Custom AI Development Built Around Your Data

Our approach to custom AI development: retrieval, prompting, then fine-tuning

Most business problems are knowledge problems. The model is capable enough, but it has never seen your contracts, product data or procedures. Retrieval-augmented generation solves this by fetching the right passages at question time and having the model answer from them. Doing it well takes care: parsing documents without losing tables, choosing chunk boundaries that preserve meaning, combining keyword and vector search, reranking results, and filtering by user permissions before anything reaches the model.

Fine-tuning is a narrower tool than many expect. It rarely teaches a model new facts reliably. It is effective for consistent style and format, specialized classification, and getting a smaller, cheaper model to perform a specific task well. We reach for it after prompt design and retrieval have been measured and found insufficient, and we judge it the same way as everything else: against a held-out test set, compared with the simpler baseline.

• Retrieval for knowledge that changes
• Prompt design for behavior and tone
• Fine-tuning for format and specialized tasks
• Structured outputs for downstream systems
• Permission filters applied before generation

Why evaluation pipelines decide whether an LLM application succeeds

A demo that works on five questions tells you very little. Language models are probabilistic, and a prompt change that fixes one answer can degrade ten others. We treat evaluation as core engineering. Early in the project we collect representative inputs with your subject experts and agree what a correct output looks like. That becomes a test set covering common cases, edge cases and inputs the system should refuse or escalate.

Each candidate change runs against that set. Deterministic checks verify format and required facts. Model-graded checks score groundedness and relevance against a written rubric. Your experts review a sample to keep the automated scoring honest. The same pipeline compares models, so choosing between a hosted API from OpenAI or Anthropic and an open-weight model such as Llama becomes a measured decision about quality, latency, cost and data privacy rather than an opinion.