Some problems don’t announce themselves as big ideas. They show up as late nights, stalled payments, and the quiet fear of not knowing what might break tomorrowSome problems don’t announce themselves as big ideas. They show up as late nights, stalled payments, and the quiet fear of not knowing what might break tomorrow

He Didn’t Set Out to Build Fintech. He Set Out to Stop the Bleeding

Some problems don’t announce themselves as big ideas.
They show up as late nights, stalled payments, and the quiet fear of not knowing what might break tomorrow.

That feeling followed Sabeer Nelli through some of the most demanding years of his working life. Not because he lacked systems, but because the systems around him never quite worked the way they should. Each workaround fixed one issue and exposed another. Over time, the weight of that friction became impossible to ignore.

He Didn’t Set Out to Build Fintech. He Set Out to Stop the Bleeding

Sabeer Nelli is the founder of Zil Money, but his story begins far away from fintech conversations and product roadmaps. It begins with responsibility learned early and reinforced often. Growing up in Manjeri, a small town in Kerala, India, he learned that effort mattered, but structure mattered just as much. When systems were loose, people paid the price. When things worked smoothly, life felt lighter.

As a young boy, he took on small jobs, selling everyday items and helping wherever he could. These weren’t romantic stories of hustle. They were lessons in consistency and accountability. If you promised something, you delivered. If you failed, the consequences were immediate and personal. That mindset stayed with him long after he left home.

When Sabeer moved to the United States, he didn’t shed those instincts. He studied business, but his curiosity went beyond textbooks. He watched how companies operated from the inside, how decisions affected people downstream, and how often inefficiency was treated as normal. He even pursued aviation, training to become a commercial pilot, only to have that goal cut short due to a medical limitation. Losing that dream was painful, but it taught him something lasting. Direction can change without erasing purpose.

Instead of retreating, he leaned into building something tangible. He entered the fuel and retail industry and founded Tyler Petroleum, growing it into a large operation with multiple locations. Running a business at that scale was relentless. There were employees depending on him, vendors waiting on payments, and margins that left little room for error. Every delay mattered.

It was during this period that the cracks in business payments became impossible to ignore. Paying suppliers meant juggling checks, ACH transfers, wires, and card payments, each with its own system and limitations. Reconciliation took hours. Errors created tension. Then one day, without warning, a payment processor froze his company’s account. Transactions stopped. Operations stalled. Trust vanished overnight.

That moment changed how he saw the entire landscape.

It wasn’t just about inconvenience. It was about control. Businesses were being asked to grow while standing on fragile ground. Tools that claimed to enable progress could just as easily shut it down. And the people building those tools rarely felt the fallout firsthand.

Sabeer didn’t respond with outrage. He responded with focus. If the system was broken, complaining wouldn’t fix it. Understanding it might.

His first step was narrow and intentional. He focused on solving one problem in a way that respected how businesses actually work. That effort became OnlineCheckWriter.com, a platform that allowed companies to manage checks digitally with clarity and control. It replaced messy workflows with something dependable. For many businesses, it was the first time payments felt manageable instead of stressful.

But as users adopted the platform, a larger pattern emerged. The issue wasn’t checks alone. It was fragmentation. Businesses didn’t want more tools. They wanted fewer, better ones. They wanted payments to feel unified, not scattered across disconnected systems.

That realization gave shape to Zil Money.

From the start, Sabeer approached it as a business owner, not a technologist chasing novelty. He asked simple questions that rarely guide software design. What would this feel like at the end of a long workday? Would this reduce anxiety or add to it? Could someone understand it without a manual?

Zil Money was built to bring multiple payment methods into one place while keeping the experience straightforward. Checks, ACH, wires, and virtual cards weren’t treated as separate products, but as parts of a single flow. The goal was never to overwhelm users with options. It was to give them confidence.

Growth didn’t come through loud promises. It came through reliability. Sabeer chose to bootstrap rather than chase aggressive expansion. He believed trust, especially in financial tools, had to be earned slowly. Each customer represented a real business with real risk. That responsibility shaped every decision.

His leadership style mirrors that philosophy. He values clarity over speed, depth over noise. He encourages teams to spend time with customer pain points instead of abstract metrics. He believes simplicity is not the absence of complexity, but the result of thoughtful design.

The journey hasn’t been smooth. Fintech comes with constant pressure. Regulations evolve. Security demands are unforgiving. Mistakes can carry serious consequences. Each challenge forced careful recalibration. Instead of treating obstacles as failures, Sabeer treated them as signals. Where could the system be stronger? Where could communication be clearer? Where could trust be reinforced?

Beyond product and platform, his vision extends outward. He has spoken about creating opportunities outside traditional tech centers, investing in innovation where it’s least expected. For him, progress isn’t about concentrating power. It’s about distributing possibility.

Today, Sabeer Nelli is recognized for building tools that quietly remove friction from business life. His impact isn’t measured in headlines. It’s measured in time saved, stress reduced, and confidence restored. Business owners who once dreaded payment days now move through them with ease.

What makes his story resonate isn’t scale alone. It’s intention. He didn’t build Zil Money to chase an industry. He built it because he knew how it felt when systems failed the people depending on them.

In a world that often celebrates disruption for its own sake, Sabeer represents something steadier. Someone who noticed where things hurt, stayed close to that discomfort, and chose to build something better. Not to be seen, but to be useful.

And sometimes, that’s how the most meaningful change begins.

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Summarize Any Stock’s Earnings Call in Seconds Using FMP API

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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. 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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. 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