TreviPay, a fully managed B2B payments platform, today announced the availability of the Growth Center, a set of capabilities within the TreviPay Client Portal TreviPay, a fully managed B2B payments platform, today announced the availability of the Growth Center, a set of capabilities within the TreviPay Client Portal
TreviPay Announces AI-Powered Growth Center to Help Enterprises Predict Buyer Behavior and Drive B2B Sales
TreviPay, a fully managed B2B payments platform, today announced the availability of the Growth Center, a set of capabilities within the TreviPay Client Portal to help identify buyer growth opportunities more strategically and build stronger, longer-lasting supplier relationships. With its advanced features, the new Growth Center enables businesses to explore buyer needs, spot trends and optimize key steps in the order-to-cash (O2C) process. Integrated into TreviPay’s global payments network, the customizable add-ons will help businesses use their O2C programs to deepen buyer relationships and drive lasting engagement.
For many suppliers, growth is not only about bringing in new buyers. It also means keeping existing buyers engaged, spotting early signs of dormancy and giving sales, operations and finance teams a practical way to act without adding friction. By combining transactional data, behavioral insights and predictive intelligence, TreviPay Growth Center enables clients to identify where growth opportunities exist, which buyers need attention and how best to engage them before revenue is at risk.
“TreviPay’s network was built to help businesses grow,” said Dan Zimmerman, Chief Product and Technology Officer at TreviPay. “The Growth Center helps clients use predictive insights to spot changes in buyer behavior, re-engage customers and measure the impact of incentives, without adding work for other teams. It’s part of how we deliver value clients can’t easily replicate and help protect long-term program performance.”
As part of TreviPay’s 2026 predictions, AI is projected to be the engine to power credit, invoicing and collections – not simply as automation, but as the intelligence anticipating risk, preventing revenue leakage and strengthening buyer-supplier relationships. The Growth Center is designed to help clients act on these insights and improve program performance. The result is a smarter, more efficient approach to managing incentive programs, helping businesses optimize loyalty investments and predict future engagement. Capabilities include:
- Buyer insights to help sellers understand purchasing trends and engagement signals
- Predictive insights to identify buyers at risk of going dormant, then inform targeted outreach and incentives
- Tools to support testing and iteration so teams can improve campaign performance over time
- Rebate management to easily configure incentives with automated tracking and reporting
- Dashboards providing at-a-glance visibility into program performance and overall program health
TreviPay Growth Center Case Studies
With general availability expected in Q2 2026 for all clients, TreviPay continues to explore Growth Center capabilities. In pilot testing with a major US-based retailer, TreviPay’s latest AI and machine learning models predicted which buyers would go dormant with high accuracy. The buyers identified have been actioned by either engagement campaigns or incentives.
TreviPay has used these predictive signals to trigger buyer outreach, such as email reminders tied to available credit alongside targeted incentives. All tests resulted in an immediate increase in new spend, and in one example, 59 previously dormant buyers made $103,946 in purchases within eight days of the outreach.
In another example, the Growth Center’s Rebate Manager, which has been embedded with a large manufacturer for over one year, supported 14% year-over-year sales growth through a promotional incentive offering.
The post TreviPay Announces AI-Powered Growth Center to Help Enterprises Predict Buyer Behavior and Drive B2B Sales appeared first on FF News | Fintech Finance.
Piyasa Fırsatı
Falcon Finance Fiyatı(FF)
$0.08119
$0.08119$0.08119
USD
Falcon Finance (FF) Canlı Fiyat Grafiği
Sorumluluk Reddi: Bu sitede yeniden yayınlanan makaleler, halka açık platformlardan alınmıştır ve yalnızca bilgilendirme amaçlıdır. MEXC'nin görüşlerini yansıtmayabilir. Tüm hakları telif sahiplerine aittir. Herhangi bir içeriğin üçüncü taraf haklarını ihlal ettiğini düşünüyorsanız, kaldırılması için lütfen crypto.news@mexc.com ile iletişime geçin. MEXC, içeriğin doğruluğu, eksiksizliği veya güncelliği konusunda hiçbir garanti vermez ve sağlanan bilgilere dayalı olarak alınan herhangi bir eylemden sorumlu değildir. İçerik, finansal, yasal veya diğer profesyonel tavsiye niteliğinde değildir ve MEXC tarafından bir tavsiye veya onay olarak değerlendirilmemelidir.
Ayrıca Şunları da Beğenebilirsiniz
Bitwise CEO: In the next 6 to 12 months, the focus of the crypto field will be on the credit and lending market
PANews reported on September 18 that Bitwise CEO Hunter Horsley tweeted that over the next six to 12 months, the focus of the cryptocurrency sector will shift to credit and lending. This sector is expected to experience explosive growth in the next few years. He pointed out that the current cryptocurrency market capitalization is approaching $4 trillion and continues to grow. When people can borrow against cryptocurrency, they will choose to borrow rather than sell. Furthermore, the market capitalization of publicly traded stocks in the United States exceeds $60 trillion. With the tokenization of assets, individuals holding $7,000 worth of stocks will be able to borrow against them on-chain for the first time. Horsley believes that cryptocurrency is redefining capital markets, and this is just the beginning.
Paylaş
PANews2025/09/18 17:00
Nvidia (NVDA) Stock Rises After Q4 Earnings and Guidance Beat – Data Center Revenue Up 75%
TLDR Nvidia beat Q4 earnings estimates with EPS of $1.62 adjusted vs $1.53 expected Total revenue hit $68.13 billion, up 73% year-over-year Data center revenue
Paylaş
Coincentral2026/02/26 17:12
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.
Summarize Any Stock’s Earnings Call in Seconds Using FMP API was originally published in Coinmonks on Medium, where people are continuing the conversation by highlighting and responding to this story
Paylaş
Medium2025/09/18 14:40