Ship AI that thinks.

RAG is the right default for most use cases. If your answers need to draw on a knowledge base that changes -- documentation, policies, product data -- RAG lets you update the retrieval corpus without retraining. Fine-tuning is better when you need a specific output format, tone, or reasoning style baked into the model itself. We typically start with RAG and only fine-tune once we've hit the ceiling of what prompt engineering and retrieval quality can achieve.

A focused proof of concept using LangChain or LangGraph with real data takes 2-3 weeks. Production hardening -- guardrails, fallbacks, latency optimization, and monitoring via Langfuse or Braintrust -- adds another 3-5 weeks depending on integration complexity. Voice AI projects with Vapi or Retell AI typically run 4-6 weeks end-to-end.

We evaluate models against four dimensions: accuracy on your specific task, latency requirements, cost at your expected token volume, and data privacy constraints. GPT-4o is our default for reasoning-heavy tasks. Claude performs better on long-context document work. Llama 3 or Mistral are the right call when data can't leave your infrastructure. We run benchmarks on your actual data before committing to a model.

Proof-of-concept builds run $8,000-$20,000 depending on scope. Production systems with custom agents, RAG pipelines, and monitoring integrations typically fall in the $25,000-$75,000 range. Ongoing LLM API costs at moderate usage run $500-$2,000/month -- we include cost modeling in our scoping process so there are no surprises.

We wire every production system to an evaluation layer using Langfuse or Braintrust for trace logging, and Ragas or DeepEval for automated quality scoring. We set up LLM-as-judge evaluations for open-ended outputs, track latency and cost per request, and create regression test suites that run on every deployment.