OpenAdapt.ai Landing Page Strategy¶

Document Version: 1.0 Date: January 2026 Author: Generated with AI assistance Status: Proposal for Review

Table of Contents¶

1. Current State Analysis
2. Target Audience Definitions
3. Core Messaging Strategy
4. Competitive Positioning
5. Page Section Recommendations
6. Copy Suggestions
7. Wireframe Concepts
8. Social Proof Strategy
9. Call-to-Action Strategy
10. Implementation Priorities

1. Current State Analysis¶

1.1 What OpenAdapt IS Today¶

OpenAdapt has evolved from a monolithic application (v0.46.0) to a modular meta-package architecture (v1.0+). This is a significant architectural maturation that should be reflected in messaging.

Core Value Proposition (Current Reality): - The open source software **adapt**er between Large Multimodal Models (LMMs) and desktop/web GUIs - Record demonstrations, train models, evaluate agents via unified CLI - Works with any VLM: Claude, GPT-4V, Gemini, Qwen, or custom fine-tuned models

Technical Differentiators (Verified): 1. Model Agnostic: Not locked to one AI provider 2. Demo-Prompted, Not User-Prompted: Learn from human demonstration, not complex prompt engineering 3. Universal GUI Support: Native apps, web browsers, virtualized environments 4. Open Source (MIT License): Full transparency, no vendor lock-in

Key Innovation: - Trajectory-conditioned disambiguation of UI affordances - validated experiment showing 33% -> 100% first-action accuracy with demo conditioning - Set-of-Marks (SoM) mode: 100% accuracy on synthetic benchmarks using element IDs instead of coordinates

1.2 Current Landing Page Assessment¶

What's Working Well: - Clean, professional design with dark theme - Video demo at hero section - GitHub star/fork buttons for social proof - Platform-specific installation instructions (auto-detects OS) - PyPI download statistics showing traction - Industry use cases grid (HR, Law, Insurance, etc.) - Email signup for updates

What's Missing or Unclear: 1. No clear "what is this?" - Visitors need to watch a video to understand 2. Tagline "AI for Desktops" is vague - Doesn't differentiate from competitors 3. No comparison to alternatives - Why choose OpenAdapt over Anthropic Computer Use? 4. No technical credibility indicators - No benchmark scores, no research citations 5. Industry grid is generic - Same features could apply to any automation tool 6. No developer/researcher angle - Focuses only on end-user automation 7. Architecture transition is hidden - v1.0+ modular design is a major selling point 8. No clear "Who is this for?" - Tries to appeal to everyone, resonates with no one

Carousel Messages Analysis: - "Show, don't tell." - Good but cryptic - "Perform, don't prompt." - Best differentiator, should be prominent - "Record, replay, and share." - Functional but not compelling

1.3 Technical Accuracy Issues¶

The current site doesn't reflect: - The modular package architecture (7 focused sub-packages) - The evaluation infrastructure (WAA, WebArena benchmarks) - The ML training capabilities (VLM fine-tuning, LoRA) - The retrieval-augmented prompting (demo library search) - The privacy scrubbing capabilities (PII/PHI redaction)

2. Target Audience Definitions¶

2.1 Primary Audiences¶

A. Developers Building Automation Agents¶

Profile: - Building AI-powered tools that interact with GUIs - May be creating internal tools, startup products, or client solutions - Comfortable with Python, CLI tools, ML concepts - Want flexibility, not black-box solutions

Pain Points: - API-only agents (Claude Computer Use) lack customization - Building from scratch is too slow - Need to run locally for privacy/security - Want to fine-tune models on specific workflows

What They Need to See: - Clear architecture diagrams - Code examples (pip install, quick start) - Benchmark scores vs. alternatives - Extensibility points (adapters, plugins)

Key Message: "The open source SDK for building GUI automation agents"

B. Enterprise Process Automation Buyers¶

Profile: - Looking to automate repetitive knowledge work - Concerned about security, privacy, compliance - Need to justify ROI and integrate with existing systems - Often have IT/security review requirements

Pain Points: - Existing RPA is brittle and expensive to maintain - Cloud-only AI raises data privacy concerns - Need clear enterprise support options - Require compliance with industry regulations

What They Need to See: - Privacy features (PII/PHI scrubbing) - On-premise deployment options - Enterprise support/contact information - Industry-specific use case studies - Security architecture information

Key Message: "AI-first automation that runs where your data lives"

C. ML Researchers Studying GUI Agents¶

Profile: - Academic researchers or industry R&D teams - Working on VLM capabilities, agent architectures, benchmarks - Need reproducible baselines and evaluation infrastructure - Want to contribute to or build upon open research

Pain Points: - Existing benchmarks are hard to set up - Need standardized evaluation metrics - Want to compare models fairly - Limited open-source alternatives to proprietary agent frameworks

What They Need to See: - Benchmark integration (WAA, WebArena, OSWorld) - Published metrics and methodology - Research paper citations (if any) - Clear contribution pathways - Schema/data format documentation

Key Message: "Open infrastructure for GUI agent research and benchmarking"

D. ML Engineers Interested in VLM Fine-Tuning¶

Profile: - Want to train custom models for specific GUI tasks - Familiar with training infrastructure (LoRA, PEFT, etc.) - Looking for training data and pipelines - Want efficient local or cloud training options

Pain Points: - Collecting GUI interaction data is tedious - Setting up VLM training pipelines is complex - Need baselines to compare against - Cloud GPU costs add up quickly

What They Need to See: - Training pipeline documentation - Supported models (Qwen3-VL, etc.) - Training results (before/after fine-tuning) - Cloud GPU integration (Lambda Labs, Azure) - Data format specifications

Key Message: "Record demonstrations, train specialized GUI agents"

2.2 Audience Prioritization¶

For the landing page, prioritize in this order: 1. Developers (highest volume, most likely to convert to users/contributors) 2. Enterprise buyers (revenue potential, require dedicated section) 3. ML engineers (overlaps with developers, training angle) 4. Researchers (smaller audience, but important for credibility)

3. Core Messaging Strategy¶

3.1 Primary Tagline Options¶

Option A (Recommended):

"Teach AI to use any software."

Why: Simple, benefit-focused, implies the key differentiator (demonstration-based learning)

Option B:

"The open source adapter between AI and any GUI."

Why: Explains the technical position, highlights open source

Option C:

"Perform, don't prompt."

Why: Clever contrast to prompt engineering, memorable

Option D:

"Record. Train. Automate."

Why: Clear 3-step process, action-oriented

3.2 Supporting Taglines (Subheadlines)¶

• "Show AI how to do a task once. Let it handle the rest."
• "From human demonstration to AI automation in minutes."
• "Open source GUI automation with the AI model of your choice."
• "Works with Claude, GPT-4V, Gemini, Qwen, or your own fine-tuned models."

3.3 Key Differentiators to Emphasize¶

1. Demonstration-Based Learning
2. Not: "Use natural language to describe tasks"
3. But: "Just do the task and OpenAdapt learns from watching"

4. Proof: 33% -> 100% first-action accuracy with demo conditioning

5. Model Agnostic

6. Not: "Works with [specific AI]"
7. But: "Your choice: Claude, GPT-4V, Gemini, Qwen, or custom models"

8. Proof: Adapters for multiple VLM backends

9. Runs Anywhere

10. Not: "Cloud-powered automation"
11. But: "Run locally, in the cloud, or hybrid"

12. Proof: CLI-based, works offline

13. Open Source

14. Not: "Try our free tier"
15. But: "MIT licensed, fully transparent, community-driven"
16. Proof: GitHub, PyPI, active Discord

3.4 Messaging Framework¶

For Developers:

"Build GUI automation agents with a modular Python SDK. Record demonstrations, train models, evaluate on benchmarks. Works with any VLM."

For Enterprise:

"AI-first process automation that learns from your team. Privacy-first architecture with PII/PHI scrubbing. Deploy where your data lives."

For Researchers:

"Open infrastructure for GUI agent research. Standardized benchmarks, reproducible baselines, extensible architecture."

For ML Engineers:

"Fine-tune VLMs on real GUI workflows. Record data, train with LoRA, evaluate accuracy. Local or cloud training."

4. Competitive Positioning¶

4.1 Primary Competitors¶

4.2 Positioning Statement¶

"OpenAdapt is the open source alternative to proprietary GUI automation APIs. Unlike cloud-only solutions, OpenAdapt lets you train custom models on your workflows and deploy anywhere. Unlike traditional RPA, OpenAdapt uses AI reasoning and learns from demonstrations instead of brittle scripts."

4.3 Comparison Talking Points¶

vs. Anthropic Computer Use: - "Model-agnostic - not locked to one provider" - "Fine-tune on your specific workflows" - "Run locally for privacy-sensitive data" - "Open source with community contributions"

vs. Traditional RPA: - "AI understands intent, not just element selectors" - "Adapts to UI changes without manual updates" - "Learn from demonstrations, not scripting" - "Fraction of the cost, faster to deploy"

5. Page Section Recommendations¶

5.1 Proposed Page Structure¶

1. Hero Section (Above the fold)
2. How It Works (3-step process)
3. Key Differentiators (3-4 value props)
4. For Developers (SDK/CLI features)
5. For Enterprise (Security, privacy, support)
6. Use Cases (Specific, concrete examples)
7. Comparison (Why OpenAdapt)
8. Social Proof (Metrics, testimonials, logos)
9. Getting Started (Install, docs, community)
10. Footer (Links, legal, contact)

5.2 Hero Section Redesign¶

Current: "OpenAdapt.AI - AI for Desktops. Automate your workflows. No coding required."

Proposed:

[Logo] OpenAdapt.AI

# Teach AI to use any software.

Show it once. Let it handle the rest.

[Video Demo - Keep current]

[Install in 30 seconds] [View on GitHub] [Join Discord]

"Works with Claude, GPT-4V, Gemini, Qwen, or your own fine-tuned models"

{GitHub Stars} {PyPI Downloads} {Discord Members}

5.3 How It Works Section¶

Current: Carousel with "Show, don't tell" / "Perform, don't prompt" / "Record, replay, share"

Proposed: Clear 3-step process with visuals

## How OpenAdapt Works

1. RECORD
   [Icon: Screen recording]
   Demonstrate the task by doing it yourself.
   OpenAdapt captures screenshots, mouse clicks, and keystrokes.

2. TRAIN
   [Icon: Neural network]
   Train an AI model on your demonstration.
   Fine-tune Qwen-VL, use Claude/GPT-4V, or bring your own model.

3. DEPLOY
   [Icon: Play button]
   Run the trained agent to automate the task.
   Evaluate with standardized benchmarks.

5.4 Differentiators Section¶

## Why OpenAdapt?

### Demonstration-Based Learning
No prompt engineering required. OpenAdapt learns from how you actually do tasks.
[Stat: 33% -> 100% first-action accuracy with demo conditioning]

### Model Agnostic
Your choice of AI: Claude, GPT-4V, Gemini, Qwen-VL, or fine-tune your own.
Not locked to any single provider.

### Run Anywhere
CLI-based, works offline. Deploy locally, in the cloud, or hybrid.
Your data stays where you want it.

### Fully Open Source
MIT licensed. Transparent, auditable, community-driven.
No vendor lock-in, ever.

5.5 For Developers Section¶

## Built for Developers

### Modular Architecture
Seven focused packages you can install individually:
- openadapt-capture: Recording
- openadapt-ml: Training & inference
- openadapt-evals: Benchmarking
- openadapt-viewer: Visualization
- openadapt-grounding: UI element detection
- openadapt-retrieval: Demo library search
- openadapt-privacy: PII/PHI scrubbing

### Quick Start
```bash
# Install
pip install openadapt[all]

# Record a demonstration
openadapt capture start --name my-task

# Train a model
openadapt train start --capture my-task --model qwen3vl-2b

# Evaluate
openadapt eval run --checkpoint model.pt --benchmark waa
```

### Benchmark Ready
Integrated with Windows Agent Arena (WAA), WebArena, and OSWorld.
Compare your models against published baselines.

[View Documentation] [GitHub Repository]

5.6 For Enterprise Section¶

## Enterprise-Ready Automation

### Privacy First
Built-in PII/PHI scrubbing with AWS Comprehend, Microsoft Presidio, or Private AI.
Your sensitive data never leaves your infrastructure.

### Deploy Your Way
Run entirely on-premise, in your cloud, or hybrid.
No data leaves your environment unless you want it to.

### Compliance Ready
Audit logging, reproducible recordings, explainable AI decisions.
Built for regulated industries.

### Enterprise Support
Custom development, training, and support packages available.

[Contact Sales: sales@openadapt.ai]

5.7 Use Cases Section (Refined)¶

Current: Generic industry grid

Proposed: Specific, concrete use cases with workflows

## Real-World Automation

### Data Entry Across Systems
Transfer information between applications that don't integrate.
Example: Copy customer data from CRM to billing system.

### Report Generation
Compile data from multiple sources into standardized reports.
Example: Monthly sales reports from Salesforce + Excel + internal tools.

### Legacy System Integration
Automate workflows in applications without APIs.
Example: Mainframe data entry, proprietary healthcare systems.

### Quality Assurance Testing
Record manual test procedures, replay with validation.
Example: Regression testing across UI updates.

### Process Documentation
Record workflows to create training materials automatically.
Example: Onboarding guides for complex internal tools.

6. Copy Suggestions¶

6.1 Headlines¶

6.2 CTAs (Calls to Action)¶

6.3 Proof Points to Include¶

• "33% -> 100% first-action accuracy with demonstration conditioning"
• "[X,XXX] PyPI downloads this month" (dynamic)
• "[XXX] GitHub stars" (dynamic)
• "7 modular packages, 1 unified CLI"
• "Integrated with Windows Agent Arena, WebArena, OSWorld benchmarks"
• "MIT licensed, fully open source"

7. Wireframe Concepts¶

7.1 Desktop Layout¶

+------------------------------------------------------------------+
|  [Logo]                    [Docs] [GitHub] [Discord] [Enterprise] |
+------------------------------------------------------------------+
|                                                                    |
|  # Teach AI to use any software.                                   |
|  Show it once. Let it handle the rest.                            |
|                                                                    |
|  [==================== Video Demo ====================]           |
|                                                                    |
|  [Get Started]  [View on GitHub]                                  |
|                                                                    |
|  Works with: [Claude] [GPT-4V] [Gemini] [Qwen] [Custom]           |
|                                                                    |
|  [GitHub Stars]  [PyPI Downloads]  [Discord Members]              |
|                                                                    |
+------------------------------------------------------------------+
|                                                                    |
|  ## How OpenAdapt Works                                           |
|                                                                    |
|  [1. RECORD]     [2. TRAIN]      [3. DEPLOY]                      |
|  [Screenshot]    [Neural Net]    [Automation]                     |
|  Demonstrate     Train on your   Run the agent                    |
|  the task.       demonstration.  to automate.                     |
|                                                                    |
+------------------------------------------------------------------+
|                                                                    |
|  ## Why OpenAdapt?                                                |
|                                                                    |
|  [Demo-Based]    [Model Agnostic]  [Run Anywhere]  [Open Source]  |
|  Learn from      Your choice of    Local, cloud,   MIT licensed   |
|  examples.       AI provider.      or hybrid.      forever.       |
|                                                                    |
+------------------------------------------------------------------+
|                                                                    |
|  [For Developers Tab]  [For Enterprise Tab]  [For Researchers Tab]|
|                                                                    |
|  Content switches based on selected audience...                   |
|                                                                    |
+------------------------------------------------------------------+
|                                                                    |
|  ## Get Started                                                   |
|                                                                    |
|  [macOS] [Windows] [Linux]                                        |
|                                                                    |
|  $ curl -LsSf https://astral.sh/uv/install.sh | sh               |
|  $ uv tool install openadapt                                      |
|  $ openadapt --help                                               |
|                                                                    |
|  [X,XXX installs this month]                                      |
|                                                                    |
+------------------------------------------------------------------+
|                                                                    |
|  [Footer: Links, Social, Legal]                                   |
|                                                                    |
+------------------------------------------------------------------+

7.2 Mobile Considerations¶

• Stack hero elements vertically
• Collapse model logos into scrollable row
• Use accordion for audience tabs
• Keep video demo prominent
• Simplify code blocks (single command with copy button)

8. Social Proof Strategy¶

8.1 Metrics to Display¶

Live Metrics (fetch from APIs): - GitHub stars (currently showing, keep) - PyPI downloads per month (currently showing, keep) - Discord member count (add if available) - Number of GitHub contributors (add)

Static Metrics (update manually): - "7 modular packages" - "100% synthetic benchmark accuracy (SoM mode)" - "3 benchmark integrations (WAA, WebArena, OSWorld)"

8.2 Testimonials Strategy¶

Priority Order: 1. Named enterprise user quotes (if available) 2. Named developer testimonials from Discord 3. Anonymous industry testimonials 4. Community member quotes

Template for Gathering:

"How has OpenAdapt helped you? Reply to be featured on our website."

8.3 Logo Wall¶

Target logos to seek permission for: - Companies using OpenAdapt in production - Universities using for research - Partner organizations

Fallback (if no logos available): - Featured in media logos (if covered) - Integration partner logos (AWS, Azure, etc.) - "Trusted by teams at Fortune 500 companies" (if true)

9. Call-to-Action Strategy¶

9.1 Primary Conversion Goals¶

1. GitHub star (low friction, high visibility)
2. PyPI install (product usage)
3. Discord join (community engagement)
4. Email signup (for updates)
5. Enterprise contact (revenue)

9.2 CTA Placement¶

9.3 Email Capture Strategy¶

Current: "Register for updates"

Proposed: More specific value prop - "Get early access to new features" - "Join [X,XXX] developers automating with AI" - "Subscribe to the OpenAdapt newsletter (monthly, no spam)"

10. Implementation Priorities¶

10.1 Phase 1: Quick Wins (1-2 weeks)¶

1. Update hero tagline to "Teach AI to use any software."
2. Add "How It Works" section with 3-step process
3. Update differentiators to 4-card grid (current features but better copy)
4. Add Discord member count to social proof
5. Add GitHub contributors count

10.2 Phase 2: Messaging Clarity (2-4 weeks)¶

1. Add "For Developers" section with code examples and architecture
2. Add "For Enterprise" section with privacy/security messaging
3. Replace generic industry grid with specific use case examples
4. Add comparison table vs. alternatives
5. Update email signup copy to be more specific

10.3 Phase 3: Credibility Building (4-8 weeks)¶

1. Add benchmark scores (once published)
2. Collect and display testimonials
3. Create case studies (1-2 real examples)
4. Add logo wall (if logos available)
5. Add "Research" or "Publications" section (if applicable)

10.4 Phase 4: Conversion Optimization (Ongoing)¶

1. A/B test hero messaging
2. Track install conversion rates
3. Optimize CTA placement
4. Add video transcripts/captions for SEO
5. Create landing page variants for different audiences (developer vs. enterprise)

Appendix A: Messaging Don'ts¶

• Don't say "AI for Desktops" - too vague, doesn't differentiate
• Don't say "No coding required" - true for end users, but alienates developers
• Don't list every industry - pick 3-4 with real stories
• Don't hide the CLI - developers want to see it
• Don't over-promise - be honest about current capabilities

Appendix B: Technical Content to Add¶

1. Architecture diagram showing package relationships
2. Mermaid flowchart of Record -> Train -> Deploy cycle
3. Comparison table of model backends (Claude, GPT, Qwen, etc.)
4. Benchmark table showing accuracy scores
5. API reference link to documentation site

Appendix C: SEO Keywords¶

Primary: - "GUI automation AI" - "desktop automation AI" - "RPA alternative AI" - "VLM GUI agent" - "open source computer use"

Secondary: - "train AI on screenshots" - "demonstration-based automation" - "model-agnostic automation" - "Claude computer use alternative" - "AI workflow automation"

This document is a living strategy guide. Updates should be made as OpenAdapt capabilities evolve and as user feedback is collected.