LLM and Generative AI Development Services

A proof of concept on your data costs $15k–$40k over 3–6 weeks. A production pilot with guardrails, monitoring and evaluation typically costs $50k–$150k over 2–4 months. Scaling to several use cases starts at $150k.

It depends on the use case. Most business applications start with a strong base model plus retrieval-augmented generation (RAG) over your own data. It's cheaper than fine-tuning and easier to keep current. Fine-tuning earns its cost for narrow, high-volume tasks where prompt-and-retrieve isn't enough. We help you decide deliberately rather than defaulting to the most expensive option.

You can't eliminate it, but you can engineer it down to an acceptable level: retrieval grounding, guardrails, output validation, confidence thresholds, and human-in-the-loop where stakes are high. Above all, you need an evaluation harness that measures how often it happens so you can improve it.

Often not fully, and that's normal. AI quality depends on the data foundation underneath it. Where it's not ready, we build the pipelines and governance first (see Data Engineering) rather than putting an LLM on top of unreliable data.

Yes, it's a core strength. We design AI systems with PHI/PII boundaries in training and inference, audit trails, and evaluation that satisfies clinical or compliance review, aligned to standards like FDA GMLP.

Production is the whole point of how we work. We build evaluation, observability, guardrails, and governance in from the start, and we don't consider an engagement done until the system is reliable in production and your team can operate it.