AI agents
Task-running agents with explicit state, tool contracts, and escalation paths, not open loops.
Hiring a dedicated AI engineer through Sentient Arc costs $15–35 per hour, depending on system complexity and seniority. Agents, RAG systems, and LLM integration, embedded in your team, starting with a free consultation.
Anyone can wire a model to a prompt and impress a meeting. Getting the same feature to survive real users takes retrieval that stays grounded, tool calls that fail safely, evals that catch drift, and costs that stay sane at volume.
That skill set is scarce and expensive to hire full-time. An embedded AI engineer gives you production-grade judgment now, at an hourly rate, without a six-month search.
Every AI feature gets a definition of correct before it gets a bigger prompt: an eval set, confidence thresholds, and human-in-the-loop gates where the stakes are high. We measure, then iterate.
The engineer is model-agnostic, typically working across Claude, OpenAI, and Gemini behind a typed tool layer, so your product depends on contracts you own rather than one vendor API shape.
Task-running agents with explicit state, tool contracts, and escalation paths, not open loops.
Retrieval pipelines that answer from your documents with citations: chunking, indexing, reranking.
Model calls wired into product features with structured outputs, fallbacks, and cost controls.
Eval suites, tracing, and quality dashboards so behavior changes are caught before users see them.
You can hire a dedicated AI engineer from Sentient Arc for $15–35 per hour, depending on project complexity and seniority. A free consultation scopes the system before you commit.
Yes, those are the two most common engagements: RAG systems that answer from your documents with citations, and agents that retrieve context, call tools, and verify their own work.
They are model-agnostic: typically Claude, OpenAI, or Gemini behind a typed tool layer, chosen per task for quality, latency, and cost rather than by default.
Prompting is a fraction of the job. An AI engineer builds the system around the model: retrieval, tool contracts, evals, guardrails, and observability, which is what makes the feature survive production.