What does Ahmed Sheikh do?

Ahmed Sheikh is a cloud-native agentic AI engineer who helps medium and large businesses move AI agents from stalled pilots to reliable, governed production. He specialises in multi-agent orchestration, agentic RAG, LLMOps, AI evaluation, and cloud-native AI infrastructure — serving clients worldwide.

What does Ahmed Sheikh build, and what does it cost?

Ahmed builds production-ready AI applications as three fixed-price packages, each with an optional monthly retainer for ongoing work after launch: a Full-Stack AI App ($800, or $350/month), an Enterprise Full-Stack Agentic AI build with multi-agent orchestration, agentic RAG, evals and observability ($2,000, or $800/month), and a Custom Product scoped and quoted on a call. Payments are processed worldwide via Paddle.

Why do most enterprise AI agent pilots fail to reach production?

Research shows only 11–14% of enterprise AI agent pilots reach production at scale. The primary reasons are orchestration complexity, inadequate evaluation and output validation, LLMOps debt, poor retrieval quality in RAG pipelines, and lack of governance controls. These are the exact failure modes Ahmed's engagements are designed to address.

Does Ahmed Sheikh work with medium and large businesses globally?

Yes. Ahmed works with medium and large businesses worldwide, remotely. He delivers fixed-price AI application builds with optional monthly retainers — including full-stack AI apps, enterprise agentic AI systems with multi-agent orchestration via LangGraph, agentic RAG implementation, and LLMOps infrastructure.

What technologies does Ahmed Sheikh use for agentic AI systems?

Ahmed builds agentic AI systems using LangGraph for multi-agent orchestration, LangChain for RAG pipelines, Python and FastAPI for backend services, Next.js and TypeScript for interfaces, and cloud-native infrastructure on Vercel, AWS, and Docker/Kubernetes. He also designs evaluation harnesses, output validation layers, and LLMOps monitoring stacks.

TrueEars AI — Context-Aware Voice Dictation

TL;DR

TrueEars is a native Windows dictation app built with Rust and Tauri. Press Ctrl+Shift+K from any application, speak, and the app detects your context (Slack, VS Code, Gmail, etc.) and injects properly formatted text. Voice data goes directly from your device to your chosen API provider — never to TrueEars servers. 15 MB installed. ~50 ms to start. 100+ languages.

The Problem

Voice dictation for developers and knowledge workers has three persistent failure modes in 2025:

—Privacy risk: Cloud-first tools route audio through company servers. For proprietary code, legal documents, or sensitive communication, this is a non-starter.
—Bloat: Electron-based dictation apps routinely consume 150–300 MB of RAM and take several seconds to cold-start — worse than just typing.
—Context blindness: Existing tools produce identical output regardless of destination. A dictated Slack message looks like a legal brief; a dictated code comment lacks syntax awareness.

The Solution

TrueEars addresses all three failure modes simultaneously:

100% Local-First

BYOK architecture — you supply your own API key (OpenAI, Groq, or Anthropic). Audio goes from your device directly to your provider. TrueEars never touches your data.

Rust + Tauri

Not Electron. Tauri uses the OS webview rather than bundling Chromium, yielding a 15 MB install and ~50 ms startup. The core processing layer is native Rust.

Context-Aware Engine

Detects the active application via OS APIs and applies output rules: casual tone for Slack, professional for email, code formatting for VS Code. One hotkey, many contexts.

Architecture

The transcription pipeline runs entirely on-device except for the API call:

Global hotkey (Ctrl+Shift+K)

OS-level hook, works from any window

Audio capture

Native Tauri audio API — no intermediate process

API transcription

Your key → OpenAI / Groq / Anthropic — direct, no proxy

Context detection

Reads active window title and process name

Output post-processing

Style adjustment, filler-word removal, grammar correction

Text injection

Inserts into active input field via OS accessibility APIs

Tech Stack

Rust

Core processing engine

Tauri

Native app shell (not Electron)

TypeScript

UI layer

OpenAI API

Transcription provider (BYOK)

Groq

Low-latency transcription provider

Anthropic

Transcription provider (BYOK)

Windows 10/11

Target platform

Why Tauri, Not Electron?

Electron bundles a full Chromium instance with every app. For a dictation tool that runs constantly in the background, a 200 MB memory footprint and 3–5 second startup are dealbreakers.

Tauri uses the OS-native webview (WebView2 on Windows), which is already loaded in memory. The result: a 15 MB install, ~50 ms cold start, and a RAM footprint 93% smaller than comparable Electron alternatives. The core transcription and injection logic is Rust, giving native performance where it matters.

Electron alternative

~200 MB

RAM · 3–5 s startup

TrueEars (Tauri)

15 MB

RAM · ~50 ms startup

Key Features

—Context-aware engine — detects active app and adapts output style automatically
—Global hotkey (Ctrl+Shift+K) — activates from any window, any application
—100+ languages — English, Spanish, French, German, Japanese, Chinese, Arabic, and more
—BYOK — bring your own OpenAI, Groq, or Anthropic API key
—Grammar auto-correction — removes filler words ("um", "uh") and corrects transcription errors
—100% local-first — voice data goes directly to your API provider, never to TrueEars
—Free tier + $29 one-time Supporter licence

Frequently Asked

Is TrueEars private? Does it store my voice data?

TrueEars is 100% local-first. You supply your own API key (BYOK). Audio goes directly from your device to your chosen provider (OpenAI, Groq, or Anthropic) — TrueEars servers never receive your voice data.

Why is TrueEars 15 MB when other dictation tools are 200 MB+?

TrueEars is built with Tauri instead of Electron. Tauri uses the OS-native webview (already in memory on Windows) rather than bundling a full Chromium instance. The core processing logic is Rust, not a Node.js runtime. The result is a 15 MB install and ~50 ms startup — 93% leaner than Electron-based alternatives.

What does 'context-aware' mean in TrueEars?

The context-aware engine reads your active window and adjusts the output style before injecting text. Speaking to Slack produces casual, conversational text. Speaking to your email client produces professional prose. Speaking inside VS Code produces properly formatted code comments. One hotkey, one voice input — many appropriate outputs.

What API providers does TrueEars support?

TrueEars supports OpenAI, Groq, and Anthropic via BYOK (Bring Your Own Key). You connect your own API key in settings; TrueEars never touches your credentials beyond local storage.

TrueEars —Voice Dictation