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 AI-Native Application Development

AI Transcription & Intelligent Note-Taking

We build deeply integrated real-time transcription engines with multi-speaker diarization, LLM-powered meeting summaries, action item extraction, and searchable knowledge repositories — embedded inside your product, not bolted on as a third-party widget.

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Core AI Capabilities
  • Real-time multi-speaker diarization with name tagging
  • Sub-500ms transcript delivery via WebSocket
  • LLM-generated executive summaries & TL;DR
  • Automatic action item & decision extraction
  • Keyword / topic detection with timestamp anchoring
  • Searchable meeting knowledge base via embeddings
  • CRM & ticketing auto-population from transcript

How It Works

Audio Capture & Streaming

Raw audio from the call or meeting is streamed in real-time to the transcription engine — either via a direct WebRTC audio track tap or a WebSocket audio stream from your application.

Diarization, Transcription & NLP

Deepgram Nova-2 or Whisper identifies speakers, transcribes speech with punctuation restoration, and pipes final segments to an LLM for summary, action item extraction, and topic tagging.

Storage, Search & Integration

Transcripts are stored in PostgreSQL with pgvector embeddings for semantic search. Webhooks fire to Slack, CRM, or ticketing systems — configurable per workspace.

What We Build

Multi-Speaker Diarization

Identifies and labels each speaker independently — no manual tagging. Supports custom speaker name mapping via pre-call roster or real-time correction.

Streaming Transcript Engine

Server-side WebSocket broadcast of partial and final transcript segments with endpointing, punctuation restoration, and inverse text normalisation.

LLM Post-Processing

Summaries, action items, sentiment, and chapter generation via GPT-4o or Claude. On-prem Llama3 / Mistral for data-sensitive deployments.

Semantic Meeting Search

pgvector or Pinecone embedding store — search across all meetings by topic, speaker, or decision. Results link back to the exact timestamp.

Workflow Integrations

Webhooks, Slack notifications, Salesforce/HubSpot sync, Jira task creation — configurable per workspace without custom dev.

Multilingual & Domain Tuning

Hindi, Tamil, Telugu, Kannada, and 30+ languages. Custom vocabulary for medical, legal, and financial terminology built into the model.

CentEdge vs The Alternative

Generic transcription APIs (Otter, Fathom, Rev)

  • Data stored on vendor servers permanently
  • No control over language model or accuracy tuning
  • Generic summaries — not domain-aware
  • Fixed features — no custom workflow integrations
  • Per-seat or per-minute cost at enterprise scale
CentEdge Custom Transcription Engine
  • All audio and transcripts on your infrastructure
  • Swap models — Deepgram, Whisper, or on-prem Llama3
  • Domain-tuned vocabulary for your specific terminology
  • Custom webhook integrations to any enterprise system
  • One-time build cost, no per-minute transcription fees

Who This Is For

  • Sales Teams: Automated CRM Note-Taking
  • Legal: Deposition & Hearing Transcripts
  • Healthcare: Clinical Visit Documentation
  • Finance: Board & Earnings Call Records
  • HR: Interview & Performance Review Notes
  • Journalism: Interview Transcription
  • EdTech: Lecture & Webinar Captions
  • BFSI: Regulatory Audit Trail Records

Technology Stack

Deepgram Nova-2

Whisper large-v3

WebSocket / SSE

GPT-4o / Claude

Llama 3 (on-prem)

pgvector

Pinecone

Node.js

PostgreSQL

Redis

Frequently Asked Questions

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What is the transcription accuracy?

For English, CentEdge's pipeline using Deepgram Nova-2 achieves 98%+ word accuracy in clean audio conditions. For noisy environments or domain-specific terminology, custom vocabulary tuning typically pushes accuracy above 95%. For Indian regional languages (Hindi, Tamil, Telugu, Kannada), accuracy ranges from 90–96% depending on dialect and audio quality.

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How does multi-speaker diarization work?

Diarization separates the audio stream into per-speaker segments before transcription. Deepgram's native diarization or a custom PyAnnote-based pipeline identifies speaker changes and assigns speaker labels. These labels can be mapped to real names via a pre-call roster upload or corrected in real-time via the UI. Each transcript segment carries a speaker ID and timestamp.

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Can the transcription engine run fully on-premise?

Yes. CentEdge deploys Whisper large-v3 on your GPU servers for the STT layer, and Llama3 or Mistral for the LLM post-processing layer. The entire pipeline — audio ingestion, transcription, summarisation, and storage — can run on bare-metal with zero external API calls. This is required for BFSI and Healthcare clients with strict data residency mandates.

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What happens with the transcripts after the meeting?

Transcripts are stored in PostgreSQL with configurable retention policies, encryption at rest, and automated deletion schedules. Embeddings are generated for each transcript segment and stored in pgvector, enabling semantic search across all historical meetings. Access is RBAC-controlled — users only see transcripts from meetings they attended or were granted access to.

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Can this be added to an existing video conferencing platform?

Yes. The transcription engine can be integrated with any existing WebRTC or telephony platform that can provide a real-time audio stream. Integration typically takes 2–4 weeks and requires a WebSocket audio feed or a SIP/RTP audio tap. CentEdge provides a REST API and SDK for embedding the transcript UI into your existing application.

GET IN TOUCH

Let’s Build This
Together

Tell us about your project and we’ll return with an architecture overview and engagement proposal within 48 hours.

  • hello@centedge.io
  • +91 6362 814071
  • T-Hub, Hyderabad, India
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