What LLMs do you work with?
OpenAI (GPT-4o, o1), Anthropic (Claude 3.5 Sonnet, Opus), Meta Llama 3, Mistral, and Gemini, plus open-source models self-hosted on AWS or GCP. We're model-agnostic and pick based on your accuracy, latency and cost targets. We write the architecture so you can swap models without rebuilding the system.
How long does an AI project take?
A working prototype (RAG pipeline or single-agent flow) typically ships in 2 weeks. Production-ready, with evals, monitoring, auth and integrations, typically takes 4–8 weeks depending on data complexity; we confirm the timeline in writing during scoping. We work in 2-week sprints with a live preview at the end of each one; you're never waiting more than a fortnight to see progress.
Do you handle my proprietary data securely?
Yes. We sign NDAs before seeing any data. For on-premise or private-VPC deployments, your data never leaves your infrastructure. For cloud builds, we use Anthropic's and OpenAI's provider retention options verified for the chosen deployment where available, and implement field-level encryption for anything classified.
What's the difference between RAG and fine-tuning?
RAG retrieves relevant context at inference time from a vector store, best for document Q&A, internal knowledge bases, support bots. Fine-tuning bakes knowledge into model weights, best for consistent style, domain terminology, or tasks where latency matters and the knowledge is relatively static. Most enterprise use cases start with RAG because it's faster to iterate, easier to update, and the quality is now comparable on most tasks. We'll tell you honestly if fine-tuning is actually worth the cost for your use case.
How do you measure AI quality?
We build an evaluation harness before we write the first prompt, ground-truth Q&A pairs, retrieval precision/recall, hallucination detection, latency P50/P95 and cost-per-query benchmarks. Every sprint preview includes a live eval run. You get a dashboard showing model performance from day one, not a vibes check at launch, not "it seems to be working" after go-live.
