DEV Community: Muhammad Abuelenin

The transcript was the easy part: what we learned building an AI meeting assistant

Muhammad Abuelenin — Thu, 09 Jul 2026 10:15:05 +0000

Transcription is a solved problem. You pipe audio into any decent ASR model and get text back at 90%+ accuracy. If you think that's the hard part of building an "AI meeting assistant," you'll ship something nobody keeps using.

We learned this the slow way while building KenzNote, and I want to walk through the parts that actually turned out to be hard; because most of them aren't about the model, they're about the product decisions around it.

"Who said what" is a harder problem than "what was said"

Raw transcripts are a wall of text. The first thing that makes a transcript useful is speaker attribution, knowing that line 47 was the VP of Sales, not the engineer who joined five minutes late. Diarization (splitting audio into "who spoke when") is its own error-prone pipeline stage, separate from ASR, and the two don't always agree with each other.
A speaker change mid-sentence, two people talking over each other, someone joining from a noisy coffee shop; all of it degrades attribution quality in ways that compound downstream.

Once you have decent diarization, you get something more interesting for free: speaking-time distribution and participation patterns. Turns out "who talked for 80% of a meeting that was supposed to be a discussion" is a data point people actually want, not just a novelty stat.

Summarization is a compression problem with a business logic layer on top

A generic LLM summary of a call is fine. A useful summary requires deciding, ahead of time, what categories of information matter: decisions made, open questions, and action items. That last one is the deceptive part. "Extract action items" sounds like a prompt-engineering exercise. In practice it's an entity-resolution problem:

Assignee detection has to resolve "can you take that" to an actual person, which means tracking who "you" refers to across turns of dialogue, not just NER on names.
Deadline detection has to normalize "let's circle back Thursday" against the actual meeting date, which sounds trivial until the meeting happens the night before a holiday and everyone means different Thursdays.
Not every commitment is phrased as a commitment. "I guess I'll look into it" is an action item. "That would be nice" is not. Getting that boundary right is most of the tuning work.

We treat this less like a single summarization call and more like a small pipeline: segment the transcript, classify segments, extract candidate items, then resolve entities against meeting metadata (attendee list, calendar context if available, date). Each stage is boring on its own. The value is in not skipping any of them.

"Chat with your meetings" is a retrieval problem, not a chat problem

The feature people ask for once they trust the transcripts is: let me just ask a question instead of reading anything. "What did we agree on pricing last Tuesday?" That's not a prompting problem, it's an indexing and retrieval problem, you need per-meeting and cross-meeting retrieval that respects timestamps and speaker context, not just semantic similarity over chunks of text.
Naive RAG over transcripts gives you answers that are technically relevant and practically wrong, because it'll happily surface a similar-sounding tangent from three meetings ago instead of the actual decision from the one you meant.

Getting retrieval to respect which meeting, when, and who turned out to matter more than which LLM sits at the end of the pipeline.

The architecture decision that actually mattered: privacy as a constraint, not a feature

Early on we had to decide whether meeting audio and transcripts would ever be used to improve our models, the default assumption for most AI products, and usually buried in a ToS nobody reads.
We went the other way: it's contractually excluded, for us and for any vendor in the pipeline, and everything is encrypted in transit and at rest.
This wasn't a marketing decision, it was an architecture decision made early, because "we'll bolt on privacy later" doesn't really work once training pipelines exist.

It constrains vendor choice and shrinks your options, but for a category where people discuss compensation, layoffs, and legal matters out loud, I think it's the correct trade.

We also skipped the assumption that we need standing calendar access. Most competitors want an OAuth grant to your calendar so a bot can auto-join everything.
We built the primary flow around local recording upload instead, you control what gets captured, full stop. A calendar-bot mode exists for people who want it, but it's opt-in, not the default posture.

Where that leaves us

None of this is exotic. ASR, diarization, structured extraction, and retrieval are all "solved" in the sense that there are libraries and APIs for each.
The actual product is in the seams between them; attribution errors that need graceful degradation, extraction that respects the difference between a commitment and a nice thought, retrieval that understands time and identity, and a privacy posture that's a constraint from day one rather than a patch afterward.

If you want to see how it holds up on your own meetings, KenzNote has a free two-meeting trial, no card required. I'd genuinely rather hear what breaks than get a compliment.

How Customer Support Teams Translate Call Notes Using APIs

Muhammad Abuelenin — Thu, 11 Dec 2025 09:48:44 +0000

Customer support teams talk to people from all over the world every day. One hour they help someone in English, the next hour someone in Spanish or Japanese.

Managers need to read every call note, but typing everything by hand is impossible.

Here is a super clean API workflow that big and small support teams are using in 2025 to transcribe and translate calls automatically.

The 4-step flow:

Record or save the call (most tools already do this)
Send the audio file to Transgate API
Get perfect transcription + translation in seconds
Save the text into your CRM or helpdesk

Let’s see it with real code examples.

Step 1 – Get your API key
Sign up at Transgate → Dashboard → API Keys → Copy the key

Step 2 – Send audio for transcription

curl -X POST https://api.transgate.ai/v1/transcribe \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -F "file=@/path/to/call_recording.mp3" \
  -F "language=en"

You get JSON back with the full text and speaker labels.

Step 3 – Translate the same text

curl -X POST https://api.transgate.ai/v1/translate \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -F "text=YOUR_TRANSCRIPT_HERE" \
  -F "target_language=es"

Step 4 – Save to your system (example with Zapier or direct to Zendesk, Freshdesk, etc.)

Real company example:
A SaaS company with 200 support agents added this flow. Before: agents spent 2.5 hours per day typing notes. After automation: only 20 minutes reviewing. They saved 400+ hours per month.

Statistics:

Companies that automate note-taking improve CSAT by 18% (Gartner 2025)
Multilingual support increases customer loyalty by 42% (Harvard Business Review)

Full API docs → https://transgate.ai/api-docs

Pricing → pay per minute, no minimum

Start building today and make your support team happy!

The AI Revolution - From Language Models to Transcription Technology

Muhammad Abuelenin — Thu, 12 Sep 2024 12:17:21 +0000

I. The Rapid Evolution of AI

Artificial Intelligence has been developing at an unprecedented rate of late. It has transformed and continues to transform the topography of many industries into unbelievable feats. Thick in this action are NLPs and LLMs which have made quantum jumps in the understanding and generation of human-like texts.

A. The Rise of OpenAI

OpenAI is among the leading pioneers in AI research and development; hence, the company has been in the limelight concerning recent developments in LLMs. The series of GPTs developed by the company stands for Generative Pre-trained Transformer and have set ground-breaking records regarding language understanding and generation. The latest one, GPT-4o outshone its predecessors in reasoning, task execution, and creation with a difference. According to OpenAI, GPT-4o outperforms GPT-3.5 on a variety of professional and academic benchmarks, scoring in the 90th percentile on a simulated bar exam compared with the 10th percentile for its predecessor [1].
GPT4 and beyond the power and potential of advanced generative ai

B. Meta's Llama 3:

A New Challenger Meta's Llama 3 is the successor to Llama 2, and this too has had a big buzz in the AI community. While exact performance metrics aren't publicly available now at the time of writing, early reports indicate a large increase in efficiency and capability. There is every reason to expect that Llama 3 will yield increased performance on a wide range of NLP tasks, rivaling or surpassing other leading models in some respects.
Introducing Meta Llama 3

However, more parameters doesn’t necessarily mean better.

II. The Power of NLP and LLMs

Natural Language Processing, along with Large Language Models, has no doubt changed the level at which machines interact with human language. These technologies have further inspired applications in everything from chatbots and virtual assistants to content generation and data analytics.

A. Advances in NLP Recent progress in the field of NLP has seen high-quality sentiment analysis, better machine translation, and advanced text summarization. According to MarketsandMarkets, the NLP market, which is witnessing increasing demand for these technologies, is projected to grow from $11.6 billion in 2020 to $35.1 billion by 2026 [2].

B. LLMs: More Than Text Generation Large Language Models have gone beyond the generation of text. They currently show capabilities in:

Few-shot learning: adapting to new tasks with a minimum number of examples.
Multi-modal understanding: processing and generating content in text, image, and even audio format.
Reasoning and problem-solving: solving logical and mathematical challenges that are really complex.

III. Impact on Transcription

The impact on Transcription Technology Research and development within NLP and LLM have impacted how transcription is carried out today, from being a very labor-intensive process to an efficient AI-driven solution.

A. Power of Transcriptinon

AI-Powered Transcription State-of-the-art transcription tools harness the power of deep speech recognition models together with NLP in developing text from audio and video content with high accuracy. In fact, there have been huge improvements in this technology: from over 8 percent in 2016 to below 5 percent in the recent years concerning speech recognition for English. [3]

B. Benefits of AI Transcription

Speed: AI can transcribe hours of audio in minutes, which is much faster compared to human transcription.
Cost-effectiveness: This automation cuts down labor costs.
Scalability: AI systems process large volumes of content without showing fatigue.

C. Applications Across Industries

Media and Entertainment: Subtitling, closed captioning, and content indexing
Legal: Court proceedings and deposition transcriptions
Healthcare: Medical dictation and patient record management
Business: Meeting minutes, interview transcriptions, and market research

IV. Transgate: Taping LLM Power for Transcription

Among AI-powered transcription tools, Transgate is the only one that leverages the latest and most powerful LLM technology updates to deliver speech-to-text transcription unparalleled for speed and precision.

A. LLM-Driven Accuracy Because Transgate uses the latest language models, its accuracy currently exceeds 98%. This level of precision becomes critical in applications requiring verbatim transcriptions or involving specialized vocabularies.

B. User-Centric Approach With a spotlight on simplicity and user experience, Transgate earned more than 100% customer satisfaction. Such great feedback underlines that the tool meets or overpasses users' expectations concerning ease of use and quality of output.

C. Versatility and Integration Transgate's solution through LLMs ensures smooth adaptive behavior, even to various accents, dialects, and terminologies used across different industry segments. The wide versatility of the platform makes it an essential tool for academic research to the corporate world.

V. AI and Transcription Future

We are witnessing the growth of AI almost every day; there is a lot to expect in the world of transcription, and some of these might be:

Real-time translation and transcription across several languages.
Greater context and understanding for better punctuation and formatting.
Emotion and Tone-of-Voice Detection in Speech for More Expressive Transcriptions.

Conclusion:

AI, most particularly in the field of NLP and LLMs, has reconditioned the field of transcription. At the forefront of this technological revolution are tools such as Transgate, which provide enterprises and researchers not with just a speech-to-text transformation but accuracy and speed that are second to none. The technology brings the unlock of new possibilities—in how the information of spoken word is further processed, analyzed, and reported—in various contexts.

[1] OpenAI. (2023). GPT-4 Technical Report.
[2] MarketsandMarkets. (2021). Natural Language Processing Market Global Forecast to 2026.
[3] Synced. (2020). A Survey of Deep Learning Techniques for Speech Recognition.