Custom AI Tooling

Why Off-the-Shelf Falls Short

Generic AI tools have to make assumptions. They assume a certain workflow, certain data formats, certain output needs. When those assumptions don't match your reality, you end up with workarounds, copy-paste gymnastics, and tools that feel like friction rather than help.

I've watched teams adopt expensive AI platforms only to use maybe 20% of the features, while the specific capability they actually need doesn't exist. Custom tooling flips this equation—you get exactly what you need, designed to fit your process.

• Inputs formatted the way your data actually looks
• Outputs structured for your downstream systems
• Interfaces that match your team's mental models
• Integration points where you actually need them
• Pricing that scales with your usage, not someone else's tiers

Understanding Before Building

I don't start with technology; I start with workflow. Before writing any code, I need to understand how your team actually works. What are the repetitive tasks that consume time? Where do errors creep in? What information do people wish they had faster access to?

This discovery process often reveals opportunities that weren't obvious at first. Sometimes the best AI tool isn't the one originally requested—it's the one that addresses the root problem rather than a symptom.

What Custom AI Tools Look Like

Custom AI tooling spans a wide range. Some examples from my work:

Content Generation Systems

Not just "write me a blog post" but systems that understand your brand voice, reference your existing content, follow your style guide, and output in formats ready for your CMS. I've built generators that produce consistent, on-brand content at scale while keeping humans in the review loop.

Document Analysis Tools

Tools that read your specific document types—contracts, reports, applications—and extract exactly the information your processes need. Rather than generic document AI, these tools know your terminology, your edge cases, and your data requirements.

Decision Support Systems

AI that helps humans make better decisions by surfacing relevant information, flagging concerns, and providing context. Not automation that removes humans from the loop, but augmentation that makes them more effective.

Internal Search and Discovery

Custom search tools that understand your organization's content—wikis, docs, Slack history, email archives—and can answer questions in natural language. "What's our policy on X?" becomes a question the AI can actually answer.

Designing for Adoption

A powerful tool that nobody uses is worthless. I think carefully about adoption throughout the design process:

• Low friction entry: Tools should be immediately usable without extensive training
• Clear value proposition: Users should see benefit within their first interaction
• Progressive complexity: Basic usage is simple; power features are available but not required
• Feedback loops: Users can see when the AI is confident versus uncertain
• Graceful handling of errors: When things go wrong, the tool explains and recovers

I also consider the organizational context. Who needs to approve this tool? What security reviews are required? How does it fit with existing systems? These factors shape design decisions from the start.

Tools That Improve Over Time

Custom AI tools shouldn't be static. I build with iteration in mind:

• Logging that captures how the tool is actually used
• Feedback mechanisms so users can flag issues
• Architecture that allows prompt and model updates without code changes
• Documentation that makes maintenance straightforward

As your needs evolve and AI capabilities advance, your tools should evolve too. I design for that from day one.