AI agent development that actually ships. Novura Studios builds production-grade LLM pipelines, RAG systems, and agentic workflows for teams who've outgrown demos. Every engagement starts with the eval harness — because quality is the #1 reason agents stall before launch. LangGraph + Langfuse + pgvector + risk-based guardrails, integrated into your product surface, observable from day one.
The gap between demo and production isn't the model — it's everything around it.
A 2026 industry survey by LangChain found that 57% of organizations have AI agents in production, and quality is the number-one barrier for those still trying to get there. Demos pass; production fails. The gap between the two isn't the model — it's everything around it: the eval harness that catches regressions, the observability that makes drift detectable, the guardrails that block prompt injection, the cost controls that keep a per-task budget honest.
That “everything around it” is the work. Most consultancies still selling “LLM strategy” or “ChatGPT for your business” in 2026 are pitching 2024-era plays. We build what actually ships in 2026: agentic systems with traceable trajectories, eval-gated releases, risk-based guardrails, and provider-abstracted model layers so you can swap Anthropic for OpenAI when the math changes.
RAG, agents, evals, and guardrails — built together, gated together, shipped together.
Hybrid retrieval (BM25 + dense embeddings + reranker), contextual chunking, and document-graph-aware retrieval where multi-hop questions matter. pgvector by default; Pinecone, Weaviate, or Qdrant when scale, latency, or multi-tenant isolation demands it. Embeddings via OpenAI, Voyage, or Cohere — selected by eval, not brand.
Production agents built on LangGraph with explicit state, ReAct and Plan-and-Execute patterns where each fits, and Model Context Protocol (MCP) for tool integration with your existing services. Human-in-the-loop checkpoints where stakes are high. Every decision logged as a trajectory, not a black box.
Golden datasets curated against your real production questions. Trajectory evals that test agent decisions, not just final outputs. Regression gates in CI so a model swap or prompt change can't silently degrade quality. Langfuse, LangSmith, Galileo, Maxim AI, or Arize Phoenix — chosen for your stack, instrumented from day one.
Layered defenses aligned to OWASP LLM Top 10 and NIST AI RMF: input sanitization, system-prompt isolation, policy-as-code for what the agent can and cannot do, output schema and factuality validation, and risk-based routing so heavy checks run only on high-stakes turns. Prompt injection isn't a checkbox — it's an architecture.
Defaults exist so we ship fast; everything is replaceable when your constraints demand it.
From golden dataset to production cutover — the bar is set in week one.
Before any agent code. We work with your team to source real production queries, build a golden dataset, define the eval rubric, and stand up Langfuse / LangSmith. The release-gate bar is set here.
Smallest agent that solves the core path. Every turn traced, every retrieval logged, every model call costed. We tune retrieval and prompting against the eval harness, not intuition.
Risk-based guardrails layered in. Cost-per-task dialed against your unit economics. Cutover to production with rollback runbooks and drift monitors live before the first user sees it.
Production AI degrades silently — model providers update, documents change, user queries evolve. We monitor drift, surface regressions before they ship, and run safe model swaps as the provider landscape moves.
No black boxes, no vendor lock-in. The deliverables you keep when we leave.
What teams ask before scoping an AI engagement.
Pair this work with the services that surround it.
Tell us what you're building. Same business day reply with a scoped next step — not a generic sales pitch.
