision projects where she chased unique concepts and simple designs. For her capstone, she chose a problem from her own surroundings: a waste segregation website that identifies dustbins and maps recycling centres, making it easier for a community to dispose of waste responsibly. She sourced data, worked on the logic, and kept the design simple.

Presenting the project to classmates and trainers meant standing in front of a room and defending her choices; why this model, why this dataset, why this approach. Each presentation pushed her to structure her thoughts, anticipate questions, and speak with clarity. English had once made her shy; now she is the first to walk to the whiteboard. Leading project discussions taught her that leadership is about knowing the work deeply enough to guide others through it. She voices opinions without waiting to be called, because the capstone gave her proof that her ideas could hold up under scrutiny.

Ayesha Fatima needed a mentor when she was sixteen. She didn't have one. Her family said, "Do whatever you want, " but no one sat down to help her figure out what that was. She chose BCom because everyone else did. It didn't work out. She rebuilt her education from scratch — NIOS for 10th and 12th, then a BA in Psychology and somewhere in that rebuilding, she decided she wanted to become the mentor she never had.

In school, she was an introvert, afraid of judgment, certain that any new friend would bully her or mock her. She had no good friends then, and no one to tell her that the fear could be worked through. Psychology gave her an understanding for it — how the mind works, why people behave the way they do. When AI began to boom, she saw a pairing: human psychology and technical systems. A friend mentioned an AI program at NSTI, and Ayesha researched it before enrolling. The program gave her Python, machine learning, and AI fundamentals, she felt drawn to these topics mentioned as part of the curriculum.

But the deeper shift happened in employability sessions. Trainers used real-time examples, not slides. They taught her to control her expressions, to structure her thoughts, to speak without stammering. She still gets nervous in the first minutes of a conversation — she admits it openly; but she no longer freezes. Her background in psychology taught her how people think; the program sessions taught her how to reach them. The training gave her the tools to build and for the first time, she can see all three converging in a single career.

Palak's father didn't just solve maths problems with her. He sat beside her, evening after evening, and taught her to love the logic behind the numbers. She grew up in Hardoi, Uttar Pradesh, where he worked as a civil engineer. Mathematics became her first language, and she carried it through a BSc, graduating with a confidence in equations but no experience with code. A cousin who had studied at NSTI Haldwani told her about an artificial intelligence program. Palak had never written a line of Python, never queried a database, but she enrolled anyway, trusting the numbers in her head would find a new form.

The program started from the basics of Python basics, SQL queries, then natural language processing—the module that became her favourite. The most memorable part was creating a driver drowsiness detection system. Working on that project, she found a team, a rhythm, and a confidence she didn't have before. The fear of code, that persistent fear that she might not belong in a technical space; lifted slowly, replaced by the satisfaction of building something that worked.

When a tech trainer position opened, she applied. Now she teaches computer basics to children between class three and eight. The adjustment was steep: small children, short attention spans. She drew on everything the program had given her; not just the technical knowledge, but the patience of explaining a concept from scratch, the ability to read a room. She is planning to bring in Ai and Ml concepts into her lesson plans, breaking them down for young students the way her trainers once broke down code for her. The girl who once solved equations with her father now teaches children how to click, type, and think.

Vanita spent her first weeks at NSTI Jodhpur as a data entry operator, maintaining student records. The work was simple enough, but it placed her close enough to see the AI classes forming around her. When a faculty member asked if she wanted to fill out the form, she didn't hesitate. She had already missed the REET cutoff by two or three marks after her BA. The apprenticeship was supposed to be a stopgap. It became a doorway instead.

She had always loved cooking. In school, she chose arts with home science, English literature, and sociology, and changed schools just to keep home science on her timetable. Cooking interested her, but when she thought of hotel management, her parents said there was no scope in it. So, she prepared for a government teacher's exam, came close, and lost by a narrow margin. The AI program offered something different: a new skill set that wasn't tied to one exam or one seat.

The course started with Word, PowerPoint, Excel — the tools she used daily. Then Python, data analysis, and the quiet discipline of working with datasets. Her capstone team built a finance analysis project, comparing salaries across gender and department using Excel, Power BI, and Python. It wasn't glamorous, but it was real: a question about equity, answered with numbers. Presenting it in front of teachers, NSTI staff, and friends stretched her past the stage fear she'd carried for years.

Manners, office etiquette, and communication improved alongside the technical work. Vanita is now looking for data entry and data analysis roles. The girl who once changed schools for a home science class, and who missed an exam by two or three marks, is no longer waiting for a single result to define her.

The extra hours began unexpectedly. After class, when the room cleared, Shejal would stay back, open the LMS portal, and practice. She had arrived at NSTI Allahabad with zero computer knowledge, not the basics, not the shortcuts, not the confidence to click without freezing. The first weeks were spent simply catching up: mouse, keyboard, interface. Then the practice hours became a routine.

She was from Kunda Pratapgarh, the district topper in school, a student who loved English and maths before choosing biology in 11th and 12th. Medicine was the dream, but money had other plans. Her father drives an auto in Mumbai; her mother a homemaker takes care of her four sisters; Shejal was the first to step toward technology. An uncle told her about the AI course, and the promise of employability skills sealed her decision.

The fear didn't vanish overnight. It faded through repetition; daily LMS drills, extra hours, constant practice. The curriculum introduced Microsoft tools, SQL, data science, then ML, DL, computer vision. She began to like the work. Not just the completion, but the wrestling with logic — why the error appeared, what the code was trying to do, where her thinking had gone wrong. Real-time projects brought deadlines, and with deadlines came the panic she now remembers fondly: the last-minute error surfacing before submission, the scramble to trace it, the team pushing through together. She called it thrilling, the way learning became fun when it arrived with adrenaline.

Now she is applying for jobs with a different presence, able to express ideas clearly and work through problems without shrinking. She chases technical growth the same way she once chased marks; steadily, and with the confidence of someone who has already caught up once.