AI & LLM Integration Services

Chatbots that answer from your own documents, document processing that pulls tidy data out of messy files, search that understands what people mean instead of matching exact words, and AI agents that plan and carry out multi-step tasks inside your product, with a person approving the risky steps.

RAG (retrieval-augmented generation) means the system first looks up the right passages in your documents, then asks the model to answer using them. The model is only as good as what you hand it. When a RAG assistant gives a wrong answer, the cause is usually the lookup: the right passage never reached the model.

So we test the lookup separately from the answer-writing. We spend real time on how documents are cut into pieces, how those pieces are stored for searching, and how results are ranked, before we spend it on the wording of instructions to the model.

We also build answers to cite the passage they came from, so a reader can check them. In customer-facing and regulated settings, an answer nobody can trace is an answer nobody can use.

OpenAI's guide to improving accuracy draws the same line. Retrieval is for when a model lacks knowledge, such as your own or up-to-date information. Fine-tuning, which means training a model further on examples, is for when its answers are inconsistent in format, tone, or reasoning.

We work with both. Claude is a strong fit for long documents, code analysis, and careful reasoning. OpenAI's GPT models and embeddings API are a solid default for general-purpose work. We choose per task, and we structure the code so switching providers later is a small change, not a rewrite.

We test the model's answers against real examples from your data, track what each request costs, set limits, and reuse earlier answers where it's safe, so a successful feature doesn't produce a surprising bill.

Before a model sees any data, we review what it can access. Personal information risks are flagged during discovery, not after launch.

Some problems are better solved with a search box, a rule, or a form. If a feature doesn't need an LLM, we'll say so. AI should earn its place in your product by solving a real problem for your users.