The Question Every Business Asks
You know you need AI in your operations. The question is: do you subscribe to an existing AI tool, or hire someone to build a custom solution? The answer depends on your specific situation, and getting it wrong can cost you months and tens of thousands of dollars.
Here's the framework we use with our clients.
When Off-the-Shelf Works
Buy when all of these are true:
- Your use case is generic — email writing, basic chatbots, simple document summarization
- You don't need deep integration — the AI tool works standalone, not connected to your systems
- Your data isn't proprietary — you're not feeding sensitive or industry-specific information
- You need it yesterday — speed matters more than customization
Good examples: ChatGPT for general writing, Jasper for marketing copy, Intercom's AI for basic customer support on your website.
When Custom AI Makes Sense
Build when any of these are true:
| Factor | Why It Requires Custom | |--------|----------------------| | Integration with your systems | You need AI connected to your CRM, EHR, scheduling, or billing systems | | Industry-specific logic | Healthcare compliance, legal requirements, financial regulations | | Proprietary data advantage | Your competitive edge comes from AI trained on your specific data | | Workflow automation | AI needs to take actions, not just generate text — book appointments, process documents, route leads | | Customer-facing quality bar | Your AI represents your brand and needs to handle edge cases gracefully | | Scale and performance | You need control over latency, uptime, and cost at scale |
The Middle Ground: Custom on Top of Platforms
The most common approach we see in 2026 is building custom solutions that leverage existing AI platforms. You don't build your own LLM — you build custom applications using OpenAI, Anthropic, or similar APIs.
This gives you:
- State-of-the-art AI models without training costs
- Full control over the user experience and integrations
- Your own data pipeline for context and retrieval (RAG)
- Custom business logic layered on top of general AI capabilities
This is how we built Loquent — using best-in-class AI models, but wrapping them in a custom application with deep integrations into healthcare systems.
Cost Comparison
Here's what to expect:
| Approach | Typical Cost | Timeline | Fit | |----------|-------------|----------|-----| | SaaS AI tool | $50-500/mo | Instant | Generic use cases | | Custom integration | $10K-40K one-time | 4-6 weeks | Connecting AI to your systems | | Custom AI product | $40K-150K+ | 8-12 weeks | Full AI-powered application | | Custom + retainer | Project fee + $3-8K/mo | Ongoing | Continuous improvement |
The right choice depends on the business value at stake. If AI can save you $10K/month in operations, a $40K custom build pays for itself in 4 months.
Questions to Ask Before Deciding
- Can an existing tool do 80% of what I need? If yes, start there.
- Do I need AI connected to my internal systems? If yes, you need custom work.
- Is this a competitive advantage? If AI is core to your value proposition, build custom.
- What's the cost of getting it wrong? Customer-facing AI needs to be reliable. Generic tools often aren't.
- Do I have ongoing AI needs? If you'll keep building AI features, invest in a custom foundation.
Our Recommendation
Start with the simplest approach that solves the problem. If a $99/month tool handles it, use that. But when you need AI that integrates with your systems, handles your industry's complexity, and represents your brand — that's when custom development delivers real ROI.
We've seen too many businesses waste months trying to force generic AI tools into complex use cases. If you're debating build vs. buy, talk to us — we'll give you an honest assessment of which approach makes sense for your situation.