Behind the Scenes at Meetingful: How We Turn Raw Dialogues into Deep Discoveries
Meetingful has been in development for close to 3 years. During this transformative journey, we’ve traversed a path filled with challenges, each pushing us closer to our current groundbreaking results. The outcome of our persistent efforts is the creation of four intricate processing “pipelines.” Each pipeline is a testament to the unique intellectual property and innovation that Meetingful brings to the table. As far as we are aware, no other tool offers the comprehensive capabilities that Meetingful does. Dive into a concise overview of our pioneering pipeline process:
Pipeline 1: Meetingful’s initial pipeline is foundational, transforming the maze of your meetings into structured data ready for in-depth analysis. Regardless of providing a meeting transcript from a transcribing platform such as Zoom, Microsoft Teams, and Google Meet, or even just providing an audio file or a YouTube link, this pipeline effortlessly handles them. Leveraging advanced speech-to-text conversion algorithms, it produces a robust meeting transcript, serving as the foundation for all subsequent text mining operations. Of course, if the audio is a poor recording and hard to interpret, this will affect the quality of the output.
The paramount strength of this pipeline is its capacity to standardize and purify data from the outset. This guarantees that the insights extracted in the following stages are not only actionable but steadfastly reliable.
Pipeline 2: Meetingful’s second pipeline is where the magic of Natural Language Processing (NLP) unfolds. It initiates its process with Part-of-Speech (POS) Tagging, diligently categorizing words into their respective grammatical classes. This foundation paves the way for the integral Sentiment Analysis, ensuring every emotional nuance and tone of the discussions is precisely captured.
Segueing into its next stage, the pipeline focuses on Phrase Mining. This segment is instrumental in identifying and extracting pivotal phrases and topics that resonate within the meeting’s discourse. By undergoing intricate clustering and topic modeling, these phrases crystallize into an organized ensemble of core subjects and themes, offering a holistic understanding of the meeting’s essence.
Pipeline 3: The third pipeline capitalizes on the cutting-edge capabilities of our fine-tuned Large Language Models (LLMs) – uniquely crafted for deep conversational analysis. Informed by the rich data structures produced in Pipelines 1 and 2, Pipeline 3 adopts a multi-layered methodology. The LLMs are meticulously directed to spotlight and delve into the meeting’s most crucial junctures, revealing profound insights.
From an initial unbiased overview summarizing the meeting’s gamut, Meetingful then meticulously crafts:
- Key Takeaways: Distilling the broad summary to pinpoint and spotlight the meeting’s paramount takeaways.
- Executive Summary: Delivering a polished, top-tier review reminiscent of a crafted report by seasoned professionals.
- Section Summary: Fragmenting the meeting into coherent sections, each appended with its respective summary for a detailed comprehension.
- Conversational Dynamics: A keen analysis capturing the intricate cadence and ebb of the dialogue, echoing the true essence of conversational interactions.
- Action Items: Gleaning definitive, actionable steps from the trove of meeting content, priming them for immediate execution.
- Opinions & Ideas: The LLM ventures into the realm of participants’ discourses, distinguishing between factual statements and opinionated content.
Concluding this pipeline, Meetingful doesn’t merely present a transcript but an insightful, articulate dissection. This converts rudimentary conversation into tangible insights, amplifying the comprehension of the meeting’s central narrative. Moreover, the “Ask Meetingful” chatbot feature enables users to pose any question about the meeting in their preferred language and receive responses seamlessly in that same language, truly transcending linguistic barriers.
Pipeline 4: The last pipeline revolves around vectorization, but like much of the process, what makes Meetingful special isn’t just vectorization but how we do it. We use unique models and workflows tailored for handling meeting transcripts. This includes how we break down transcripts and insights extracted by Meetingful in pipelines 2 and 3, as well as creating unique metadata for each piece of embedded text. This process provides us with the capability for enhanced filtering and similarity matching in our vector search and in comparative and cumulative analysis.
The Uniqueness of Meetingful: The cornerstone of Meetingful’s unparalleled capabilities lies in its fusion of advanced methodologies. We pride ourselves on our linguistic approach to text mining, discerning deeper layers of meaning and nuance in conversations. The innovative methodologies across all four pipelines enable us to deftly select signals and units of analysis, from individual words and phrases to overarching concepts and sentences. At the core of Meetingful’s innovation is our use of generative AI with specialized Large Language Models (LLMs). These LLMs, built upon the data from Pipelines 1 and 2, focus keenly on the most critical parts of a meeting, extracting deep insights from key moments. Additionally, our hybrid approach, which integrates rule-based, statistical, and deep learning NLP techniques, guarantees that results are cross-checked and refined for optimal insights.
With this multi-layer approach, Meetingful is not just another meeting tool—it’s a paradigm shift in the world of meeting analytics, delivering insights that were once deemed unattainable.