Meet Pranjali Awasthi: The teen who built a Rs 100 crore AI startup in 1 year
Born in India and later moving to Florida, Pranjali Awasthi ’s early life sits in that familiar migration pattern of families shifting for work and study, yet her path diverges quickly from anything ordinary. While most children are still moving between school routines and hobbies, she was already spending time around code, guided by a father with a background in engineering who treated computers less like gadgets and more like tools to be understood. There is a tendency in startup storytelling to smooth these beginnings into something neatly linear, but here the timeline feels more fragmented.
Early curiosity, a few structured lessons, then a steady drift into increasingly technical spaces that most teenagers never encounter at all. By the time she was still in early adolescence, she was already being pulled into environments shaped by research and experimentation rather than classrooms alone.
Pranjali Awasthi’s early childhood coding to Florida International University as an intern
Coding entered her life at around 7 years old, not as a hobby picked up from peers, but as something introduced at home. The environment mattered as much as the instruction itself. Instead of treating programming as an abstract skill, it was framed as something workable, almost practical, in the same way one might learn to build or repair things.
Her LinkedIn profile reveals that, at 13, she was already with Florida International University as a Research Intern, engaging with machine learning work that placed her alongside students and academics significantly older.
The period spent in university research labs did not resemble a traditional internship narrative. It was less about defined responsibilities and more about exposure to the mechanics of machine learning work as it is actually carried out. Data sets, formatting issues, and time lost in cleaning information rather than analysing it.
Inside Delv.AI: Built for extracting meaning from scattered research data
Delv.AI emerged in January 2022, built around a problem that sits quietly inside academic and research work: information is abundant, but rarely organised in a way that allows quick extraction. Researchers often spend more time navigating documents than engaging with their actual content.
The platform she built focuses on reducing that friction. It allows users to query across multiple documents, connect to cloud storage, and export structured outputs in formats like CSV. At its core, the system is aimed at making large bodies of text less cumbersome to handle, particularly for those working with PDFs and written datasets.
It is not positioned as a broad consumer tool. The emphasis remains on research workflows, where the issue is not access to data but the time lost moving through it. Even within that narrow focus, the work reflects a broader shift in how machine learning tools are being applied: less about novelty, more about removing repetitive labour from information-heavy tasks.
A small team and early investment attention
As reported by Business Insider, the company operates with a relatively small team based in Miami. In its early stages, it attracted funding from investors amounting to roughly $450,000 (approx. 4.2 crore). For an early-stage AI-focused startup, this level of backing places it within a crowded but still attentive segment of the market.
By late 2023, valuation estimates placed the company at around $12 million (approx 113 crore). The scale of the team, reportedly under ten people, also suggests a fairly tight operational structure. Work at that size usually involves overlapping responsibilities rather than clearly separated roles, especially in technical development environments.
Early curiosity, a few structured lessons, then a steady drift into increasingly technical spaces that most teenagers never encounter at all. By the time she was still in early adolescence, she was already being pulled into environments shaped by research and experimentation rather than classrooms alone.
Pranjali Awasthi’s early childhood coding to Florida International University as an intern
Coding entered her life at around 7 years old, not as a hobby picked up from peers, but as something introduced at home. The environment mattered as much as the instruction itself. Instead of treating programming as an abstract skill, it was framed as something workable, almost practical, in the same way one might learn to build or repair things.
Her LinkedIn profile reveals that, at 13, she was already with Florida International University as a Research Intern, engaging with machine learning work that placed her alongside students and academics significantly older.
The period spent in university research labs did not resemble a traditional internship narrative. It was less about defined responsibilities and more about exposure to the mechanics of machine learning work as it is actually carried out. Data sets, formatting issues, and time lost in cleaning information rather than analysing it.
Inside Delv.AI: Built for extracting meaning from scattered research data
Delv.AI emerged in January 2022, built around a problem that sits quietly inside academic and research work: information is abundant, but rarely organised in a way that allows quick extraction. Researchers often spend more time navigating documents than engaging with their actual content.
The platform she built focuses on reducing that friction. It allows users to query across multiple documents, connect to cloud storage, and export structured outputs in formats like CSV. At its core, the system is aimed at making large bodies of text less cumbersome to handle, particularly for those working with PDFs and written datasets.
It is not positioned as a broad consumer tool. The emphasis remains on research workflows, where the issue is not access to data but the time lost moving through it. Even within that narrow focus, the work reflects a broader shift in how machine learning tools are being applied: less about novelty, more about removing repetitive labour from information-heavy tasks.
A small team and early investment attention
As reported by Business Insider, the company operates with a relatively small team based in Miami. In its early stages, it attracted funding from investors amounting to roughly $450,000 (approx. 4.2 crore). For an early-stage AI-focused startup, this level of backing places it within a crowded but still attentive segment of the market.
By late 2023, valuation estimates placed the company at around $12 million (approx 113 crore). The scale of the team, reportedly under ten people, also suggests a fairly tight operational structure. Work at that size usually involves overlapping responsibilities rather than clearly separated roles, especially in technical development environments.
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