Capacity Review: The AI Workflow Engine That Actually Understands Vibe Coding

Look, I need to be upfront about something before we dive into this: I'm skeptical of productivity tools that promise to "revolutionize your workflow." I've been burned too many times by platforms that sound incredible in demos but fall apart the moment you try to do something they didn't anticipate. The graveyard of "game-changing" SaaS tools I've abandoned is embarrassingly large.

But I'm also honest enough to admit when something genuinely delivers. And Capacity—despite my initial cynicism—has become the kind of tool that makes me rethink how I approach building software. Not because it's magic. Not because it eliminates thinking. But because it finally understands what developers like me actually need: a way to translate clear specifications into repeatable, reliable workflows without rebuilding everything from scratch every single time.

This isn't a sponsored post. I'm not getting paid to write this. What I am doing is sharing a deep dive into a platform that's solving real problems for people who work the way I work—document-driven, AI-assisted, focused on outcomes rather than performance coding theater.

What Capacity Actually Is (And Why Most People Get It Wrong)

Here's what Capacity isn't: it's not another ChatGPT wrapper with a fancy interface. It's not a code generator that spits out mediocre boilerplate. It's not trying to be "AI for everything."

What Capacity is: a workflow automation platform built around the idea that if you can articulate what you want clearly enough, the system should be able to execute it reliably, repeatedly, and without constant hand-holding.

Think of it like this: you know how document-driven development works? You write comprehensive specs, clear requirements, defined patterns—and then you use those specifications to guide implementation, whether that implementation happens through AI agents, human developers, or some combination?

Capacity is what happens when you take that philosophy and bake it directly into the tooling. Instead of fighting with prompts, context windows, and trying to remember what worked last time, you're building workflows—reusable, shareable, version-controlled processes that capture your best thinking and make it executable.

And here's the part that made me actually pay attention: it's not trying to replace your technical judgment. It's trying to amplify it. The platform assumes you know what you're trying to accomplish. It just removes the friction between "here's what needs to happen" and "here's the working result."

The Core Features That Actually Matter

Let me break down what Capacity offers, but I'm going to skip the marketing fluff and focus on what these features mean in practice for someone building real software.

1. Workflow Automation That Respects Context

What the marketing says: "Build automated workflows with AI assistance."

What it actually means: You can create multi-step processes where each step can access the context from previous steps, call external APIs, transform data, and make decisions based on real outputs—not just predefined if/then logic.

Here's why this matters: I spend a huge amount of time in my document-driven development workflow doing repetitive tasks that require some intelligence but not constant attention. Things like:

• Taking requirements docs and generating initial API specifications
• Converting user stories into test scenarios
• Analyzing code for security patterns and generating compliance documentation
• Transforming technical specs into client-friendly summaries
• Creating deployment checklists based on architecture decisions

These aren't tasks you want to do manually, but they're also not tasks you can hand off to a dumb automation tool. You need context awareness. You need the ability to reference multiple documents. You need intelligence that adapts to the specific inputs rather than just running a script.

Capacity handles this by letting you build workflows that maintain state, pass data between steps, and leverage AI models (including your own local models) to make informed decisions at each stage.

Practical example: I've built a workflow that takes a requirements document, extracts the security-critical sections, checks them against OWASP Top 10 standards, generates specific implementation recommendations, and outputs a security implementation checklist—all in about 30 seconds. Doing this manually used to take me an hour and required keeping multiple browser tabs open.

2. Knowledge Base Integration That Doesn't Suck

What the marketing says: "Connect your data sources and give AI access to your knowledge base."

What it actually means: You can feed Capacity documentation, code repositories, Notion pages, Google Docs, whatever—and the workflows can actually use that information intelligently, not just regurgitate it.

This is huge for document-driven development because your specifications aren't static. They evolve. Your architecture docs get updated. Your security requirements change. Your standards documents get refined.

With Capacity, when you update your source documentation, workflows that reference that documentation automatically work with the new information. You're not constantly updating prompts or rebuilding context. The system knows where to look.

What this replaces:

• Manually copying documentation into ChatGPT
• Maintaining separate context files for different AI tools
• Repeatedly explaining the same architectural decisions
• Writing custom scripts to parse and inject context

Practical example: I have all my standard documentation templates (requirements.md, architecture.md, security.md, etc.) stored in Capacity's knowledge base. When I start a new project, workflows automatically reference these templates, extract relevant patterns, and apply them to the specific project context. It's like having an experienced developer who's read all your documentation and actually remembers it.

3. API Integrations That Handle Real-World Complexity

What the marketing says: "Connect to thousands of apps and services."

What it actually means: You can call REST APIs, handle authentication, manage rate limits, parse responses, and chain multiple API calls together—all within your workflows, with proper error handling.

Look, I've used Zapier. I've used IFTTT. I've used Make. They're all fine for simple integrations, but they fall apart the moment you need to do something slightly complex, like:

• Call an API, parse the JSON response, transform the data, and use it in a subsequent call
• Handle OAuth flows that require token refresh
• Implement exponential backoff for rate-limited endpoints
• Work with APIs that return paginated results

Capacity treats API integrations as first-class citizens. You're not fighting with limited visual builders or trying to squeeze logic into pre-defined boxes. You define the integration once, test it, and then use it across workflows.

Practical example: I have a workflow that monitors GitHub repositories for new issues, analyzes them using a local LLM to categorize priority, checks them against project requirements documentation, and generates initial response templates—all coordinated through API calls with proper error handling and retry logic. This would have taken days to build with traditional automation tools.

4. Prompt Engineering That Persists

What the marketing says: "Optimize your AI prompts for better results."

What it actually means: You can craft, test, version, and share prompts that work—and you never have to rewrite them from scratch.

Here's a truth about working with AI: most of the value comes from figuring out exactly how to ask for what you want. The problem is that this knowledge is ephemeral. You craft the perfect prompt, use it a few times, then six weeks later you can't remember exactly how you phrased it to get good results.

Capacity solves this by treating prompts as reusable components. You build them once, test them until they work reliably, and then reference them in workflows. When you discover a better prompt pattern, you update the component—and every workflow using it automatically improves.

This is especially valuable for document-driven development because the prompts you need are specific and nuanced. It's not just "write me a React component." It's "analyze this requirements document, extract the authentication requirements, cross-reference with our security standards, and generate a specification for an authentication middleware that follows our established patterns."

Getting that prompt right takes iteration. But once you've got it, you shouldn't have to rebuild it every time you need it.

5. Version Control and Collaboration That Makes Sense

What the marketing says: "Share workflows with your team."

What it actually means: Workflows are treated like code—you can version them, fork them, merge changes, and actually collaborate without everyone stepping on each other's toes.

This matters because workflow automation isn't a solo activity in any serious development environment. Multiple people need to build workflows. Workflows need to evolve as requirements change. You need to be able to test changes without breaking production workflows.

Capacity handles this with actual version control, not just "save a copy" functionality. You can branch workflows, test changes in isolation, and merge when you're confident. It's collaborative development for automation, which sounds obvious but is shockingly rare in the automation tool space.

What this replaces:

• Emailing workflow exports around
• Screenshots of configurations
• "Don't touch that workflow, I'm testing something"
• Copy-pasting workflows and hoping nothing breaks

6. Custom Agents with Persistent Memory

What the marketing says: "Build AI agents that remember context."

What it actually means: You can create specialized AI assistants that maintain conversation history, learn from interactions, and access your knowledge base—and these agents can be embedded in workflows or used standalone.

This is where Capacity starts to feel genuinely different from other tools. You're not just automating tasks—you're building intelligent systems that can handle nuanced interactions.

Practical example: I've built an agent that helps with code review. It knows our coding standards (because they're in the knowledge base), it remembers previous reviews (because it has persistent memory), and it can be called within workflows to analyze code changes automatically. But I can also chat with it directly when I need clarification about why something was flagged.

It's the difference between a static automation and an actual assistant.

How This Changes Document-Driven Development

Alright, let's talk about why this matters specifically for people working the way I work—with comprehensive documentation driving development.

The traditional problem with document-driven development is that you do a ton of upfront work creating specifications, but then you still have to implement everything based on those specs. You've articulated what needs to happen, but execution is still manual and error-prone.

AI coding assistants help with this, but they have limitations:

• Limited context windows mean you can't feed them everything
• They forget between sessions what worked last time
• You end up explaining the same architectural decisions repeatedly
• Every new project means rebuilding your prompting strategy

Capacity changes this calculus by letting you codify your development methodology as workflows.

Here's a concrete example from my own work:

Before Capacity:

1. Write requirements document
2. Manually extract security requirements
3. Open ChatGPT, paste security requirements
4. Ask ChatGPT to generate security implementation spec
5. Copy response, clean up formatting
6. Manually cross-reference against OWASP guidelines
7. Update spec based on gaps
8. Save to project repo
9. Repeat for every project, forgetting details each time

With Capacity:

1. Write requirements document
2. Run "Security Spec Generator" workflow
3. Review generated spec (which automatically cross-references OWASP, our internal security patterns, and project-specific requirements)
4. Approve or iterate

The workflow remembers our security patterns. It knows our architecture preferences. It references our standards documentation automatically. And most importantly: it produces consistent results every time.

This isn't about being lazy. It's about capturing and reusing the cognitive work you've already done.

Every time you figure out the right way to structure an API spec, or the optimal way to analyze requirements for security implications, or the best pattern for generating test scenarios—that's valuable knowledge. Capacity lets you preserve that knowledge as executable workflows rather than just documentation that people should read but probably won't.

The Workflow Templates That Immediately Pay Off

Let me share some specific workflow templates that I've found immediately valuable, so you can assess whether this aligns with problems you're actually facing:

Documentation Analysis and Enhancement

What it does: Takes technical documentation (architecture docs, API specs, etc.), analyzes for completeness and consistency, identifies gaps, and generates improvement suggestions.

Why it matters: Documentation review is tedious but critical. Automating the initial review catches obvious issues and lets you focus on substantive improvements.

Time saved: ~2 hours per documentation review cycle

Code to Documentation Sync

What it does: Analyzes code changes in a Git repository, extracts meaningful updates, and automatically generates or updates documentation to reflect those changes.

Why it matters: Documentation drift is real. This workflow doesn't eliminate the need for human documentation, but it dramatically reduces the gap between code reality and documentation.

Time saved: ~4 hours per sprint on documentation maintenance

Requirements to Test Scenario Generation

What it does: Takes user stories or requirements, analyzes for testable behaviors, and generates comprehensive test scenarios including edge cases and error conditions.

Why it matters: Test coverage gaps usually happen not because developers don't care, but because it's hard to think through all scenarios. Automating initial test scenario generation ensures nothing obvious gets missed.

Time saved: ~3 hours per major feature on test planning

Security Audit and Compliance Reporting

What it does: Scans code and architecture docs for security patterns, cross-references against compliance frameworks (OWASP, GDPR, etc.), and generates audit reports.

Why it matters: Security compliance is non-negotiable but tedious. Automating the initial audit lets you focus on actual remediation rather than documentation review.

Time saved: ~6 hours per audit cycle

API Documentation Generation and Validation

What it does: Analyzes API code, extracts endpoints and parameters, generates OpenAPI specifications, validates against documentation standards, and produces human-readable docs.

Why it matters: API documentation that's out of sync with implementation is worse than no documentation. This workflow ensures they stay aligned.

Time saved: ~2 hours per API version

What You Need to Know Before Committing

Alright, I've spent enough time on the positives. Let's talk about the actual constraints and considerations, because no tool is perfect, and pretending otherwise is dishonest.

Learning Curve Reality Check

Capacity isn't ChatGPT with a nicer interface. It's a platform for building automation, which means you need to actually learn how to use it effectively.

The first few workflows you build will be clunky. You'll structure steps inefficiently. You'll misunderstand how context flows between steps. You'll realize halfway through that there was a better way to approach the problem.

This is normal. This is how you learn any serious tool. But if you're expecting immediate productivity gains without investment, you'll be disappointed.

Realistic timeline for competence:

• Week 1: Fumbling around, building simple workflows, lots of documentation reading
• Week 2-3: Starting to get the hang of it, building moderately complex workflows
• Month 2: Building workflows that genuinely save time
• Month 3+: Building workflows that fundamentally change how you work

Cost Considerations

Capacity isn't cheap. The pricing model is based on usage, which makes sense but can be unpredictable if you're building heavy automation.

Is it worth it?

That depends entirely on what your time is worth and how much repetitive cognitive work you're doing.

If you're spending 10+ hours per week on tasks that could be automated (documentation review, test generation, security audits, etc.), then yes, Capacity probably pays for itself quickly.

If you're mostly doing creative, novel work that doesn't repeat patterns, then maybe not yet.

Integration Limitations

Despite the impressive API integration capabilities, there are still some tools that are difficult to integrate cleanly. Particularly legacy systems without modern APIs, or services that require complex authentication flows.

The platform is constantly adding integrations, but if your critical workflow depends on connecting to something obscure, verify that integration exists before committing.

AI Model Dependency

Capacity's value is deeply tied to the quality of underlying AI models. When GPT-4 has an off day, or when local models produce inconsistent results, workflows can be unreliable.

This isn't Capacity's fault—it's the nature of working with LLMs. But it's worth acknowledging that you're building on a foundation that's still evolving.

Mitigation strategies:

• Build validation steps into workflows
• Use multiple model providers where possible
• Have human review for critical outputs
• Version workflows so you can roll back if model behavior changes

The Honest Comparison to Alternatives

Let's talk about other tools in this space and why you might choose Capacity or why you might not.

Zapier / Make / IFTTT

When they're better: If you need simple, reliable integrations between a small number of apps and don't need complex logic or AI involvement.

When Capacity is better: When you need intelligent processing, complex decision trees, or workflows that reference documentation and context.

The truth: Zapier is cheaper and easier for simple stuff. Capacity is worth it when you need actual intelligence in your automation.

Custom Python Scripts

When they're better: If you have very specific requirements, need absolute control, and have engineering time to maintain them.

When Capacity is better: When you want reusable workflows that non-technical team members can understand and modify, or when you're building something that needs to evolve frequently.

The truth: Writing custom scripts gives you complete control but poor scalability. Capacity workflows are easier to share and maintain but less flexible for truly custom logic.

GitHub Actions / GitLab CI

When they're better: For code-focused automation that's tightly integrated with your development pipeline.

When Capacity is better: For cross-functional workflows that involve documentation, knowledge management, and AI processing beyond simple CI/CD.

The truth: These are different tools for different jobs. I use both. GitHub Actions for code deployment, Capacity for everything else.

Real Talk: Who Should Actually Use This

Let me be direct about who benefits most from Capacity based on what I've observed:

You're a Good Fit If:

1. You practice document-driven development or similar methodologies where clear specifications drive implementation
2. You have repetitive cognitive tasks that require intelligence but not creativity (documentation review, test generation, security analysis)
3. You work with teams where workflow sharing and collaboration adds value
4. You value time over money in the specific sense that spending $X/month to save 10+ hours is worth it
5. You're comfortable with abstraction and can think in terms of processes and workflows rather than just specific implementations

You're Probably Not a Good Fit If:

1. You mostly do creative, novel work that doesn't involve repeatable patterns
2. You're working alone on small projects where workflow sharing doesn't matter
3. Your budget is extremely constrained and you can't justify subscription tools
4. You need absolute control over every implementation detail and don't trust abstraction layers
5. You're not willing to invest time learning a new platform

You Should Definitely Try It If:

• You're managing technical documentation for multiple projects and struggling with consistency
• You're doing regular security or compliance audits that involve a lot of repetitive analysis
• You're generating test scenarios, API specs, or other structured technical artifacts regularly
• You're coordinating development across teams and need shared workflow standards
• You've hit the limits of simpler automation tools and need more intelligence in your workflows

The Migration Strategy That Actually Works

If you're convinced this is worth trying, here's how to approach adoption without disrupting everything you're currently doing:

Phase 1: Observation (Week 1)

Don't build anything yet. Just observe your workflow for a week and identify:

• Tasks you do repeatedly with slight variations
• Documentation you reference constantly
• Decisions you make based on patterns rather than creativity
• Context you have to rebuild frequently

Write these down. These are your automation candidates.

Phase 2: Proof of Concept (Week 2-3)

Pick one repetitive task—preferably something moderately important but not mission-critical. Build a workflow for it in Capacity.

Don't try to perfect it. Just get something working that demonstrates value.

Good first projects:

• Documentation review workflow
• Test scenario generation from user stories
• Security checklist generation from requirements
• API documentation generation from code

Phase 3: Integration (Week 4-6)

If the proof of concept works, build out 2-3 more workflows for other repetitive tasks. Start building your knowledge base properly. Establish patterns for how your team (even if it's just you) will structure workflows.

Phase 4: Expansion (Month 2+)

Now you can start building more complex workflows, connecting multiple systems, and really leveraging the platform's capabilities.

Critical: Don't try to automate everything immediately. That's how you burn out on tools. Automate incrementally, learn what works, iterate.

The Bottom Line (And Whether You Should Buy)

Look, here's the honest assessment: Capacity is a genuinely useful tool for people who work a certain way, but it's not universal.

If you're someone who:

• Thinks in systems and processes
• Values documentation and methodology
• Spends significant time on repetitive but intelligent work
• Sees value in capturing and reusing cognitive patterns

Then yes, Capacity is probably worth the investment. It's not perfect, it requires learning, it costs real money—but it delivers real value in the form of time saved and consistency improved.

If you're someone who:

• Prefers ad-hoc, creative work without much repetition
• Values complete control over implementation details
• Works alone on small projects
• Has tight budget constraints

Then probably not yet. Maybe revisit when your workflow changes or when pricing becomes more accessible.

For me personally? I'm keeping the subscription. It's become part of my standard toolkit, sitting alongside GitHub, VS Code, and Notion as tools I use daily. Not because it's magical, but because it solves real problems I was facing.

The workflows I've built have saved me probably 10-15 hours per week. The knowledge base integration means I'm not constantly re-explaining architectural decisions. The ability to share workflows with collaborators has improved consistency across projects.

It's not revolutionary in the sense of completely changing what's possible. It's valuable in the much more important sense of making what's possible actually achievable without burning yourself out.

And honestly? That's the kind of tool worth paying for.

Try It Yourself (With Realistic Expectations)

If you want to evaluate Capacity, here's what I'd recommend:

Don't:

• Expect immediate productivity gains
• Try to automate everything at once
• Judge it based on the first workflow you build
• Compare it to fundamentally different tools like ChatGPT

Do:

• Give yourself 2-3 weeks to learn the platform
• Start with one genuinely repetitive task
• Build your knowledge base properly
• Iterate on workflows until they work reliably
• Track time saved objectively

And if after a month you're not seeing value? Cancel the subscription. No tool works for everyone, and that's fine.

But if you're like me—someone who's been looking for a way to capture and execute document-driven development workflows without rebuilding everything from scratch every time—then Capacity might be exactly what you've been needing.

👉 Try Capacity here

(Full disclosure: That's an affiliate link. If you sign up through it, I get a small commission at no cost to you. I'd recommend the tool either way, but transparency matters.)

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Key Takeaways

• Capacity is workflow automation for intelligent, context-aware processes—not just simple if/then logic
• It excels at capturing and reusing cognitive patterns from document-driven development
• Learning curve is real but payoff comes within weeks for the right use cases
• Cost is justified by time savings if you have repetitive but intelligent work (10+ hours/week)
• Best for teams doing document-driven development with established methodologies and patterns
• Not a replacement for creative thinking—it's a tool for preserving and executing established workflows

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Frequently Asked Questions

Q: Is Capacity better than just using ChatGPT or Claude directly?

A: Different tools for different jobs. ChatGPT/Claude are excellent for one-off tasks and creative work. Capacity is better for workflows you'll run repeatedly, that need to reference documentation, and that benefit from being versioned and shared.

Q: Can I use my own local LLMs with Capacity?

A: Yes, Capacity supports integration with local models via API endpoints. This is particularly valuable if you're running Ollama or similar local inference servers.

Q: How long does it take to see ROI?

A: If you're spending 10+ hours per week on repetitive cognitive tasks, you'll likely see ROI within 2-3 months. If your work is mostly creative and non-repetitive, ROI may be longer or non-existent.

Q: Can non-technical team members use Capacity?

A: Yes, once workflows are built. Building workflows requires technical understanding, but using existing workflows can be made accessible to non-technical users through good interface design.

Q: What happens if I cancel my subscription?

A: You lose access to the platform and workflows, but you can export workflow definitions (they're JSON) before canceling. Whether you can run them elsewhere depends on how much they rely on Capacity-specific features.

Q: How does this compare to AI coding assistants like Cursor or Copilot?

A: Those are code generation tools. Capacity is workflow automation. They complement each other—use Cursor/Copilot for writing code, use Capacity for automating processes around that code (documentation, testing, security analysis, etc.).

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Have questions about Capacity or want to share your experience with workflow automation? Find me at danielkliewer.com where I'm always thinking through this stuff alongside everyone else trying to work smarter.