Some people are bothered by big failures. Others are unsettled by the small things that never quite work the way they should. Sabeer Nelli belonged to the secondSome people are bothered by big failures. Others are unsettled by the small things that never quite work the way they should. Sabeer Nelli belonged to the second

How a Relentless Eye for Detail Shaped a Better Way to Move Money

Some people are bothered by big failures.
Others are unsettled by the small things that never quite work the way they should.

Sabeer Nelli belonged to the second group. Long before he built anything new, he noticed patterns most people learned to tolerate. Delays that were brushed off as normal. Processes that wasted time but were rarely questioned. Systems that demanded patience instead of earning trust. Those details stayed with him, quietly shaping how he viewed work and responsibility.

How a Relentless Eye for Detail Shaped a Better Way to Move Money

Sabeer Nelli is the founder of Zil Money, but his story begins far from fintech. It begins with awareness formed early in life. Growing up in Manjeri, a small town in Kerala, India, he learned that reliability mattered. When something failed, there was no abstraction. Someone felt it immediately. Excuses didn’t travel far. Accountability did.

As a child, he helped his family by selling everyday items and taking on small responsibilities. These moments weren’t framed as lessons, but they taught him something lasting. If you said something would be done, it needed to be done. If it wasn’t, trust faded quickly. That simple understanding became a quiet standard he carried forward.

When he moved to the United States, he pursued business studies, but observation remained his strongest tool. He paid attention to how companies operated under pressure. He noticed how inefficiencies became habits. How people built workarounds instead of fixing root problems. Over time, he began to see that many systems survived not because they were effective, but because challenging them felt inconvenient.

For a time, he pursued aviation, training to become a commercial pilot. The structure and discipline appealed to him. When medical limitations forced him to step away from that path, it was a difficult pause. Losing direction can shake anyone. For Sabeer, it clarified something important. Precision mattered to him, whether in the air or on the ground.

He redirected that focus into building a business. Entering the fuel and retail industry, he founded Tyler Petroleum and grew it into a substantial operation. Running a business of that scale demanded constant attention. Employees relied on timely payroll. Vendors depended on consistent payments. Customers noticed every breakdown. Nothing stayed theoretical for long.

That’s when the friction became unavoidable.

Payments were fragmented. Checks lived in one system, ACH transfers in another, wires and cards somewhere else entirely. Reconciling accounts was time-consuming. Errors were frequent. Then came a moment that cut through everything. A payment processor froze his business account without warning. Transactions stopped. Operations stalled. Control vanished.

The experience was unsettling, not dramatic. It revealed a vulnerability many business owners feel but rarely articulate. The systems meant to support them often held more power than they did. Growth felt fragile when it depended on tools that could shut down without explanation.

Sabeer didn’t respond with outrage. He responded with attention. He began asking why businesses were expected to accept this level of risk as normal. Why tools were built without regard for the pressure they created. Why responsibility flowed one way.

Instead of trying to fix everything at once, he focused on one problem he understood deeply. That effort led to OnlineCheckWriter.com, a platform designed to make check management straightforward and reliable. It didn’t aim to impress. It aimed to remove friction. For many businesses, that alone was transformative.

But as usage grew, a larger pattern emerged. The issue wasn’t checks. It was fragmentation. Businesses didn’t want more tools. They wanted fewer systems that worked together. They wanted clarity, not complexity.

That realization became the foundation for Zil Money.

From the start, Sabeer approached it with restraint. He wasn’t chasing trends or headlines. He was solving problems he had lived with. Every feature had to earn its place. Would it save time? Would it reduce errors? Would it feel intuitive after a long day?

Zil Money was built to bring multiple payment methods into a single environment while keeping the experience simple. Checks, ACH transfers, wires, and virtual cards weren’t treated as separate products. They were parts of one coherent flow. The goal wasn’t speed alone. It was confidence.

Growth followed steadily. Sabeer chose to bootstrap rather than expand recklessly. He believed trust in financial tools had to be earned deliberately. Each customer represented a real business with real consequences. That understanding shaped how decisions were made.

His leadership style mirrors that philosophy. He values clarity over noise. He believes good systems should explain themselves. He encourages teams to think from the user’s perspective rather than internal convenience. And he treats responsibility as central, not optional.

Challenges were inevitable. Fintech leaves little room for error. Regulations evolve. Security demands increase. Each obstacle required careful response. Instead of treating these moments as setbacks, Sabeer viewed them as signals. Where could the system be stronger? Where could trust be reinforced? Where could friction be removed?

Beyond the platform, he has remained connected to a broader sense of purpose. He has spoken about creating opportunities outside traditional tech hubs and investing in innovation where it’s least expected. For him, progress isn’t limited by location. It’s driven by mindset.

Today, Sabeer Nelli is known for building tools that quietly improve how businesses operate. His impact isn’t measured in attention. It’s measured in smoother workflows, fewer disruptions, and restored confidence. Businesses that once dreaded payment processes now move through them with ease.

What makes his story compelling isn’t spectacle. It’s consistency. He noticed the small things others ignored and refused to accept them as permanent.

Sometimes, real change doesn’t come from bold disruption. It comes from someone who pays close attention, cares deeply about responsibility, and builds with the people on the other side in mind.

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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. 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