Building with AI
Straight answers on building software with AI - production-readiness, code audits, AI agents, and choosing an AI-assisted partner.
Explore our AI-Assisted Software Development service →How do you keep AI-generated code secure, maintainable, and production-grade?
With the same controls you would apply to any production code, plus a few that are AI-specific. Every change goes through human review, automated tests, and security and dependency scanning before merge, and AI-suggested code is never shipped unread. For code that was generated quickly, or by a no-code tool, we run a focused audit covering security, architecture, and maintainability before it scales.
Read the answer →What Does It Cost to Add AI Workflow Automation to My Business?
AI workflow automation has no fixed price. Most small and mid-sized businesses spend $200 to $600 a month using no-code platforms, while custom builds start around $15,000 upfront. Your cost depends on how many workflows you automate, the systems they connect to, and the run volume. ROI usually starts in 2 to 6 months.
Read the answer →Should I use an open-source LLM or a commercial API?
Most businesses should start with a commercial API like OpenAI, Anthropic, or Google. It ships fast and needs no infrastructure. Self-host an open-source model only when your volume is high and steady, or when data must stay on your own servers. Quality is now similar; cost, control, and maintenance decide the call.
Read the answer →How Do I Add AI to My Existing App or SaaS?
You don't rebuild your app to add AI. Pick one feature your users actually struggle with, then wire in a hosted model API (OpenAI, Anthropic, or Google) through a thin service layer. Feed the model your own data at the moment of each request so answers stay accurate. Most SaaS products need nothing more.
Read the answer →Should You Build a Custom AI Agent or Use the OpenAI API?
It's not either/or. The OpenAI API is the engine; a custom agent is what you build around it (memory, tools, and your business logic). Use the API alone for a first or simple feature. Build an agent when you need multi-step reasoning, deep integrations, or proprietary logic. Most teams do both.
Read the answer →Does AI-assisted development actually make software cheaper and faster to build?
Often yes, but the savings come from speed to a validated product, not from skipping engineering. AI-assisted development can meaningfully shorten early build cycles, especially for MVPs and well-scoped features. It does not remove the need for architecture, testing, and review, and treating it as a shortcut around those is where projects get expensive later.
Read the answer →Is AI-assisted software development reliable enough for production software?
Yes, when it is done with engineering discipline rather than left to the tool. AI accelerates how senior engineers write, test, and review code, but architecture, security, and quality gates stay human-owned. The risk is not AI-assisted development itself; it is AI-assisted development without senior oversight.
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