BitcoinWorld AI Notetaker Turbo AI: College Dropouts’ Phenomenal Journey to 5 Million Users In a world increasingly shaped by digital innovation and decentralized technologies, the story of Turbo AI stands out as a testament to rapid innovation and entrepreneurial spirit. While our focus at Bitcoin World often gravitates towards blockchain and cryptocurrency, it’s crucial to acknowledge groundbreaking developments in parallel tech sectors, especially those demonstrating explosive startup growth. This is precisely the narrative of Rudy Arora and Sarthak Dhawan, two 20-year-old college dropouts who transformed a classroom frustration into a global phenomenon: an AI notetaker now serving five million users. Turbo AI: Redefining the AI Notetaker Experience Imagine never missing a crucial point in a lecture or meeting again. That’s the promise delivered by Turbo AI. Launched in early 2024, this innovative platform addresses a common problem: the struggle to simultaneously listen and take effective notes. CEO Sarthak Dhawan vividly recalls his personal challenge: “I would always struggle with taking notes because I just couldn’t both listen to the teacher and write at the same time.” This personal pain point became the catalyst for an extraordinary solution. The initial concept, Turbolearn, allowed users to record lectures and automatically generate comprehensive notes, flashcards, and quizzes. What began as a side project quickly transcended its academic origins, evolving into a sophisticated AI notetaker that integrates recording, transcription, summarization, and interactive study tools, including a built-in chat assistant for clarifying concepts. From Classroom Problem to Global Solution: The College Dropouts’ Journey Rudy Arora and Sarthak Dhawan, friends since middle school, embarked on this venture while enrolled at Duke and Northwestern universities. Their decision to become college dropouts wasn’t impulsive; it was a calculated move driven by the undeniable traction of their creation. Initially shared among friends, Turbolearn rapidly spread across prestigious campuses like Harvard and MIT. This organic adoption underscored the universal need for such a tool. While live lecture recordings were an initial use case, the founders ingeniously adapted to user feedback, allowing students to upload PDFs, YouTube videos, or readings. This flexibility proved crucial, making the platform invaluable even when audio quality was a concern. Their journey exemplifies how identifying a core problem and applying innovative technology can lead to widespread impact, even for those who choose an unconventional path. Explosive Startup Growth: Numbers That Speak Volumes The numbers behind Turbo AI‘s success are compelling. Within months, the platform grew from one million to five million users, demonstrating remarkable startup growth. It boasts an eight-figure annual recurring revenue and welcomes approximately 20,000 new users daily, all while remaining profitable. This rapid expansion is not merely about user acquisition; it’s about engagement. Dhawan notes, “Students will upload a 30-page lecture and spend two hours going through 75 quiz questions in a row. You don’t do that unless it’s really working.” This high level of interaction highlights the product’s efficacy in saving time and enhancing information retention. Unlike many high-growth AI companies, Arora and Dhawan have maintained a cautious approach to funding, having raised only $750,000 last year. Their profitability allows them to be deliberate, focusing on sustainable growth and user-centric development. Beyond Academics: Turbo AI as an Essential AI Study Tool and Professional Assistant The evolution from “Turbolearn” to “Turbo AI” signifies a strategic expansion beyond its initial academic focus. While it remains an unparalleled AI study tool for students, its utility has broadened significantly. Professionals across various fields—consultants, lawyers, doctors, and even analysts at top-tier firms like Goldman Sachs and McKinsey—have adopted the platform. They leverage Turbo AI to summarize lengthy reports, convert documents into podcasts for commute listening, and streamline their information processing. This versatility demonstrates the universal appeal of intelligent automation in knowledge work. The founders’ foresight in broadening the product’s scope has unlocked new markets and solidified Turbo AI’s position as a versatile assistant for anyone dealing with large volumes of information. Navigating the Future: Innovation, Pricing, and Competitive Edge Operating with a lean, 15-person team based in Los Angeles, Arora and Dhawan are committed to staying connected with their user base, particularly student and creator communities. Their pricing strategy, currently around $20 a month, is under active experimentation to accommodate the price sensitivity of students while scaling to a broader professional audience. “Right now, we’re experimenting with other pricing and running a lot of A/B tests to see what works,” explains Arora. This adaptive approach is crucial in a competitive landscape featuring other AI note-takers. However, Turbo AI differentiates itself by offering a unique balance between full automation and user control. Users can choose to let the AI take notes entirely or write alongside it, providing a flexible and powerful experience. As Dhawan proudly states, “What’s cool now is that when students think of an AI notetaker or AI study tool, we’re the first ones that come to mind.” This brand recognition, combined with continuous innovation, positions Turbo AI for continued success. Conclusion: A Blueprint for Modern Entrepreneurship The story of Rudy Arora and Sarthak Dhawan, the visionaries behind Turbo AI, is a powerful narrative of modern entrepreneurship. Their journey from college dropouts with a classroom problem to leading a multi-million-user platform illustrates the immense potential of AI-driven solutions and focused execution. They’ve not only built a highly effective AI notetaker but also cultivated a profitable business with sustained startup growth, proving that deep understanding of user needs, combined with innovative technology, can lead to remarkable success. Turbo AI is more than just a tool; it’s a testament to how young innovators can leverage artificial intelligence to solve real-world problems and create lasting value. To learn more about the latest AI market trends, explore our article on key developments shaping AI features. This post AI Notetaker Turbo AI: College Dropouts’ Phenomenal Journey to 5 Million Users first appeared on BitcoinWorld.BitcoinWorld AI Notetaker Turbo AI: College Dropouts’ Phenomenal Journey to 5 Million Users In a world increasingly shaped by digital innovation and decentralized technologies, the story of Turbo AI stands out as a testament to rapid innovation and entrepreneurial spirit. While our focus at Bitcoin World often gravitates towards blockchain and cryptocurrency, it’s crucial to acknowledge groundbreaking developments in parallel tech sectors, especially those demonstrating explosive startup growth. This is precisely the narrative of Rudy Arora and Sarthak Dhawan, two 20-year-old college dropouts who transformed a classroom frustration into a global phenomenon: an AI notetaker now serving five million users. Turbo AI: Redefining the AI Notetaker Experience Imagine never missing a crucial point in a lecture or meeting again. That’s the promise delivered by Turbo AI. Launched in early 2024, this innovative platform addresses a common problem: the struggle to simultaneously listen and take effective notes. CEO Sarthak Dhawan vividly recalls his personal challenge: “I would always struggle with taking notes because I just couldn’t both listen to the teacher and write at the same time.” This personal pain point became the catalyst for an extraordinary solution. The initial concept, Turbolearn, allowed users to record lectures and automatically generate comprehensive notes, flashcards, and quizzes. What began as a side project quickly transcended its academic origins, evolving into a sophisticated AI notetaker that integrates recording, transcription, summarization, and interactive study tools, including a built-in chat assistant for clarifying concepts. From Classroom Problem to Global Solution: The College Dropouts’ Journey Rudy Arora and Sarthak Dhawan, friends since middle school, embarked on this venture while enrolled at Duke and Northwestern universities. Their decision to become college dropouts wasn’t impulsive; it was a calculated move driven by the undeniable traction of their creation. Initially shared among friends, Turbolearn rapidly spread across prestigious campuses like Harvard and MIT. This organic adoption underscored the universal need for such a tool. While live lecture recordings were an initial use case, the founders ingeniously adapted to user feedback, allowing students to upload PDFs, YouTube videos, or readings. This flexibility proved crucial, making the platform invaluable even when audio quality was a concern. Their journey exemplifies how identifying a core problem and applying innovative technology can lead to widespread impact, even for those who choose an unconventional path. Explosive Startup Growth: Numbers That Speak Volumes The numbers behind Turbo AI‘s success are compelling. Within months, the platform grew from one million to five million users, demonstrating remarkable startup growth. It boasts an eight-figure annual recurring revenue and welcomes approximately 20,000 new users daily, all while remaining profitable. This rapid expansion is not merely about user acquisition; it’s about engagement. Dhawan notes, “Students will upload a 30-page lecture and spend two hours going through 75 quiz questions in a row. You don’t do that unless it’s really working.” This high level of interaction highlights the product’s efficacy in saving time and enhancing information retention. Unlike many high-growth AI companies, Arora and Dhawan have maintained a cautious approach to funding, having raised only $750,000 last year. Their profitability allows them to be deliberate, focusing on sustainable growth and user-centric development. Beyond Academics: Turbo AI as an Essential AI Study Tool and Professional Assistant The evolution from “Turbolearn” to “Turbo AI” signifies a strategic expansion beyond its initial academic focus. While it remains an unparalleled AI study tool for students, its utility has broadened significantly. Professionals across various fields—consultants, lawyers, doctors, and even analysts at top-tier firms like Goldman Sachs and McKinsey—have adopted the platform. They leverage Turbo AI to summarize lengthy reports, convert documents into podcasts for commute listening, and streamline their information processing. This versatility demonstrates the universal appeal of intelligent automation in knowledge work. The founders’ foresight in broadening the product’s scope has unlocked new markets and solidified Turbo AI’s position as a versatile assistant for anyone dealing with large volumes of information. Navigating the Future: Innovation, Pricing, and Competitive Edge Operating with a lean, 15-person team based in Los Angeles, Arora and Dhawan are committed to staying connected with their user base, particularly student and creator communities. Their pricing strategy, currently around $20 a month, is under active experimentation to accommodate the price sensitivity of students while scaling to a broader professional audience. “Right now, we’re experimenting with other pricing and running a lot of A/B tests to see what works,” explains Arora. This adaptive approach is crucial in a competitive landscape featuring other AI note-takers. However, Turbo AI differentiates itself by offering a unique balance between full automation and user control. Users can choose to let the AI take notes entirely or write alongside it, providing a flexible and powerful experience. As Dhawan proudly states, “What’s cool now is that when students think of an AI notetaker or AI study tool, we’re the first ones that come to mind.” This brand recognition, combined with continuous innovation, positions Turbo AI for continued success. Conclusion: A Blueprint for Modern Entrepreneurship The story of Rudy Arora and Sarthak Dhawan, the visionaries behind Turbo AI, is a powerful narrative of modern entrepreneurship. Their journey from college dropouts with a classroom problem to leading a multi-million-user platform illustrates the immense potential of AI-driven solutions and focused execution. They’ve not only built a highly effective AI notetaker but also cultivated a profitable business with sustained startup growth, proving that deep understanding of user needs, combined with innovative technology, can lead to remarkable success. Turbo AI is more than just a tool; it’s a testament to how young innovators can leverage artificial intelligence to solve real-world problems and create lasting value. To learn more about the latest AI market trends, explore our article on key developments shaping AI features. This post AI Notetaker Turbo AI: College Dropouts’ Phenomenal Journey to 5 Million Users first appeared on BitcoinWorld.
AI Notetaker Turbo AI: College Dropouts’ Phenomenal Journey to 5 Million Users
For feedback or concerns regarding this content, please contact us at crypto.news@mexc.com
BitcoinWorld
AI Notetaker Turbo AI: College Dropouts’ Phenomenal Journey to 5 Million Users
In a world increasingly shaped by digital innovation and decentralized technologies, the story of Turbo AI stands out as a testament to rapid innovation and entrepreneurial spirit. While our focus at Bitcoin World often gravitates towards blockchain and cryptocurrency, it’s crucial to acknowledge groundbreaking developments in parallel tech sectors, especially those demonstrating explosive startup growth. This is precisely the narrative of Rudy Arora and Sarthak Dhawan, two 20-year-old college dropouts who transformed a classroom frustration into a global phenomenon: an AI notetaker now serving five million users.
Turbo AI: Redefining the AI Notetaker Experience
Imagine never missing a crucial point in a lecture or meeting again. That’s the promise delivered by Turbo AI. Launched in early 2024, this innovative platform addresses a common problem: the struggle to simultaneously listen and take effective notes. CEO Sarthak Dhawan vividly recalls his personal challenge: “I would always struggle with taking notes because I just couldn’t both listen to the teacher and write at the same time.” This personal pain point became the catalyst for an extraordinary solution. The initial concept, Turbolearn, allowed users to record lectures and automatically generate comprehensive notes, flashcards, and quizzes. What began as a side project quickly transcended its academic origins, evolving into a sophisticated AI notetaker that integrates recording, transcription, summarization, and interactive study tools, including a built-in chat assistant for clarifying concepts.
From Classroom Problem to Global Solution: The College Dropouts’ Journey
Rudy Arora and Sarthak Dhawan, friends since middle school, embarked on this venture while enrolled at Duke and Northwestern universities. Their decision to become college dropouts wasn’t impulsive; it was a calculated move driven by the undeniable traction of their creation. Initially shared among friends, Turbolearn rapidly spread across prestigious campuses like Harvard and MIT. This organic adoption underscored the universal need for such a tool. While live lecture recordings were an initial use case, the founders ingeniously adapted to user feedback, allowing students to upload PDFs, YouTube videos, or readings. This flexibility proved crucial, making the platform invaluable even when audio quality was a concern. Their journey exemplifies how identifying a core problem and applying innovative technology can lead to widespread impact, even for those who choose an unconventional path.
Explosive Startup Growth: Numbers That Speak Volumes
The numbers behind Turbo AI‘s success are compelling. Within months, the platform grew from one million to five million users, demonstrating remarkable startup growth. It boasts an eight-figure annual recurring revenue and welcomes approximately 20,000 new users daily, all while remaining profitable. This rapid expansion is not merely about user acquisition; it’s about engagement. Dhawan notes, “Students will upload a 30-page lecture and spend two hours going through 75 quiz questions in a row. You don’t do that unless it’s really working.” This high level of interaction highlights the product’s efficacy in saving time and enhancing information retention. Unlike many high-growth AI companies, Arora and Dhawan have maintained a cautious approach to funding, having raised only $750,000 last year. Their profitability allows them to be deliberate, focusing on sustainable growth and user-centric development.
Beyond Academics: Turbo AI as an Essential AI Study Tool and Professional Assistant
The evolution from “Turbolearn” to “Turbo AI” signifies a strategic expansion beyond its initial academic focus. While it remains an unparalleled AI study tool for students, its utility has broadened significantly. Professionals across various fields—consultants, lawyers, doctors, and even analysts at top-tier firms like Goldman Sachs and McKinsey—have adopted the platform. They leverage Turbo AI to summarize lengthy reports, convert documents into podcasts for commute listening, and streamline their information processing. This versatility demonstrates the universal appeal of intelligent automation in knowledge work. The founders’ foresight in broadening the product’s scope has unlocked new markets and solidified Turbo AI’s position as a versatile assistant for anyone dealing with large volumes of information.
Navigating the Future: Innovation, Pricing, and Competitive Edge
Operating with a lean, 15-person team based in Los Angeles, Arora and Dhawan are committed to staying connected with their user base, particularly student and creator communities. Their pricing strategy, currently around $20 a month, is under active experimentation to accommodate the price sensitivity of students while scaling to a broader professional audience. “Right now, we’re experimenting with other pricing and running a lot of A/B tests to see what works,” explains Arora. This adaptive approach is crucial in a competitive landscape featuring other AI note-takers. However, Turbo AI differentiates itself by offering a unique balance between full automation and user control. Users can choose to let the AI take notes entirely or write alongside it, providing a flexible and powerful experience. As Dhawan proudly states, “What’s cool now is that when students think of an AI notetaker or AI study tool, we’re the first ones that come to mind.” This brand recognition, combined with continuous innovation, positions Turbo AI for continued success.
Conclusion: A Blueprint for Modern Entrepreneurship
The story of Rudy Arora and Sarthak Dhawan, the visionaries behind Turbo AI, is a powerful narrative of modern entrepreneurship. Their journey from college dropouts with a classroom problem to leading a multi-million-user platform illustrates the immense potential of AI-driven solutions and focused execution. They’ve not only built a highly effective AI notetaker but also cultivated a profitable business with sustained startup growth, proving that deep understanding of user needs, combined with innovative technology, can lead to remarkable success. Turbo AI is more than just a tool; it’s a testament to how young innovators can leverage artificial intelligence to solve real-world problems and create lasting value.
To learn more about the latest AI market trends, explore our article on key developments shaping AI features.
This post AI Notetaker Turbo AI: College Dropouts’ Phenomenal Journey to 5 Million Users first appeared on BitcoinWorld.
Disclaimer: The articles reposted on this site are sourced from public platforms and are provided for informational purposes only. They do not necessarily reflect the views of MEXC. All rights remain with the original authors. If you believe any content infringes on third-party rights, please contact crypto.news@mexc.com for removal. MEXC makes no guarantees regarding the accuracy, completeness, or timeliness of the content and is not responsible for any actions taken based on the information provided. The content does not constitute financial, legal, or other professional advice, nor should it be considered a recommendation or endorsement by MEXC.
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Summarize Any Stock’s Earnings Call in Seconds Using FMP API
Turn lengthy earnings call transcripts into one-page insights using the Financial Modeling Prep APIPhoto by Bich Tran
Earnings calls are packed with insights. They tell you how a company performed, what management expects in the future, and what analysts are worried about. The challenge is that these transcripts often stretch across dozens of pages, making it tough to separate the key takeaways from the noise.
With the right tools, you don’t need to spend hours reading every line. By combining the Financial Modeling Prep (FMP) API with Groq’s lightning-fast LLMs, you can transform any earnings call into a concise summary in seconds. The FMP API provides reliable access to complete transcripts, while Groq handles the heavy lifting of distilling them into clear, actionable highlights.
In this article, we’ll build a Python workflow that brings these two together. You’ll see how to fetch transcripts for any stock, prepare the text, and instantly generate a one-page summary. Whether you’re tracking Apple, NVIDIA, or your favorite growth stock, the process works the same — fast, accurate, and ready whenever you are.
Fetching Earnings Transcripts with FMP API
The first step is to pull the raw transcript data. FMP makes this simple with dedicated endpoints for earnings calls. If you want the latest transcripts across the market, you can use the stable endpoint /stable/earning-call-transcript-latest. For a specific stock, the v3 endpoint lets you request transcripts by symbol, quarter, and year using the pattern:
https://financialmodelingprep.com/api/v3/earning_call_transcript/{symbol}?quarter={q}&year={y}&apikey=YOUR_API_KEY
here’s how you can fetch NVIDIA’s transcript for a given quarter:
import requestsAPI_KEY = "your_api_key"symbol = "NVDA"quarter = 2year = 2024url = f"https://financialmodelingprep.com/api/v3/earning_call_transcript/{symbol}?quarter={quarter}&year={year}&apikey={API_KEY}"response = requests.get(url)data = response.json()# Inspect the keysprint(data.keys())# Access transcript contentif "content" in data[0]: transcript_text = data[0]["content"] print(transcript_text[:500]) # preview first 500 characters
The response typically includes details like the company symbol, quarter, year, and the full transcript text. If you aren’t sure which quarter to query, the “latest transcripts” endpoint is the quickest way to always stay up to date.
Cleaning and Preparing Transcript Data
Raw transcripts from the API often include long paragraphs, speaker tags, and formatting artifacts. Before sending them to an LLM, it helps to organize the text into a cleaner structure. Most transcripts follow a pattern: prepared remarks from executives first, followed by a Q&A session with analysts. Separating these sections gives better control when prompting the model.
In Python, you can parse the transcript and strip out unnecessary characters. A simple way is to split by markers such as “Operator” or “Question-and-Answer.” Once separated, you can create two blocks — Prepared Remarks and Q&A — that will later be summarized independently. This ensures the model handles each section within context and avoids missing important details.
Here’s a small example of how you might start preparing the data:
import re# Example: using the transcript_text we fetched earliertext = transcript_text# Remove extra spaces and line breaksclean_text = re.sub(r'\s+', ' ', text).strip()# Split sections (this is a heuristic; real-world transcripts vary slightly)if "Question-and-Answer" in clean_text: prepared, qna = clean_text.split("Question-and-Answer", 1)else: prepared, qna = clean_text, ""print("Prepared Remarks Preview:\n", prepared[:500])print("\nQ&A Preview:\n", qna[:500])
With the transcript cleaned and divided, you’re ready to feed it into Groq’s LLM. Chunking may be necessary if the text is very long. A good approach is to break it into segments of a few thousand tokens, summarize each part, and then merge the summaries in a final pass.
Summarizing with Groq LLM
Now that the transcript is clean and split into Prepared Remarks and Q&A, we’ll use Groq to generate a crisp one-pager. The idea is simple: summarize each section separately (for focus and accuracy), then synthesize a final brief.
Prompt design (concise and factual)
Use a short, repeatable template that pushes for neutral, investor-ready language:
You are an equity research analyst. Summarize the following earnings call sectionfor {symbol} ({quarter} {year}). Be factual and concise.Return:1) TL;DR (3–5 bullets)2) Results vs. guidance (what improved/worsened)3) Forward outlook (specific statements)4) Risks / watch-outs5) Q&A takeaways (if present)Text:<<<{section_text}>>>
Python: calling Groq and getting a clean summary
Groq provides an OpenAI-compatible API. Set your GROQ_API_KEY and pick a fast, high-quality model (e.g., a Llama-3.1 70B variant). We’ll write a helper to summarize any text block, then run it for both sections and merge.
import osimport textwrapimport requestsGROQ_API_KEY = os.environ.get("GROQ_API_KEY") or "your_groq_api_key"GROQ_BASE_URL = "https://api.groq.com/openai/v1" # OpenAI-compatibleMODEL = "llama-3.1-70b" # choose your preferred Groq modeldef call_groq(prompt, temperature=0.2, max_tokens=1200): url = f"{GROQ_BASE_URL}/chat/completions" headers = { "Authorization": f"Bearer {GROQ_API_KEY}", "Content-Type": "application/json", } payload = { "model": MODEL, "messages": [ {"role": "system", "content": "You are a precise, neutral equity research analyst."}, {"role": "user", "content": prompt}, ], "temperature": temperature, "max_tokens": max_tokens, } r = requests.post(url, headers=headers, json=payload, timeout=60) r.raise_for_status() return r.json()["choices"][0]["message"]["content"].strip()def build_prompt(section_text, symbol, quarter, year): template = """ You are an equity research analyst. Summarize the following earnings call section for {symbol} ({quarter} {year}). Be factual and concise. Return: 1) TL;DR (3–5 bullets) 2) Results vs. guidance (what improved/worsened) 3) Forward outlook (specific statements) 4) Risks / watch-outs 5) Q&A takeaways (if present) Text: <<< {section_text} >>> """ return textwrap.dedent(template).format( symbol=symbol, quarter=quarter, year=year, section_text=section_text )def summarize_section(section_text, symbol="NVDA", quarter="Q2", year="2024"): if not section_text or section_text.strip() == "": return "(No content found for this section.)" prompt = build_prompt(section_text, symbol, quarter, year) return call_groq(prompt)# Example usage with the cleaned splits from Section 3prepared_summary = summarize_section(prepared, symbol="NVDA", quarter="Q2", year="2024")qna_summary = summarize_section(qna, symbol="NVDA", quarter="Q2", year="2024")final_one_pager = f"""# {symbol} Earnings One-Pager — {quarter} {year}## Prepared Remarks — Key Points{prepared_summary}## Q&A Highlights{qna_summary}""".strip()print(final_one_pager[:1200]) # preview
Tips that keep quality high:
Keep temperature low (≈0.2) for factual tone.
If a section is extremely long, chunk at ~5–8k tokens, summarize each chunk with the same prompt, then ask the model to merge chunk summaries into one section summary before producing the final one-pager.
If you also fetched headline numbers (EPS/revenue, guidance) earlier, prepend them to the prompt as brief context to help the model anchor on the right outcomes.
Building the End-to-End Pipeline
At this point, we have all the building blocks: the FMP API to fetch transcripts, a cleaning step to structure the data, and Groq LLM to generate concise summaries. The final step is to connect everything into a single workflow that can take any ticker and return a one-page earnings call summary.
The flow looks like this:
Input a stock ticker (for example, NVDA).
Use FMP to fetch the latest transcript.
Clean and split the text into Prepared Remarks and Q&A.
Send each section to Groq for summarization.
Merge the outputs into a neatly formatted earnings one-pager.
Here’s how it comes together in Python:
def summarize_earnings_call(symbol, quarter, year, api_key, groq_key): # Step 1: Fetch transcript from FMP url = f"https://financialmodelingprep.com/api/v3/earning_call_transcript/{symbol}?quarter={quarter}&year={year}&apikey={api_key}" resp = requests.get(url) resp.raise_for_status() data = resp.json() if not data or "content" not in data[0]: return f"No transcript found for {symbol} {quarter} {year}" text = data[0]["content"] # Step 2: Clean and split clean_text = re.sub(r'\s+', ' ', text).strip() if "Question-and-Answer" in clean_text: prepared, qna = clean_text.split("Question-and-Answer", 1) else: prepared, qna = clean_text, "" # Step 3: Summarize with Groq prepared_summary = summarize_section(prepared, symbol, quarter, year) qna_summary = summarize_section(qna, symbol, quarter, year) # Step 4: Merge into final one-pager return f"""# {symbol} Earnings One-Pager — {quarter} {year}## Prepared Remarks{prepared_summary}## Q&A Highlights{qna_summary}""".strip()# Example runprint(summarize_earnings_call("NVDA", 2, 2024, API_KEY, GROQ_API_KEY))
With this setup, generating a summary becomes as simple as calling one function with a ticker and date. You can run it inside a notebook, integrate it into a research workflow, or even schedule it to trigger after each new earnings release.
Free Stock Market API and Financial Statements API...
Conclusion
Earnings calls no longer need to feel overwhelming. With the Financial Modeling Prep API, you can instantly access any company’s transcript, and with Groq LLM, you can turn that raw text into a sharp, actionable summary in seconds. This pipeline saves hours of reading and ensures you never miss the key results, guidance, or risks hidden in lengthy remarks. Whether you track tech giants like NVIDIA or smaller growth stocks, the process is the same — fast, reliable, and powered by the flexibility of FMP’s data.
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