First AI Agent or a Stalled Prototype — We Build Domain-Specific AI That Actually Ships.
Most AI agent projects stall between prototype and production — retrieval quality, observability, and infrastructure reliability are the gaps. KubeNiche builds and operates domain-specific RAG AI Agents and multi-agent workflows with full retrieval pipelines, managed Kubernetes infrastructure, and production observability. Whether you're starting from a blank slate or rescuing a six-month-old prototype, we get to production fast.
Discuss Your AI Agent ProjectGeneric LLM Wrappers Hallucinate. Prototypes Don't Scale. We Solve Both.
SMBs starting their AI journey face a common trap: a ChatGPT wrapper that works in demos but hallucinates on real domain data, has no observability, and can't be trusted in production. Teams with existing AI projects face a different version — a prototype that's been 'almost ready' for six months because retrieval quality is inconsistent, the infrastructure isn't production-grade, and nobody owns the eval pipeline. Both problems have the same root cause: AI agents built without production discipline. KubeNiche brings ML engineering rigour to both scenarios — domain-specific RAG design, production K8s infrastructure, evaluation pipelines, and ongoing managed operations. We've delivered this in Insurance AI Ops modernisation and we apply the same discipline to every engagement.
What's Included
Core Capabilities
Greenfield AI Agent Architecture — Start With Production Discipline
For SMBs building their first AI agent, we design the full stack from scratch — agent framework selection (LangGraph, CrewAI, AutoGen, or custom), LLM provider strategy, RAG pipeline architecture, vector store selection, MCP tool server design, and K8s infrastructure. You get a production-ready architecture blueprint before writing the first line of agent code. No expensive retrofits when you try to scale.
Domain-Specific RAG Design & Retrieval Optimisation
Generic RAG pipelines built without domain expertise produce inconsistent retrieval and high hallucination rates. We design retrieval pipelines grounded in your proprietary data — policy documents, claims data, product catalogs, runbooks, contracts, or knowledge bases — with chunking strategies, embedding model selection, hybrid search (dense + sparse retrieval with BM25), metadata filtering, and re-ranking tuned for your domain. For teams with existing RAG pipelines, we audit retrieval quality and implement targeted improvements.
Multi-Agent Workflow Design — LangGraph, CrewAI & AutoGen
Design and implement multi-agent workflows for complex, multi-step tasks — research agents, document processing pipelines, code generation workflows, and decision-support systems. LangGraph for stateful agent graphs with human-in-the-loop checkpoints, CrewAI for role-based agent crews, AutoGen for conversational multi-agent patterns. MCP tool servers expose your internal APIs and data sources as callable tools for agent workflows.
Insurance AI Ops Modernisation — Proven Domain Delivery
Proven delivery in Insurance: we've helped carriers modernise in-house AI Ops with RAG-based agents that handle claims triage, policy Q&A, and underwriting support — replacing brittle rule engines with LLM-powered workflows that are auditable, observable, and production-grade. The same discipline applies to any domain with complex, document-heavy workflows: legal, healthcare, financial services, and professional services.
Agent Observability, Evals & Answer Quality Monitoring
Every agent we build or operate includes distributed tracing (OpenTelemetry + LangSmith or Arize Phoenix), LLM call logging via LiteLLM, evaluation pipelines (Ragas, DeepEval, or custom domain evals), and answer quality drift detection. You know when retrieval degrades or model behaviour shifts before your users do. For greenfield deployments, we establish eval baselines before go-live — so you have a quality benchmark from day one, not after the first user complaint.
Production Handoff & Ongoing Managed Operations
We don't build and leave. KubeNiche provides ongoing agent operations as a managed service — index refresh automation, embedding model updates, retrieval quality monitoring, token cost attribution, and incident response. Sub-15-minute SLA for production agent incidents. For SMBs without ML engineering depth, this means your AI agent stays production-grade as your data and requirements evolve.
Starting from scratch or rescuing a stalled prototype — we ship domain AI that works
Tell us about your data, your domain, and your use case. Whether you're starting fresh or unsticking a six-month prototype, we'll scope a RAG agent engagement that gets to production fast — with the observability and infrastructure to stay there.
Start the Conversation