LLM integration services

What LLM integration means in practice

Calling a model’s API takes a few lines of code. Making it useful inside a real product takes much more. Someone has to decide what information the model sees, how its answer is checked, what happens when it is slow or wrong, how much each request costs and where the data goes. That is the work of LLM integration.

The goal is usually a feature, not a chat window. A “summarize this case” button in your internal app. Incoming emails that arrive already tagged and routed. A search box that answers questions from your documents. A form that fills itself from an uploaded PDF. The model does the reading and writing, and your software decides what to do with the result.

Common LLM integration projects

• OpenAI API integration: adding GPT models to an existing web app, CRM or internal tool for drafting, summarizing or classifying.
• Claude API integration: using Anthropic’s models for long documents, careful writing or tasks where a second model is useful for comparison.
• RAG development: letting a model answer questions from your own manuals, policies, contracts or help articles, with links to the source.
• Structured extraction: turning emails, notes or documents into clean fields your database can store and your reports can use.
• LLM application development: building a new internal or customer-facing app where the model is one part of a larger product.
• Model changes: moving an existing feature to a different provider or an open-source model for cost, quality or privacy reasons.

RAG development, explained plainly

A language model only knows what it learned in training. It does not know your prices, your procedures or last week’s contract. Retrieval-augmented generation, or RAG, fixes that by searching your own content first and handing the model the relevant passages along with the question. The model then answers from those passages and can cite where the answer came from.

Good RAG is mostly careful data work: splitting documents sensibly, keeping them up to date, respecting who is allowed to see what, and testing whether the right passages are actually found. When answers are poor, the cause is usually retrieval, not the model. We test retrieval on your real questions before we tune anything else.

Choosing a model provider

There is no single best model. OpenAI, Anthropic and Google each offer strong models with different strengths, prices, speed and data terms. Open-source models can run on servers you control, which can help with privacy or cost at high volume, but they need more setup and are not always as capable. Doing it yourself in ChatGPT or Claude by hand is still the right first step for many teams to see what works.

We test candidate models on your own examples, compare quality, speed and cost, and review each provider’s data handling terms with you. We also build integrations so the model sits behind one layer of your code, which means you can switch providers later without rebuilding the feature.

How our LLM integration services work

1. Define the featureWe agree exactly what the model should do, what it sees, and what a good result looks like.
2. Collect test casesWe gather real examples from your business to measure quality before and after each change.
3. Compare modelsWe try suitable models on those cases and pick on quality, cost, speed and data terms.
4. Design the experienceYou click through the screens where results appear, including how people review or correct them.
5. Build and connectWe integrate the model with your app and data, with logging, error handling and cost limits.
6. Launch and monitorWe watch quality and cost in production and re-test whenever a provider updates its models.

Costs, limits and privacy

Zoti custom projects start at $499/month on a monthly plan that covers design, development, hosting and ongoing changes. Model API fees are separate, depend on volume and are explained up front. Language models can still be wrong, so we design features with source links, confidence checks and human review where mistakes would matter, and we only send a model the data it needs. You get a written quote before any work starts.