Daemlabs | Production AI Systems & Agents

2026-03-10•4 min read•blog

Why We Call Ourselves Daemons

The story behind the Daemlabs name and what it means for how we work.

2026-06-14•10 min read•docs

How We Built an Offline RAG AI That Reads 1000s of technical Pages — And Never Hallucinates

We built a fully offline AI document intelligence system that reads thousands of pages of technical documentation and answers engineering queries instantly — no cloud, no internet, no hallucinations.

ragvector-databaseOffline LLM InferencePython

2026-06-07•7 min read•blog

AI voice agent platform

When a client came to us needing a voice bot that could handle inbound customer calls for their FAQ support, we knew the standard approaches would not work. IVR trees are brittle. Generic LLM wrappers hallucinate. Off-the-shelf voice platforms are black boxes you cannot tune. So we built the infrastructure ourselves.

voice-ailatencypipecatvoice-agentcustomer-support-agent

2026-06-14•5 min read•blog

Drone Swarm Intelligence Platform: Autonomous Mapping, Detection & Voice-Controlled Navigation

When developing autonomous drone operations, we encountered a fundamental challenge: collecting aerial data is easy, but transforming that data into actionable intelligence and autonomous navigation decisions is much harder. We needed a system capable of generating large-scale aerial maps, detecting threats and obstacles, planning safe routes, and allowing operators to control drone missions entirely through voice commands. The result was a multi-pipeline Drone Swarm Intelligence Platform that combines computer vision, path planning, and speech-driven command execution into a unified workflow.

drone-swarmcomputer-visionyolosahipath-planningautonomous-navigationspeech-recognitionroboticsaerial-mapping

2026-06-14•5 min read•blog

Smart Shot Analyzer: Basketball Trajectory & Collision Detection

When we started exploring sports analytics, we wanted to answer a deceptively simple question: can a computer determine whether a basketball shot goes in or hits the rim just by watching video footage? Traditional object tracking could locate the ball, but understanding the physics of the shot required something more. So we built a computer vision system that tracks basketball trajectories, predicts projectile motion using polynomial regression, and detects rim collisions through statistical analysis.

computer-visionbasketball-analyticstrajectory-predictionopencvsports-aipolynomial-regressioncollision-detection

2026-06-14•5 min read•blog

Smart AI Task Scheduler: Bringing Operating System Scheduling Principles to Human Productivity

When we looked at how people manage their daily work, we noticed a familiar problem: everyone has tasks competing for limited time, deadlines constantly shifting, and priorities changing throughout the day. Interestingly, operating systems have solved a similar problem for decades through CPU scheduling algorithms. So we asked a simple question: what if we treated a person's day like a processor and tasks like processes? The result was Smart AI Task Scheduler — an intelligent scheduling platform that combines classical scheduling theory, machine learning, and agentic AI to optimize daily productivity.

agentic-aitask-schedulermachine-learningxgboostfastapielectronreactproductivityscheduling-algorithms

2026-06-14•5 min read•blog

Audio Denoiser: Speech-Noise Separation with U-Net

When we started experimenting with speech enhancement, the challenge was clear: real-world audio is rarely recorded in perfect conditions. Hospital environments, busy streets, crowded rooms, and industrial settings all introduce noise that makes speech difficult to understand. Traditional filtering techniques struggle with complex, non-stationary noise, so we built a deep learning-based denoising system using a U-Net architecture trained directly on spectrograms.

audio-processingdeep-learningspeech-enhancementunetpytorchdenoisingspectrograms

2026-06-14•5 min read•blog

VidSage: Building a Smart Video Transcoder & Quality Analysis Pipeline

When working with video delivery systems, one problem quickly becomes apparent: encoding a video is easy, but understanding how encoding decisions affect quality is much harder. Resolution changes, bitrate constraints, compression artifacts, and visual degradation can all impact the viewing experience. To better understand this process, we built VidSage — a video transcoding and quality analysis platform that combines FFmpeg, OpenCV, and automated reporting into a single workflow. Markdown Content:- When working with video delivery systems, one problem quickly becomes apparent: encoding a video is easy, but understanding how encoding decisions affect quality is much harder. Resolution changes, bitrate constraints, compression artifacts, and visual degradation can all impact the viewing experience. To better understand this process, we built VidSage — a video transcoding and quality analysis platform that combines FFmpeg, OpenCV, and automated reporting into a single workflow. This is the story of how we built a lightweight video engineering tool that automates transcoding, extracts media metadata, evaluates visual quality, and generates actionable reports for video analysis.

video-processingffmpegvideo-transcodingcomputer-visionvideo-analyticsopencvmedia-engineeringstreaming

Blog

Why We Call Ourselves Daemons

How We Built an Offline RAG AI That Reads 1000s of technical Pages — And Never Hallucinates

AI voice agent platform

Drone Swarm Intelligence Platform: Autonomous Mapping, Detection & Voice-Controlled Navigation

Smart Shot Analyzer: Basketball Trajectory & Collision Detection

Smart AI Task Scheduler: Bringing Operating System Scheduling Principles to Human Productivity

Audio Denoiser: Speech-Noise Separation with U-Net

VidSage: Building a Smart Video Transcoder & Quality Analysis Pipeline