Guides, newsletters, press releases, and case studies from our team of AI engineers and researchers.
We ran open-source TTS models through Hindi, English, and Telugu listening evaluations to find out which ones actually sound natural, warm, and clear across all three languages.
Build a privacy-first pipeline that strips human speech from audio using Silero VAD and classifies the remaining ambient environment with Hugging Face's Audio Spectrogram Transformer.
Build a Target Speaker Extraction pipeline using SpeechBrain's ECAPA-TDNN and SepFormer models to isolate a specific speaker's voice from mixed audio recordings using cosine similarity matching.
A hands-on comparison of five noise suppression techniques — SpeechBrain, Spectral Subtraction, Wiener Filter, Spectral Gating, and Median Filter — benchmarked on speech quality, intelligibility, latency, and real-time performance.
Learn how to build a sovereign AI-powered medical prescription parsing and search system using PyMuPDF, PaddleOCR, PostgreSQL, open-source LLMs, and pgvector for semantic retrieval.
A complete walkthrough of building ARIA — a branded, in-car voice assistant — from the microphone capturing your voice to the speaker playing the reply. Learn how adaptive VAD, streaming LLM output, parallel TTS synthesis, and intent routing all fit together.
Build a real-time defect detection AI agent for manufacturing using YOLO for detection, CLIP for semantic embeddings, and Qdrant Edge for on-device vector search — no cloud required.
Learn how to build a fully local AI assistant using Qdrant Edge for vector search and a lightweight embedding model — no cloud required, with full privacy and control.
A complete walkthrough of building a deepfake voice detection pipeline — from raw .flac files to a REST API verdict — trained on the ASVspoof 2019 dataset using acoustic features and a lightweight CNN.
A step-by-step guide to building a tool that fetches any article URL, extracts the clean content, and converts it into a well-formatted Markdown file — complete with working images and syntax-highlighted code blocks.
A fully edge-based anomaly detection system that learns continuously from live sensor data, adapts in real time, and detects anomalies within milliseconds — no labelled data, no cloud required.
A Python pipeline that groups unlabeled images by visual content and automatically names each group — no manual labeling required — using CLIP embeddings, K-Means clustering, and zero-shot classification.
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