AI integration doesn't mean rebuilding your product. It means adding intelligence where it actually helps your users, in a way that is reliable, cost-effective, and maintainable.
This is how we approach it at Soleno.
Start With the Problem, Not the Model
The biggest mistake we see is teams starting with a model (GPT-4, Claude, etc.) and then hunting for places to use it. The result is features that feel bolted on.
Instead, start with questions like:
- Where do users spend the most time on repetitive tasks?
- What parts of the product require human judgment that could be augmented?
- Where would instant, intelligent responses significantly improve the experience?
The best AI features are ones where users think "why didn't this always work this way?"
Common AI Integration Patterns
Most AI features fall into a few patterns:
Content Generation
Drafting emails, summaries, descriptions, or reports based on user data. This is the most common starting point because it's high-impact and relatively straightforward.
Intelligent Search and Retrieval
Using embeddings and vector search to let users find information in natural language instead of exact keyword matching. Powerful for knowledge bases, documentation, and internal tools.
Classification and Routing
Automatically categorizing support tickets, leads, or content. This works well with smaller, faster models and can dramatically reduce manual work.
Conversational Interfaces
Chatbots and assistants that can answer questions, take actions, or guide users through workflows. These require more careful design but can be the highest-impact feature.
Data Analysis and Insights
Generating summaries, trends, or recommendations from structured data. Particularly valuable in dashboards and reporting tools.
Choosing the Right Model
Not every feature needs the most powerful (and expensive) model.
- GPT-4o / Claude Sonnet. Best for complex reasoning, nuanced content generation, and multi-step tasks.
- GPT-4o Mini / Claude Haiku. Great for classification, simple extraction, and high-volume low-latency work.
- Open-source models (Llama, Mistral). Consider these for on-premise requirements or when you need full control over the model.
The right choice depends on your latency requirements, cost constraints, and the complexity of the task.
The Integration Architecture
A clean AI integration typically looks like this:
- API layer. Your backend calls the AI model's API with structured prompts.
- Prompt management. Prompts are versioned and separated from application code.
- Caching. Identical requests return cached responses to reduce cost and latency.
- Fallbacks. If the AI call fails or times out, the feature degrades gracefully.
- Monitoring. Track response quality, latency, cost, and user feedback.
This isn't over-engineering. It's the minimum for a production-quality integration.
What to Avoid
- Don't expose raw model output to users. Always validate and format responses.
- Don't build AI features without usage metrics. You need to know if users actually find them useful.
- Don't try to make AI do everything. Pick one or two high-impact features, ship them as part of your MVP, learn, then expand.
- Don't ignore cost. AI API costs scale with usage. Build in monitoring from day one.
Getting Started
If you're considering AI integration for your product, the best first step is identifying one feature where AI would save users real time or effort. Start small, validate with real users, and expand from there.
At Soleno, we help teams integrate AI into their products without the complexity spiral. From picking the right model to building production-ready pipelines, let's talk about what AI can do for your product.