Generative AI consulting, by people who run their own AI product in production.

Generative AI consulting is the engineering practice of getting LLM-backed features from a demo into production with an eval harness, a prompt rig, and a dashboard the buyer can read. The honest version of the category builds and runs the systems it sells; the dressed-up version sells prompt-tweaking by the hour with no instrumentation. We are the first kind. The proof is Sentinel.

AI consulting is the strategy practice - which projects to fund, which to kill, how to sequence them. Generative AI consulting is the engineering practice for the LLM-specific subset - prompts, evals, RAG, fine-tuning, function calling, structured outputs. We do both, but a head of product or CTO shopping for GenAI consulting wants the implementers, not the strategists. This page is for the implementers.

We standardize on Anthropic's Claude - Opus for hard reasoning, Sonnet as the daily driver, Haiku for high-throughput cheap calls - and reach for GPT-4 class OpenAI models, open-source Llama derivatives, or specialized models when the eval says we should. We will tell you on the fit call which tier fits the latency and cost envelope you have to hit. The right answer is whichever model the eval picks.

Almost always: prompt first, then RAG, then fine-tune only if the eval says you have to. Fine-tuning earns its place when the task is narrow, the format is rigid, latency or cost matters more than reasoning, or the prompt has hit the model's instruction-following ceiling. Most teams arriving at us are budgeting to fine-tune three months too early. We will tell you on the fit call whether your problem actually needs it.

Yes - and we spend more time on chunking, retrieval evals, and hybrid keyword-plus-vector search than on the model. RAG is not a thing you ship; it is a thing you tune. We build with whichever vector database the team already runs (Pinecone, Weaviate, pgvector) and treat the embedding step as a calibration that has to be revisited every quarter as the corpus shifts, not a one-time decision.

A GenAI strategy sprint is two weeks, no extensions. A production proof-of-concept is fourteen days from kickoff to a feature serving real traffic behind a feature flag. A full implementation - RAG, evals, monitoring, the integration layer - is six to twelve weeks depending on data hygiene and integration surface. We refuse engagements that don't fit a 14-day sprint at the unit level.

Strategy sprints run $25–45k. Production proofs-of-concept run $50–150k. Full implementations start at $150k and scale with integration depth. Embedded team augmentation is a monthly retainer. Pricing is the same regardless of industry - we charge by engagement shape, not by domain.

Yes, and we reach for them in that order. Structured outputs first - most prompts that look like agents are actually one structured output away from being a function. Function calling for the cases that genuinely need tool use. MCP when the same tool surface needs to be reachable by multiple clients without rebuilding the integration each time. We treat MCP as a deployment pattern, not a religion.

Yes, mutual NDA before any technical conversation. We do not work for clients with conflicting active engagements in the same competitive set during a quarter - a rule we enforce on ourselves more strictly than most clients ask us to.