Answers grounded in your knowledge, with receipts.

Most startups start with RAG: it grounds a general-purpose model in your live data without retraining, and it's cheaper and faster to update. Fine-tuning earns its cost when you need a consistent tone, a specialized task format, or behavior that retrieval alone can't teach — and the two combine well when both apply.

When you need a specific output format at scale, a domain vocabulary the base model gets wrong, or lower latency and cost than a long RAG prompt allows. We'll tell you honestly if your use case doesn't need it — a lot of "we need to fine-tune" requests are solved by better retrieval instead.

We build on the major hosted APIs — OpenAI's GPT models, Anthropic's Claude, Google's Gemini — and on open-weight models like Llama and Mistral when you need self-hosting, data residency, or a fine-tune you fully own. The right choice depends on your latency, cost, and privacy constraints.

No. We use enterprise API tiers and opt-out settings that exclude your data from provider training, and any fine-tuned weights or embeddings we produce are yours, isolated to your environment. Nothing you send us gets pooled with another client's data.

Typically in a vector database inside your own cloud account — Pinecone, pgvector, or a managed alternative — so you retain access control and audit trails. We'll match the hosting model to whatever compliance posture your startup already needs (SOC 2, HIPAA, etc.).

Cost scales with data volume, source complexity, and whether fine-tuning is involved — a straightforward RAG pipeline over clean documentation is far cheaper than fine-tuning across messy, multi-source data. We scope and quote after the discovery workshop, once we've seen your data.

A focused RAG pipeline over a well-defined data source usually ships in 3–5 weeks. Add fine-tuning, messier data sources, or tighter evaluation requirements and timelines extend to 6–10 weeks. We give a firm estimate once scoping is done.

We build a held-out evaluation set from real questions and score retrieval relevance, answer correctness, and hallucination rate before anything ships. You see the numbers, not just a demo that happened to work.

Grounded retrieval with citations, confidence thresholds that trigger a fallback instead of a guess, and a guardrail layer that filters outputs before they reach the user. We tune these thresholds against your evaluation set, not a generic default.

RAG pipelines are built with a sync job that re-embeds changed content on a schedule or on webhook, so retrieval stays current without a redeploy. Fine-tuned models are refreshed on a slower cadence — we set that interval based on how fast your underlying data actually moves.

Usually your own cloud account, so you keep control and predictable billing as usage scales. We can also manage hosting directly for teams that would rather not run ML infrastructure themselves — both models are on the table during scoping.

Monitoring flags drops in retrieval relevance or answer quality against your baseline eval set, and we review flagged sessions monthly. Most drift traces back to new data that hasn't been indexed yet, which the sync job usually catches on its own.

You get a working pipeline in weeks instead of a multi-month hiring search, and our engineers have shipped RAG and fine-tuning across enough startup stacks to know where these projects actually break. Marketing and product sit alongside the build, so the system is scoped to a real business outcome, not a research exercise.

Book a scoping call and bring a sense of your data sources and the problem you want the model to solve. We'll walk through the RAG-vs-fine-tuning tradeoff for your case and follow up with a written architecture recommendation and quote within 24 hours.