GENERATIVE AI BUILDS THAT SHIP TO PRODUCTION

Quick Answer: NUUN AI builds custom assistants, RAG systems, agentic workflows, and LLM applications — with evaluation harnesses, hallucination monitoring, and production-grade observability from day one. Pilot-stage AI that never graduates fails the business; we build to ship.

WHAT WE BUILD

Custom assistants. Internal and customer-facing AI assistants with domain grounding.
Retrieval-Augmented Generation (RAG). Document-grounded Q&A over enterprise corpora.
Agentic workflows. Multi-step, tool-using agents for complex tasks.
LLM-powered applications. Customer service, content generation, analysis, and decision-support tools.
Evaluation harnesses. Offline and online evaluation, hallucination detection, and regression testing.
Model orchestration. Routing, fallback, and cost-aware model selection.

HOW WE DO IT

Problem framing. What does the user need; what does success look like; what's the guardrail.
Retrieval and grounding design. RAG architecture, chunking, embedding, and reranking strategy.
Prompt, context, and evaluation design. Prompts tested against eval sets; iteration data-driven.
Ship with monitoring. Observability, hallucination flags, and user feedback loops from launch.
Sustain and improve. Retraining, re-embedding, and prompt evolution as usage data accumulates.

PLATFORMS AND STACKS WE WORK WITH

Foundation models: Claude (Anthropic) · GPT (OpenAI) · Gemini (Google) · Llama · Mistral · Cohere · Specialized (medical, legal).

Orchestration: LangChain · LlamaIndex · LangGraph · Custom Python/TypeScript.

Vector stores: Pinecone · Weaviate · Qdrant · pgvector · Chroma.

Evaluation: Ragas · DeepEval · Promptfoo · Custom eval harnesses.

SELECTED WORK

Financial services client — RAG-based internal assistant → [X]% reduction in analyst search time; zero compliance flags. Read case →
Healthcare client — Clinical-support assistant → MLR-approved, production-deployed with hallucination monitoring. Read case →

SOURCES & FURTHER READING

AI & Digital Transformation practice
AI strategy and roadmap
Machine learning and predictive models
Anthropic engineering — https://www.anthropic.com/engineering
OpenAI Cookbook — https://cookbook.openai.com/

Frequently asked.

Should we build on Claude, GPT, Gemini, or open-source?

Depends on the use case. Claude strong at long-context reasoning and writing; GPT strong at tool use; Gemini strong at multimodal; open-source strong for cost-sensitive, sovereignty-sensitive, or specialized fine-tuning scenarios. We benchmark per project.

How do you handle hallucinations?

Multi-layered: RAG grounding, structured-output constraints, self-check prompts, eval harness with hallucination tests, and online monitoring for flagged outputs. Hallucination is managed, not eliminated — and we're transparent about that.

Can you build with sensitive data (PHI, financial, legal)?

Yes. Sensitive-data builds use appropriate environment isolation (VPC, private endpoints, BAA-compliant deployments). PHI-handling follows HIPAA patterns; financial services follows SOC 2 patterns.

What about AI evaluation?

Non-negotiable. Every build ships with an eval harness — unit-test-style for prompts, regression tests for behaviour, and online monitoring. Evals are first-class build artifacts, not an afterthought.

How do you handle prompt and context changes post-launch?

Prompts are versioned, tested against eval suites, and gated by regression tests. Prompt changes are code changes and follow code-change discipline.