Shadow

Overview

Shadow is an AI-powered meeting assistant that captures both audio and visual context from video meetings to turn conversations into actionable workflows. Unlike traditional meeting tools that require bots to join calls, Shadow operates silently in the background on a user’s device, recording discussions and automatically generating structured notes, action items, and transcripts. The platform’s unique value proposition centers on context completeness—capturing what’s said and what’s shown—and immediate post-meeting automation that delivers tangible outcomes rather than just documentation.

Key Features

• No-Bot Design: Operates entirely on the user’s device without joining meetings as a participant, maintaining natural conversation flow and eliminating awkward bot notifications
• Dual-Input Capture: Records both audio conversation and screen content simultaneously to capture visual context (whiteboards, designs, code, shared documents) alongside dialogue
• Automatic Meeting Detection: Detects when users join Zoom, Google Meet, Microsoft Teams, Slack Huddle, and other meeting platforms without manual start/stop
• Local Transcription: Processes audio transcription locally on-device for privacy; sensitive data never transmitted for transcription processing
• Multi-Speaker Identification: Automatically detects and labels different speakers with real-time voice recognition
• AI-Powered Workflow Automation: Generates customizable AI tasks for post-meeting actions including email drafting, CRM updates, proposal creation, and report generation
• Searchable Knowledge Base: Transforms entire meeting history into a searchable “second brain” for finding past decisions, discussions, and context
• Pre-Engineered Prompts: Role-specific skills (sales BANT extraction, project timelines, technical decisions) with one-click activation
• Integration Ecosystem: Native support for Obsidian, Notion, Slack, and standard webhook integrations for workflow automation

How It Works

Shadow installs as a macOS application that runs continuously in the background. When a user joins a video meeting on any supported platform, Shadow automatically detects it and begins recording both audio and screen content locally on the device. The transcription occurs on-device using local processing, then the raw meeting data is structured by AI to extract action items, identify speakers, and summarize key points. Users can trigger custom workflows that automatically execute post-meeting tasks—drafting follow-up emails, updating CRM fields, generating reports—all based on conversation and visual content. The entire meeting history becomes searchable through Shadow’s chat interface, enabling users to query past discussions like “What was decided about Q3 budget?”

Use Cases

• Sales Teams: Automatically captures BANT (Budget, Authority, Need, Timing) criteria from client calls and drafts CRM updates or follow-up proposals
• Engineering Standups: Records technical decisions, architectural discussions, and debugging sessions with full screen and audio context for knowledge preservation
• Design Reviews: Captures visual feedback, design iterations discussed, and decisions made during collaborative reviews with complete visual context
• Project Management: Automatically generates meeting summaries, tracks action items with assignees, and updates project management systems
• Executive Leadership: Produces high-level summaries of strategic discussions and decision documents without drowning in meeting minutiae
• Asynchronous Communication: Processes pre-recorded audio and videos for teams across time zones who cannot attend live meetings

Pros \& Cons

Advantages

• Captures Visual Context: Records screen content alongside audio, addressing the gap where traditional meeting tools miss what’s displayed
• No Awkward Bots: Operates silently in background; participants never see or interact with AI presence, maintaining natural conversation
• Complete Task Automation: Performs post-meeting tasks up to 20x faster than manual execution (drafting emails, updating CRMs, creating reports)
• Privacy-First Architecture: Local-on-device processing and storage means sensitive conversation content never leaves user’s device for transcription
• Automatic Operation: Detects meetings and starts recording without manual intervention; “set it and forget it” approach
• Searchable Knowledge Base: Transforms scattered meeting history into unified, queryable institutional memory

Disadvantages

• Screen Recording Privacy Concerns: Capturing screen content may raise concerns in regulated industries or when sensitive client information is displayed; requires explicit team awareness
• Pricing Not Publicly Listed: Pricing structure not transparent in summary; requires visiting website or contacting sales for details
• macOS-Only (Initially): Currently Mac-focused with Windows support mentioned as future roadmap item
• Learning Curve for Workflows: Customizing AI tasks and automations requires understanding workflow configuration
• Early Stage: Relatively new product (founded 2022) with continuously evolving features and potential stability issues

How Does It Compare?

Otter.ai

• Key Features: Real-time transcription, AI meeting summaries, action item extraction, OtterPilot for Sales CRM integration, 300+ monthly transcription minutes
• Strengths: Real-time transcription during calls, established market leader, wide integrations with Salesforce and HubSpot, proven reliability
• Limitations: Requires bot to join meeting, no screen capture, limited automation of post-meeting tasks, primarily transcription and note-focused
• Differentiation: Otter.ai excels at real-time transcription and note-taking; Shadow captures visual context and automates post-meeting workflows

Fireflies.ai

• Key Features: Real-time transcription, speaker identification, sentiment analysis, conversation intelligence, CRM integration, 50+ hour monthly transcription
• Strengths: Excellent sentiment analysis, conversation intelligence features, strong transcription quality, good CRM integrations
• Limitations: Requires bot in meetings, no screen capture capability, less emphasis on visual context, primarily analytics-focused
• Differentiation: Fireflies focuses on conversation analytics and sentiment; Shadow emphasizes visual context and task automation

Rewind.ai

• Key Features: Personal knowledge manager, screenshot capture, meeting recording, cross-application search, local processing, privacy-first design
• Strengths: Comprehensive screen recording and search, strong privacy focus with local processing, captures browser and application context
• Limitations: Broader personal knowledge management tool (not meeting-specific), less automated workflow generation, requires different interaction model
• Differentiation: Rewind is a general personal knowledge database; Shadow specializes in meeting-driven automation and workflow execution

Fellow

• Key Features: Meeting notes and agendas, collaborative meeting management, action tracking, agenda templates, meeting analytics
• Strengths: Collaborative note-taking, strong agenda management, action item tracking, meeting effectiveness analytics
• Limitations: More manual note-taking focused, less AI automation, limited visual context capture, requires active participation
• Differentiation: Fellow emphasizes collaborative meeting management; Shadow automates post-meeting task execution

Jamie

• Key Features: Privacy-first meeting assistant, offline capability, detailed summaries, works with any video conference tool, no bot required
• Strengths: Strong privacy features, works offline, no bot required, comprehensive note generation
• Limitations: Limited visual context capture, less advanced automation, smaller feature set compared to Shadow
• Differentiation: Jamie focuses on privacy and offline capability; Shadow adds visual context capture and advanced workflow automation

Final Thoughts

Shadow successfully differentiates itself in the crowded meeting assistant space by capturing the complete meeting context—both what’s said and what’s shown—and translating that understanding into concrete post-meeting action. The no-bot design and local processing address two critical pain points: the awkwardness of AI participants in meetings and the privacy concerns around sensitive data transmission.

The platform’s strength lies in closing the gap between meeting understanding and action execution. Rather than simply creating better notes, Shadow automates the downstream work that typically follows meetings: email drafting, CRM updates, proposal generation, and report creation. Benchmark claims of 20x faster task completion suggest meaningful productivity gains for high-meeting-volume professionals.

However, potential concerns around screen recording privacy and the non-transparent pricing structure warrant careful evaluation before team deployment. Managers should ensure team comfort with visual context capture and verify pricing fits budget requirements.

For sales teams, engineering organizations, and product-focused companies where meetings drive decision-making and screen content (whiteboards, designs, proposals) is essential context, Shadow offers compelling value. The privacy-first architecture and automatic operation make it particularly suitable for teams prioritizing data security and frictionless workflows.

As the platform evolves with Windows support and additional automation capabilities, Shadow is well-positioned to become a standard tool in meeting-heavy industries where the ability to capture complete context and automate follow-up directly impacts productivity and decision execution velocity.