TLDR; LLM integration isn't just about plugging in AI it's about your data, your processes, and finding someone who gets how your business actually works. Good data plus the right partner equals results.
Everyone wants to add AI to their business right now.
CEOs are asking "What's our LLM strategy?" and teams are scrambling to figure out what that even means.
But most companies are approaching this backwards.
The Data Part Nobody Wants to Talk About
Here's the truth: your LLM is only as good as your data. If you're feeding it messy, incomplete, or outdated information, you're going to get messy, incomplete, or outdated answers back.
What good data looks like:
- It's organized in a way that makes sense for your business
- It's updated regularly
- It covers the questions your customers actually ask
- It's formatted consistently
What most companies actually have:
- Spreadsheets from 2017 that nobody's touched
- Customer data scattered across five different systems
- Important knowledge living in people's heads instead of documents
- Duplicate information that contradicts itself
Before you even think about which LLM to use, you need to clean up your data house. I know it's not exciting. But do this step wrong, and everything that follows is just polishing garbage.
Open Source vs. APIs: The Money Question
Companies spend weeks debating whether to use open source models or paid APIs. Here's the reality:
Open source sounds great because:
- No per request costs
- You can modify the model
- Your data stays on your servers
- Complete control
Until you realize:
- You need GPUs to run them (expensive)
- Someone has to maintain them
- They're usually not as good as the paid ones
- You still need people who know what they're doing
Paid APIs like OpenAI are simple because:
- They just work
- Better performance
- No maintenance headaches
- Someone else handles the hard parts
Until the bill arrives:
- Token costs add up fast
- Your data goes through their servers
- You're dependent on their uptime
- Prices can change anytime
Most successful companies start with APIs to prove the concept, then consider open source if costs get crazy.
Finding Someone Who Gets Your Business
This is where most companies mess up. They hire someone who knows all about transformers and attention mechanisms but has never run a business before.
The wrong consultant asks:
- "Which LLM architecture do you want to use?"
- "What's your vector database strategy?"
- "Have you considered fine tuning parameters?"
The right consultant asks:
- "What questions are your customers asking most often?"
- "Where are your employees wasting time on repetitive tasks?"
- "What would make the biggest difference to your bottom line?" The best LLM integrations happen when someone understands how your business actually operates, not just how the technology works. They'll know that your support team needs quick answers to customer questions, not some fancy AI dashboard that looks good in board meetings.
Token Costs Will Surprise You
Here's something most companies don't realize until it's too late: every request costs money, and it adds up fast. If your customer support bot handles 1,000 conversations a day, and each conversation costs $0.02 in tokens, that's $20 per day or $600 per month. That's just for one use case. Multiply that by different departments and use cases, and suddenly you're looking at serious money.
Good consultants help you figure out:
- Which requests actually need expensive models
- When you can cache responses instead of calling the API
- How to design prompts that use fewer tokens
- Which use cases even need AI in the first place
The Bottom Line
LLM integration isn't a technology projectit's a business project with technology components. You need clean data, a clear understanding of what you're trying to solve, and someone who speaks both business and tech. Start small, measure everything, and focus on problems that actually matter to your customers and employees. The rest is just details.
