Why Your Internal Chatbot Should Be Model-Agnostic: Lessons from a Past Project

July 14, 2025 • 3 min read

Two years ago - when AI chatbots in enterprises were still relatively new - I implemented an internal chatbot system for a company. When external auditors reviewed the architecture recently, they mentioned it was one of the more thoughtful, model-agnostic implementations they'd encountered in their evaluations. This got me thinking about something I've noticed: many companies are building chatbots that lock them into their first vendor choice. It's an easy trap to fall into, especially when you're focused on getting something working quickly. I've since heard from other consultants that many of their clients struggle with AI implementations - some companies spend weeks, if not months, and significant budget just trying to upgrade to a newer model version. ### The Problem Most Companies Don't See Coming Here's what typically happens: your team picks OpenAI (or Google, or Anthropic) because they need to get a chatbot running. The initial implementation goes well, everything works, and everyone's happy. Including the Executive Board. A few months later, you discover: - A competitor offers better pricing for your specific use case - Your industry requires data to stay in Europe, but your current vendor doesn't support that - A new model emerges that's significantly better for your particular needs - Your current vendor changes their terms or pricing structure Suddenly, what seemed like a simple vendor switch becomes a complex, expensive rebuild project. ### A Different Approach: Building for Change When I architected the system for this company, we made a few key decisions early on that proved valuable: **We created our own internal standards** instead of adopting any vendor's format directly. This meant defining how conversations should be structured, how user context gets handled, and how business logic integrates with AI responses. **We built an abstraction layer** so the business applications never directly call vendor APIs. Instead, they communicate with our internal AI service, which handles the vendor-specific complexities behind the scenes. **We planned for multiple models from day one**, even though we started with just one. This wasn't much extra work upfront, but it meant we could easily add new models later. ### The Practical Benefits The company can now: - Test new models in hours instead of weeks - Negotiate better deals because vendors know they can switch - Use different models for different purposes (customer service vs. technical documentation) - Adapt quickly to regulatory changes or new market requirements ### What I'd Recommend If you're planning an internal chatbot: 1. **Don't rush into vendor-specific implementations** - those "quick start" guides often create long-term dependencies 2. **Budget a bit more time upfront** for proper architecture - it's much cheaper than rebuilding later 3. **Think about your specific requirements** - compliance, data residency, cost optimization 4. **Consider getting an outside perspective** - it's easy to miss these architectural decisions when you're focused on delivery ### The Bottom Line The AI landscape changes quickly. Companies that maintain flexibility will be better positioned to adapt and optimize as new options emerge. It's not about building the most complex system - it's about making smart architectural choices that keep your options open.